Internet DRAFT - draft-ietf-dtn-dtnma
draft-ietf-dtn-dtnma
Delay-Tolerant Networking E.J. Birrane
Internet-Draft S.E. Heiner
Intended status: Informational E. Annis
Expires: 31 August 2024 Johns Hopkins Applied Physics Laboratory
28 February 2024
DTN Management Architecture
draft-ietf-dtn-dtnma-12
Abstract
The Delay-Tolerant Networking (DTN) architecture describes a type of
challenged network in which communications may be significantly
affected by long signal propagation delays, frequent link
disruptions, or both. The unique characteristics of this environment
require a unique approach to network management that supports
asynchronous transport, autonomous local control, and a small
footprint (in both resources and dependencies) so as to deploy on
constrained devices.
This document describes a DTN management architecture (DTNMA)
suitable for managing devices in any challenged environment but, in
particular, those communicating using the DTN Bundle Protocol (BP).
Operating over BP requires an architecture that neither presumes
synchronized transport behavior nor relies on query-response
mechanisms. Implementations compliant with this DTNMA should expect
to successfully operate in extremely challenging conditions, such as
over uni-directional links and other places where BP is the preferred
transport.
Status of This Memo
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provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
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material or to cite them other than as "work in progress."
This Internet-Draft will expire on 31 August 2024.
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Copyright Notice
Copyright (c) 2024 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Scope . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2. Organization . . . . . . . . . . . . . . . . . . . . . . 5
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 6
3. Challenged Network Overview . . . . . . . . . . . . . . . . . 8
3.1. Challenged Network Constraints . . . . . . . . . . . . . 8
3.2. Topology and Service Implications . . . . . . . . . . . . 9
3.2.1. Management Implications . . . . . . . . . . . . . . . 10
3.3. Management Special Cases . . . . . . . . . . . . . . . . 11
4. Desirable Design Properties . . . . . . . . . . . . . . . . . 11
4.1. Dynamic Architectures . . . . . . . . . . . . . . . . . . 12
4.2. Hierarchically Modeled Information . . . . . . . . . . . 12
4.3. Adaptive Push of Information . . . . . . . . . . . . . . 13
4.4. Efficient Data Encoding . . . . . . . . . . . . . . . . . 14
4.5. Universal, Unique Data Identification . . . . . . . . . . 15
4.6. Runtime Data Definitions . . . . . . . . . . . . . . . . 16
4.7. Autonomous Operation . . . . . . . . . . . . . . . . . . 16
5. Current Remote Management Approaches . . . . . . . . . . . . 17
5.1. SNMP and SMI Models . . . . . . . . . . . . . . . . . . . 18
5.1.1. The SMI Modeling Language . . . . . . . . . . . . . . 19
5.1.2. SNMP Protocol and Transport . . . . . . . . . . . . . 19
5.2. XML-Infoset-Based Protocols and YANG Models . . . . . . . 19
5.2.1. The YANG Modeling Language . . . . . . . . . . . . . 20
5.2.2. NETCONF Protocol and Transport . . . . . . . . . . . 22
5.2.3. RESTCONF Protocol and Transport . . . . . . . . . . . 22
5.2.4. CORECONF Protocol and Transport . . . . . . . . . . . 23
5.3. gRPC Network Management Interface (gNMI) . . . . . . . . 23
5.3.1. The Protobuf Modeling Language . . . . . . . . . . . 23
5.3.2. gRPC Protocol and Transport . . . . . . . . . . . . . 24
5.4. Intelligent Platform Management Interface (IPMI) . . . . 24
5.5. Autonomic Networking . . . . . . . . . . . . . . . . . . 24
5.6. Deep Space Autonomy . . . . . . . . . . . . . . . . . . . 25
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6. Motivation for New Features . . . . . . . . . . . . . . . . . 25
7. Reference Model . . . . . . . . . . . . . . . . . . . . . . . 26
7.1. Important Concepts . . . . . . . . . . . . . . . . . . . 26
7.2. Model Overview . . . . . . . . . . . . . . . . . . . . . 27
7.3. Functional Elements . . . . . . . . . . . . . . . . . . . 28
7.3.1. Managed Applications and Services . . . . . . . . . . 28
7.3.2. DTNMA Agent (DA) . . . . . . . . . . . . . . . . . . 29
7.3.3. Managing Applications and Services . . . . . . . . . 31
7.3.4. DTNMA Manager (DM) . . . . . . . . . . . . . . . . . 32
7.3.5. Pre-Shared Definitions . . . . . . . . . . . . . . . 34
8. Desired Services . . . . . . . . . . . . . . . . . . . . . . 34
8.1. Local Monitoring and Control . . . . . . . . . . . . . . 35
8.2. Local Data Fusion . . . . . . . . . . . . . . . . . . . . 35
8.3. Remote Configuration . . . . . . . . . . . . . . . . . . 36
8.4. Remote Reporting . . . . . . . . . . . . . . . . . . . . 37
8.5. Authorization . . . . . . . . . . . . . . . . . . . . . . 37
9. Logical Autonomy Model . . . . . . . . . . . . . . . . . . . 38
9.1. Overview . . . . . . . . . . . . . . . . . . . . . . . . 38
9.2. Model Characteristics . . . . . . . . . . . . . . . . . . 40
9.3. Data Value Representation . . . . . . . . . . . . . . . . 42
9.4. Data Reporting . . . . . . . . . . . . . . . . . . . . . 42
9.5. Command Execution . . . . . . . . . . . . . . . . . . . . 43
9.6. Predicate Autonomy Rules . . . . . . . . . . . . . . . . 44
10. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . 45
10.1. Notation . . . . . . . . . . . . . . . . . . . . . . . . 45
10.2. Serialized Management . . . . . . . . . . . . . . . . . 46
10.3. Intermittent Connectivity . . . . . . . . . . . . . . . 47
10.4. Open-Loop Reporting . . . . . . . . . . . . . . . . . . 48
10.5. Multiple Administrative Domains . . . . . . . . . . . . 50
10.6. Cascading Management . . . . . . . . . . . . . . . . . . 52
11. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 54
12. Security Considerations . . . . . . . . . . . . . . . . . . . 54
13. Informative References . . . . . . . . . . . . . . . . . . . 54
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 60
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 60
1. Introduction
The Delay-Tolerant Networking (DTN) architecture, as described in
[RFC4838], has been designed to cope with data exchange in challenged
networks. Just as the DTN architecture requires new capabilities for
transport and transport security, special consideration is needed for
the management of DTN devices.
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This document describes a logical DTN Management Architecture (DTNMA)
providing configuration, monitoring, and local control of both
application and network services on a managed device. The DTNMA is
designed to provide for the management of devices operating either
within or across a challenged network.
| NOTE: A logical architecture describes the concepts and
| principles that support the logical operation of a system.
| This includes identifying components of the system (such as in
| a reference model), the behaviors they enable, and use cases
| describing how those behaviors result in the desired operation
| of the system. Logical architectures are not functional
| architectures.
|
| As such, this document does not specify a particular functional
| design for achieving desired behaviors. What is presented here
| is a set of architectural principles. It is not implied to be
| complete or to define interfaces between components.
Fundamental properties of a challenged network are outlined in
Section 2.2.1 of [RFC7228]. These properties include lacking end-to-
end IP connectivity, having "serious interruptions" to end-to-end
connectivity, and exhibiting delays longer than can be tolerated by
end-to-end synchronization mechanisms (such as TCP). It is further
noted that the DTN architecture was designed to cope with such
networks.
| NOTE: These challenges may be caused by a variety of factors
| such as physical constraints (e.g., long signal propagation
| delays and frequent link disruptions), administrative policies
| (e.g., quality-of-service prioritization, service-level
| agreements, and traffic management and scheduling), and off-
| nominal behaviors (e.g., active attackers and
| misconfigurations).
Device management in these environments occurs without human
interactivity, without system-in-the-loop synchronous function, and
without requiring a synchronous underlying transport layer. This
means that managed devices need to determine their own schedules for
data reporting, their own operational configuration, and perform
their own error discovery and mitigation.
Certain outcomes of device self-management should be determinable by
a privileged external observer (such as a managing device). In a
challenged network, these observers may need to communicate with a
managed device after significant periods of disconnectedness. Non-
deterministic behavior of a managed device may make establishing
communication difficult or impossible.
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The desire to define asynchronous and autonomous device management is
not new. However, challenged networks (in general) and the DTN
environment (in particular) represent unique deployment scenarios and
impose unique design constraints. To the extent that these
environments differ from more traditional, enterprise networks, their
management may also differ from the management of enterprise
networks. Therefore, existing techniques may need to be adapted to
operate in the DTN environment or new techniques may need to be
created.
| NOTE: The DTNMA is designed to leverage any transport, network,
| and security solutions designed for challenged networks.
| However, the DTNMA specifically needs to be able to operate in
| any environment in which the Bundle Protocol (BPv7) [RFC9171]
| is deployed.
1.1. Scope
This document describes the desirable properties of, and motivation
for, a DTNMA. This document also provides a reference model, service
descriptions, autonomy model, and use cases to better reason about
ways to standardize and implement this architecture.
This document provides informative guidance to authors and users of
such models, protocols, and implementations.
The selection of any particular transport or network layer is outside
of the scope of this document. The DTNMA does not require the use of
any specific protocol such as IP, BP, TCP, or UDP. In particular,
the DTNMA design does not assume the use of either IPv4 or IPv6.
| NOTE: The fact that the DTNMA can operate in any environment
| that deploys BP does not mean that the DTNMA requires the use
| of BP to operate.
Network features such as naming, addressing, routing, and
communications security are out of scope of the DTNMA. It is
presumed that any operational network communicating DTNMA messages
would implement these services for any payloads carried by that
network.
The interactions between and amongst the DTNMA and other management
approaches are outside of the scope of this document.
1.2. Organization
The remainder of this document is organized into the following nine
sections, described as follows.
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Terminology: This section identifies terms fundamental to
understanding DTNMA concepts. Whenever possible, these terms
align in both word selection and meaning with their use in other
management protocols.
Challenged Network Overview: This section describes important
aspects of challenged networks and necessary approaches for their
management.
Desirable Design Properties: This section defines those properties
of the DTNMA needed to operate within the constraints of a
challenged network. These properties are similar to the
specification of system-level requirements of a DTN management
solution.
Current Remote Management Approaches: This section provides a brief
overview of existing remote management approaches. Where
possible, the DTNMA adopts concepts from these approaches. The
limitations of current approaches from the perspective of the
DTNMA desirable properties are identified and discussed.
Motivation for New Features: This section provides an overall
motivation for this work, to include explaining why a management
architecture for challenged networks is useful and necessary.
Reference Model: This section defines a reference model that can be
used to reason about the DTNMA independent of an implementation or
implementation architecture. This model identifies the logical
components of the system and the high-level relationships and
behaviors amongst those components.
Desired Services: This section identifies and defines the DTNMA
services provided to network and mission operators.
Logical Autonomy Model: This section provides an exemplar data model
that can be used to reason about DTNMA control and data flows.
This model is based on the DTNMA reference model.
Use Cases: This section presents multiple use cases accommodated by
the DTNMA architecture. Each use case is presented as a set of
control and data flows referencing the DTNMA reference model and
logical autonomy model.
2. Terminology
This section defines terminology that either is unique to the DTNMA
or is necessary for understanding the concepts defined in this
specification.
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Timely Data Exchange: The ability to communicate information between
two (or more) entities within a required period of time. In some
cases, a 1-second exchange may not be timely and in other cases
1-hour exchange may be timely.
DTN Management: Management that does not depend on stateful
connections, timely data exchange of management messages, or
system-in-the-loop synchronous functions.
DTNMA Agent (DA): A role associated with a managed device,
responsible for reporting performance data, accepting policy
directives, performing autonomous local control, error-handling,
and data validation. DAs exchange information with DMs operating
either on the same device and/or on remote devices in the network.
DTNMA Manager (DM): A role associated with a managing device
responsible for configuring the behavior of, and eventually
receiving information from, DAs. DMs interact with one or more
DAs located on the same device and/or on remote devices in the
network.
Controls: Procedures run by a DA to change the behavior,
configuration, or state of an application or protocol managed by
that DA. This includes procedures to manage the DA itself, such
as to have the DA produce performance reports or to apply new
management policies.
Externally Defined Data (EDD): Typed information made available to a
DA by its hosting device, but not computed directly by the DA
itself.
Data Reports: Typed, ordered collections of data values gathered by
one or more DAs and provided to one or more DMs. Reports comply
to the format of a given Data Report Schema.
Data Report Schemas: Named, ordered collection of data elements that
represent the schema of a Data Report.
Rules: Unit of autonomous specification that provides a stimulus-
response relationship between time or state on a DA and the
actions or operations to be run as a result of that time or state.
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3. Challenged Network Overview
The DTNMA provides network management services able to operate in a
challenged network environment, such as envisioned by the DTN
architecture. This section describes what is meant by the term
"challenged network", the important properties of such a network, and
observations on impacts to conventional management approaches.
3.1. Challenged Network Constraints
Constrained networks are defined as networks where "some of the
characteristics pretty much taken for granted with link layers in
common use in the Internet at the time of writing are not
attainable." [RFC7228]. This broad definition captures a variety of
potential issues relating to physical, technical, and regulatory
constraints on message transmission. Constrained networks typically
include nodes that regularly reboot or are otherwise turned off for
long periods of time, transmit at low or asynchronous bitrates, and/
or have very limited computational resources.
Separately, a challenged network is defined as one that "has serious
trouble maintaining what an application would today expect of the
end-to-end IP model" [RFC7228]. Links in such networks may be
impacted by attenuation, propagation delays, mobility, occultation,
and other limitations imposed by energy and mass considerations.
Therefore, systems relying on such links cannot guarantee timely end-
to-end data exchange.
| NOTE: Because challenged networks might not provide services
| expected of the end-to-end IP model, devices in such networks
| might not implement networking stacks associated with the end-
| to-end IP model. This means that devices might not include
| support for certain transport protocols (TCP/UDP), web
| protocols (HTTP), or internetworking protocols (IPv4/IPv6).
By these definitions, a "challenged" network is a special type of
"constrained" network, where constraints prevent timely end-to-end
data exchange. As such, "all challenged networks are constrained
networks ... but not all constrained networks are challenged networks
... Delay-Tolerant Networking (DTN) has been designed to cope with
challenged networks" [RFC7228].
Solutions that work in constrained networks might not be solutions
that work in challenged networks. In particular, challenged networks
exhibit the following properties that impact the way in which the
function of network management is considered.
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* Timely end-to-end data exchange cannot be guaranteed to exist at
any given time between any two nodes.
* Latencies on the order of seconds, hours, or days must be
tolerated.
* Individual links may be uni-directional.
* Bi-directional links may have asymmetric data rates.
* The existence of external infrastructure, services, systems, or
processes such as a Domain Name Service (DNS) or a Certificate
Authority (CA) cannot be guaranteed.
3.2. Topology and Service Implications
The set of constraints that might be present in a challenged network
impact both the topology of the network and the services active
within that network.
Operational networks handle cases where nodes join and leave the
network over time. These topology changes may or may not be planned,
they may or may not represent errors, and they may or may not impact
network services. Challenged networks differ from other networks not
in the present of topological change, but in the likelihood that
impacts to topology result in impacts to network services.
The difference between topology impacts and service impacts can be
expressed in terms of connectivity. Topological connectivity usually
refers to the existence of a path between an application message
source and destination. Service connectivity, alternatively, refers
to the existence of a path between a node and one or more services
needed to process (often just-in-time) application messaging.
Examples of service connectivity include access to infrastructure
services such as a Domain Name System (DNS) or a Certificate
Authority (CA).
In networks that might be partitioned most of the time, it is less
likely that a node would concurrently access both an application
endpoint and one or more network service endpoints. For this reason,
network services in a challenged network should be designed to allow
for asynchronous operation. Accommodating this use case often
involves the use of local caching, pre-placing information, and not
hard-coding message information at a source that might change when a
message reaches its destination.
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| NOTE: One example of rethinking services in a challenged
| network is the securing of BPv7 bundles. The BPSec [RFC9172]
| security extensions to BPv7 do not encode security destinations
| when applying security. Instead, BPSec requires nodes in a
| network to identify themselves as security verifiers or
| acceptors when receiving and processing secured messages.
3.2.1. Management Implications
Network management approaches need to adapt to the topology and
service impacts encountered in challenged networks. In particular,
the ways in which "managers" and "agents" operate will need to adapt
to changes in connectivity and service endpoints.
When connectivity to a manager cannot be guaranteed, agents will need
to rely on locally available information and use local autonomy to
react to changes at the node. Architectures that rely on external
resources such as access to third-party oracles, operators-in-the-
loop, or other service infrastructure may fail to operate in a
challenged network.
In addition to disconnectivity, topological change can alter the
associations amongst managed and managing devices. Different
managing devices might be active in a network at different times or
in different partitions. Managed devices might communicate with
some, all, or none of these managing devices as a function of their
own local configuration and policy.
| NOTE: These concepts relate to practices in conventional
| networks. For example, supporting multiple managing devices is
| similar to deploying multiple instances of a network service --
| such as a DNS server or CA node. Selecting from a set of
| managing devices is similar to a sensor node practice of
| electing cluster heads to act as privileged nodes for data
| storage and exfiltration.
Therefore, a network management architecture for challenged networks
should:
1. Support a many-to-many association amongst managing and managed
devices, and
2. Allow "control from" and "reporting to" managing devices to
function independent of one another.
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3.3. Management Special Cases
The following special cases illustrate some of the operational
situations that can be encountered in the management of devices in a
challenged network.
One-Way Management: A managed device can only be accessed via a uni-
directional link, or a via a link whose duration is shorter than a
single round-trip propagation time.
Summary Data: A managing device can only receive summary data of a
managed device's state because a link or path is constrained by
capacity or reliability.
Bulk Historical Reporting: A managing device receives a large volume
of historical report data for a managed device. This can occur
when a managed device rejoins a network or has access to a high
capacity link (or path) to the managed device.
Multiple Managers A managed device tracks multiple managers in the
network and communicates with them as a function of time, local
state, or network topology. This includes challenged networks
that interconnect two or more unchallenged networks such that
managed and managing devices exist in different networks.
These special cases highlight the need for managed devices to operate
without presupposing a dedicated connection to a single managing
device. To support this, managing devices deliver instruction sets
that govern the local, autonomous behavior of managed devices. These
behaviors include (but are not limited to) collecting performance
data, state, and error conditions, and applying pre-determined
responses to pre-determined events. Managing devices in a challenged
network might never expect a reply to a command, and communications
from managed devices may be delivered much later than the events
being reported.
4. Desirable Design Properties
This section describes those design properties that are desirable
when defining a management architecture operating across challenged
links in a network. These properties ensure that network management
capabilities are retained even as delays and disruptions in the
network scale. Ultimately, these properties are the driving design
principles for the DTNMA.
| NOTE: These properties may influence the design, construction,
| and adaptation of existing management tools for use in
| challenged networks. For example, the properties the DTN
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| architecture [RFC4838] resulted in the development of BPv7
| [RFC9171] and BPSec [RFC9172]. The DTNMA may result in the
| construction of new management data models, policy expressions,
| and/or protocols.
4.1. Dynamic Architectures
The DTNMA should be agnostic of the underlying physical topology,
transport protocols, security solutions, and supporting
infrastructure of a given network. Due to the likelihood of
operating in a frequently partitioned environment, the topology of a
network may change over time. Attempts to stabilize an architecture
around individual nodes can result in a brittle management framework
and the creation of congestion points during periods of connectivity.
The DTNMA should not prescribe any association between a DM and a DA
other than those defined in this document. There should be no
logical limitation to the number of DMs that can control a DA, the
number of DMs that a DA should report to, or any requirement that a
DM and DA relationship implies a pair.
| NOTE: Practical limitations on the relationships between and
| amongst DMs and DAs will exist as a function of the
| capabilities of networked devices. These limitations derive
| from processing and storage constraints, performance
| requirements, and other engineering factors. While this
| information is vital to the proper engineering of a managed and
| managing device, they are implementation considerations, and
| not otherwise design constraints on the DTNMA.
4.2. Hierarchically Modeled Information
The DTNMA should use data models to define the syntactic and semantic
contracts for data exchange between a DA and a DM. A given model
should have the ability to "inherit" the contents of other models to
form hierarchical data relationships.
| NOTE: The term data model in this context refers to a schema
| that defines a contract between a DA and a DM for how
| information is represented and validated.
Many network management solutions use data models to specify the
semantic and syntactic representation of data exchanged between
managed and managing devices. The DTNMA is not different in this
regard - information exchanged between DAs and DMs should conform to
one or more pre-defined, normative data models.
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A common best practice when defining a data model is to make it
cohesive. A cohesive model is one that includes information related
to a single purpose such as managing a single application or
protocol. When applying this practice, it is not uncommon to develop
a large number of small data models that, together, describe the
information needed to manage a device.
Another best practice for data model development is the use of
inclusion mechanisms to allow one data model to include information
from another data model. This ability to include a data model avoids
repeating information in different data models. When one data model
includes information from another data model, there is an implied
model hierarchy.
Data models in the DTNMA should allow for the construction of both
cohesive models and hierarchically related models. These data models
should be used to define all sources of information that can be
retrieved, configured, or executed in the DTNMA. This includes
supporting DA autonomy functions such as parameterization, filtering,
and event driven behaviors. These models will be used to both
implement interoperable autonomy engines on DAs and define
interoperable report parsing mechanisms on DMs.
| NOTE: While data model hierarchies can result in a more concise
| data model, arbitrarily complex nesting schemes can also result
| in very verbose encodings. Where possible, data
| identifications schemes should be constructed that allow for
| both hierarchical data and highly compressible data
| identification.
4.3. Adaptive Push of Information
DAs in the DTNMA architecture should determine when to push
information to DMs as a function of their local state.
Pull management mechanisms require a managing device to send a query
to a managed device and then wait for a response to that specific
query. This practice implies some serialization mechanism (such as a
control session) between entities. However, challenged networks
cannot guarantee timely round-trip data exchange. For this reason,
pull mechanisms should be avoided in the DTNMA.
Push mechanisms, in this context, refer to the ability of DAs to
leverage local autonomy to determine when and what information should
be sent to which DMs. The push is considered adaptive because a DA
determines what information to push (and when) as an adaptation to
changes to the DA's internal state. Once pushed, information might
still be queued pending connectivity of the DA to the network.
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| NOTE: Even in cases where a round-trip exchange can occur, pull
| mechanisms increase the overall amount of traffic in the
| network and preclude the use of autonomy at managed devices.
| So even when pull mechanisms are feasible they should not be
| considered a pragmatic alternative to push mechanisms.
4.4. Efficient Data Encoding
Messages exchanged between a DA and a DM in the DTNMA should be
defined in a way that allows for efficient on-the-wire encoding.
DTNMA design decisions that result in smaller message sizes should be
preferred over those that result in larger message sizes.
There is a relationship between message encoding and message
processing time at a node. Messages with little or no encodings may
simplify node processing whereas more compact encodings may require
additional activities to generate/parse encoded messages. Generally,
compressing a message takes processing time at the sender and
decompressing a message takes processing time at a receiver.
Therefore, there is a design tradeoff between minimizing message
sizes and minimizing node processing.
| NOTE: There are many ways in which message size, number of
| messages, and node behaviors can impact processing performance.
| Because the DTNMA does not presuppose any underlying protocol
| or implementation, this section is focused solely on the
| compactness of an individual message and the processing for
| encoding and decoding that individual message.
However, there is a significant advantage to smaller message sizes in
a challenged network. Smaller messages require smaller periods of
viable transmission for communication, they incur less re-
transmission cost, and they consume less resources when persistently
stored en-route in the network.
| NOTE: Naive approaches to minimizing message size through
| general purpose compression algorithms do not produce minimal
| encodings. Data models can, and should, be designed for
| compact encoding from the beginning. Design strategies for
| compact encodings involve using structured data, hierarchical
| data models, and common structures in data models. These
| strategies allow for compressibility beyond what would
| otherwise be achieved by computing large hash values over
| generalized data structures.
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4.5. Universal, Unique Data Identification
Data elements within the DTNMA should be uniquely identifiable so
that they can be individually manipulated. Further, these
identifiers should be universal - the identifier for a data element
should be the same regardless of role, implementation, or network
instance.
Identification schemes that are relative to a specific DA or specific
system configuration might change over time. In particular, nodes in
a challenged network may change their status or configuration during
periods of partition from other parts of the network.
Resynchronizing relative state or configuration should be avoided
whenever possible.
| NOTE: Consider the common technique for approximating an
| associative array lookup. For example, if a managed device
| tracks the number of bytes passed by multiple named interfaces,
| then the number of bytes through a specific named interface
| (say, "int_foo"), would be retrieved in the following way:
|
| 1. Query a list of ordered interface names from an agent.
|
| 2. Find the name that matches "int_foo" and infer the
| agent's index of "int_foo" from the ordered interface
| list. In this instance, say "int_foo" is the 4th
| interface in the list.
|
| 3. Query the agent to return the number of bytes passed
| through the 4th interface.
|
| Ignoring the inefficiency of two round-trip exchanges, this
| mechanism will fail if the agent changes its index mapping
| between the first and second query. For example, were
| "int_foo" to be restarted and slotted in a different index
| position. While this is unlikely to occur in a low-latency
| network, it is more likely to occur in a challenged network.
|
| The desired data being queried, "number of bytes through
| int_foo" should be uniquely and universally identifiable and
| independent of how that data exists in an agent's custom
| implementation.
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4.6. Runtime Data Definitions
The DTNMA allows for the addition of new data elements to a data
model as part of the runtime operation of the management system.
These definitions may represent custom data definitions that are
applicable only for a particular device or network. Custom
definitions should also be able to be removed from the system during
runtime.
The goal of this approach is to dynamically add or remove data
elements to the local runtime schemas as needed - such as the
definition of new counters, new reports, or new rules.
The custom definition of new data from existing data (such as through
data fusion, averaging, sampling, or other mechanisms) provides the
ability to communicate desired information in as compact a form as
possible.
| NOTE: A DM could, for example, define a custom data report that
| includes only summary information around a specific operational
| event or as part of specific debugging. DAs could then produce
| this smaller report until it is no longer necessary, at which
| point the custom report could be removed from the management
| system.
Custom data elements should be calculated and used both as parameters
for DA autonomy and for more efficient reporting to DMs. Defining
new data elements allows for DAs to perform local data fusion and
defining new reporting templates allows for DMs to specify desired
formats and generally save on link capacity, storage, and processing
time.
4.7. Autonomous Operation
The management of applications by a DA should be achievable using
only knowledge local to the DA because DAs might need to operate
during times when they are disconnected from a DM.
DA autonomy may be used for simple automation of predefined tasks or
to support semi-autonomous behavior in determining when to run tasks
and how to configure or parameterize tasks when they are run.
Important features provided by the DA are listed below. These
features work together to accomplish tasks. As such, there is
commonality amongst their definitions and nature of their benefits.
Stand-alone Operation: Pre-configuration allows DAs to operate
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without regular contact with other nodes in the network. Updates
for configurations remain difficult in a challenged network, but
this approach removes the requirement that a DM be in-the-loop
during regular operations. Sending stimuli-and-responses to a DA
during periods of connectivity allows DAs to self-manage during
periods of disconnectivity.
Deterministic Behavior: Operational systems might need to act in a
deterministic way even in the absence of an operator in-the-loop.
Deterministic behavior allows an out-of-contact DM to predict the
state of a DA and to determine how a DA got into a particular
state.
Engine-Based Behavior: Operational systems might not be able to
deploy "mobile code" [RFC4949] solutions due to network bandwidth,
memory or processor loading, or security concerns. Engine-based
approaches provide configurable behavior without incurring these
concerns.
Authorization, and Accounting: The DTNMA does not require a specific
underlying transport protocol, network infrastructure, or network
services. Therefore, mechanisms for authorization and accounting
need to be present in a standard way at DAs and DMs to provide
these functions if the underlying network does not. This is
particularly true in cases where multiple DMs may be active
concurrently in the network.
To understand the contributions of these features to a common
behavior, consider the example of a managed device coming online with
a set of pre-installed configuration. In this case, the device's
stand-alone operation comes from the pre-configuration of its local
autonomy engine. This engine-based behavior allows the system to
behave in a deterministic way and any new configurations will need to
be authorized before being adopted.
Features such as deterministic processing and engine-based behavior
do not preclude the use of other Artificial Intelligence (AI) and
Machine Learning (ML) approaches on a managed device.
5. Current Remote Management Approaches
Several remote management solutions have been developed for both
local-area and wide-area networks. Their capabilities range from
simple configuration and report generation to complex modeling of
device settings, state, and behavior. Each of these approaches are
successful in the domains for which they have been built, but are not
all equally functional when deployed in a challenged network.
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Remote management tools designed for unchallenged networks provide
synchronous mechanisms for communicating locally-collected data from
devices to operators. Applications are typically managed using a
"pull" mechanism, requiring a managing device to explicitly request
the data to be produced and transmitted by a managed device.
| NOTE: Network management solutions that pull large sets of data
| might not operate in a challenged environment that cannot
| support timely round-trip exchange of large data volumes.
More recent network management tools focus on message-based
management, reduced state keeping by managed and managing devices,
and increased levels of system autonomy.
This section describes some of the well-known protocols for remote
management and contrasts their purposes with the desirable properties
of the DTNMA. The purpose of this comparison is to identify parts of
existing approaches that can be adopted or adapted for use in
challenged networks and where new capabilities should be created
specifically for this environment.
5.1. SNMP and SMI Models
An early and widely used example of a remote management protocol is
the Simple Network Management Protocol (SNMP) currently at Version 3
[RFC3410]. The SNMP utilizes a request/response model to get and set
data values within an arbitrarily deep object hierarchy. Objects are
used to identify data such as host identifiers, link utilization
metrics, error rates, and counters between application software on
managing and managed devices [RFC3411]. Additionally, SNMP supports
a model for unidirectional push messages, called event notifications,
based on agent-defined triggering events.
SNMP relies on logical sessions with predictable round-trip latency
to support its "pull" mechanism but a single activity is likely to
require many round-trip exchanges. Complex management can be
achieved, but only through careful orchestration of real-time, end-
to-end, managing-device-generated query-and-response logic.
There is existing work that uses the SNMP data model to support some
low-fidelity Agent-side processing, to include the Distributed
Management Expression MIB [RFC2982] and Definitions of Managed
Objects for the Delegation of Management Scripts [RFC3165]. However,
Agent autonomy is not an SNMP mechanism, so support for a local agent
response to an initiating event is limited. In a challenged network
where the delay between a managing device receiving an alert and
sending a response can be significant, SNMP is insufficient for
autonomous event handling.
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5.1.1. The SMI Modeling Language
SNMP separates the representations for managed data models from
Manager--Agent messaging, sequencing and encoding. Each data model
is termed a Management Information Base (MIB) [RFC3418] and uses the
Structure of Management Information (SMI) modeling language
[RFC2578]. Additionally, the SMI itself is based on the ASN.1 Syntax
[ASN.1] which is used not just for SMI but for other, unrelated data
structure specification such as the Cryptographic Message Syntax
(CMS) [RFC5652]. Separating data models from messaging and encoding
is a best practice in remote management protocols and is also
necessary for the DTNMA.
Each SNMP MIB is composed of managed object definitions each of which
is associated with a hierarchical Object Identifier (OID). Because
of the arbitrarily deep nature of MIB object trees, the size of OIDs
is not strictly bounded by the protocol (though may be bounded by
implementations).
5.1.2. SNMP Protocol and Transport
The SNMP protocol itself, which is at version 2 [RFC3416], can
operate over a variety of transports, including plaintext UDP/IP
[RFC3417], SSH/TCP/IP [RFC5592], and DTLS/UDP/IP or TLS/TCP/IP
[RFC5953].
SNMP uses an abstracted security model to provide authentication,
integrity, and confidentiality. There are options for user-based
security model (USM) of [RFC3414], which uses in-message security,
and transport security model (TSM) [RFC5591], which relies on the
transport to provide security functions and interfaces.
5.2. XML-Infoset-Based Protocols and YANG Models
Several network management protocols, including NETCONF [RFC6241],
RESTCONF [RFC8040], and CORECONF [I-D.ietf-core-comi], share the same
XML information set [xml-infoset] for its hierarchical managed
information and [XPath] expressions to identify nodes of that
information model. Since they share the same information model and
the same data manipulation operations, together they will be referred
to as "*CONF" protocols. Each protocol, however, provides a
different encoding of that information set and its related operation-
specific data.
The YANG modeling language of [RFC7950] is used to define the data
model for these management protocols. Currently, YANG represents the
IETF standard for defining managed information models.
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5.2.1. The YANG Modeling Language
The YANG modeling language defines a syntax and modular semantics for
organizing and accessing a device's configuration or operational
information. YANG allows subdividing a full managed configuration
into separate namespaces defined by separate YANG modules. Once a
module is developed, it is used (directly or indirectly) on both the
client and server to serve as a contract between the two. A YANG
module can be complex, describing a deeply nested and inter-related
set of data nodes, actions, and notifications.
Unlike the separation in Section 5.1.1 between ASN.1 syntax and
module semantics from higher-level SMI data model semantics, YANG
defines both a text syntax and module semantics together with data
model semantics.
The YANG language provides flexibility in the organization of model
objects to the model developer. The YANG supports a broad range of
data types noted in [RFC6991]. YANG supports the definition of
parameterized Remote Procedure Calls (RPCs) and actions to be
executed on managed devices as well as the definition of event
notifications within the model.
| Current *CONF notification logic allows a client to subscribe
| to the delivery of specific containers or data nodes defined in
| the model, either on a periodic or "on change" basis [RFC8641].
| These notification events can be filtered according to XPath
| [XPath] or subtree [RFC6241] filtering as described in
| Section 2.2 of [RFC8639].
The use of YANG for data modeling necessarily comes with some side-
effects, some of which are described here.
Text Naming: Data nodes, RPCs, and notifications within a YANG model
are named by a namespace-qualified, text-based path of the module,
sub-module, container, and any data nodes such as lists, leaf-
lists, or leaves, without any explicit hierarchical organization
based on data or object type.
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Existing efforts to make compressed names for YANG objects, such
as the YANG Schema Item iDentifiers (SID) from Section 3.2 of
[RFC9254], allow a node to be named by an globally unique integer
value but are still relatively verbose (up to 8 bytes per item)
and still must be translated into text form for things like
instance identification (see below). Additionally, when
representing a tree of named instances the child elements can use
differential encoding of SID integer values as "delta" integers.
The mechanisms for assigning SIDs and the lifecycle of those SIDs
are still in development [I-D.ietf-core-sid].
Text Values and Built-In Types: Because the original use of YANG
with NETCONF was to model XML information sets, the values and
built-in types are necessarily text based. The JSON encoding of
YANG data [RFC7951] allows for optimized representations of many
built-in types, and similarly the CBOR encoding [RFC9254] allows
for different optimized representations.
In particular, the YANG built-in types natively support a fixed
range of decimal fractions (Section 9.3 of [RFC7950]) but
purposefully do not support floating point numbers. There are
alternatives, such as the type bandwidth-ieee-float32 from
[RFC8294] or using the "binary" type with one of the IEEE-754
encodings.
Deep Hierarchy: YANG allows for, and current YANG modules take
advantage of, the ability to deeply nest a model hierarchy to
represent complex combinations and compositions of data nodes.
When a model uses a deep hierarchy of nodes this necessarily means
that the qualified paths to name those nodes and instances is
longer than a flat hierarchy would be.
Instance Identification: The node instances in a YANG module
necessarily use XPath expressions for identification. Some
identification is constrained to be strictly within the YANG
domain, such as "must" "when", "augment", or "deviation"
statements. Other identification needs to be processed by a
managed device, such as in "instance-identifier" built-in type.
This means any implementation of a managed device must include
XPath processing and related information model handling of
Section 6.4 of [RFC7950] and its referenced documents.
Protocol Coupling: A significant amount of existing YANG tooling or
modeling presumes the use of YANG data within a management
protocol with specific operations available. For exmaple, the
access control model of [RFC8341] relies on those operations
specific to the *CONF protocols for proper behavior.
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The emergence of multiple NETCONF-derived protocols may make these
presumptions less problematic in the future. Work to more
consistently identify different types of YANG modules and their
use has been undertaken to disambiguate how YANG modules should be
treated [RFC8199].
Manager-Side Control: YANG RPCs and actions execute on a managed
device and generate an expected, structured response. RPC
execution is strictly limited to those issued by the manager.
Commands are executed immediately and sequentially as they are
received by the managed device, and there is no method to
autonomously execute RPCs triggered by specific events or
conditions.
The YANG modeling language continues to evolve as new features are
needed by adopting management protocols.
5.2.2. NETCONF Protocol and Transport
NETCONF is a stateful, XML-encoding-based protocol that provides a
syntax to retrieve, edit, copy, or delete any data nodes or exposed
functionality on a server. It requires that underlying transport
protocols support long-lived, reliable, low-latency, sequenced data
delivery sessions. A bi-directional NETCONF session needs to be
established before any data transfer (or notification) can occur.
The XML exchanged within NETCONF messages is structured according to
YANG modules supported by the NETCONF agent, and the data nodes
reside within one of possibly many datastores in accordance with the
Network Management Datastore Architecture (NMDA) of [RFC8342].
NETCONF transports are required to provide authentication, data
integrity, confidentiality, and replay protection. Currently,
NETCONF can operate over SSH/TCP/IP [RFC6242] or TLS/TCP/IP
[RFC7589].
5.2.3. RESTCONF Protocol and Transport
RESTCONF is a stateless, JSON-encoding-based protocol that provides
the same operations as NETCONF, using the same YANG modules for
structure and same NMDA datastores, but using RESTful exchanges over
HTTP. It uses HTTP-native methods to express its allowed operations:
GET, POST, PUT, PATCH, or DELETE data nodes within a datastore.
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Although RESTCONF is a logically stateless protocol, it does rely on
state within its transport protocol to achieve behaviors such as
authentication and security sessions. Because RESTCONF uses the same
data node semantics of NETCONF, a typical activity can involve the
use of several sequential round-trips of exchanges to first discover
managed device state and then act upon it.
5.2.4. CORECONF Protocol and Transport
CORECONF is an emerging stateless protocol built atop the Constrained
Application Protocol (CoAP) [RFC7252] that defines a messaging
construct developed to operate specifically on constrained devices
and networks by limiting message size and fragmentation. CoAP also
implements a request/response system and methods for GET, POST, PUT,
and DELETE.
5.3. gRPC Network Management Interface (gNMI)
Another emerging but not-IETF-affiliated management protocol is the
gRPC Network Management Interface (gNMI) [gNMI] which is based on
gRPC messaging and uses Protobuf data modeling.
The same limitations of RESTCONF listed above apply to gNMI because
of its reliance on synchronous HTTP exchanges and TLS security for
normal operations, as well as the likely deep nesting of data
schemas. There is a capability for gNMI to transport JSON-encoded
YANG-modeled data, but this composing is not fully standardized and
relies on specific tool integrations to operate.
5.3.1. The Protobuf Modeling Language
The data managed and exchanged via gNMI is encoded and modeled using
Google Protobuf, an encoding and modeling syntax not affiliated with
the IETF (although an attempt has been made and abandoned
[I-D.rfernando-protocol-buffers]).
Because the Protobuf modeling syntax is relatively low-level (around
the same as ASN.1 or CBOR), there are some efforts as part of the
OpenConfig work [gNMI] to translate YANG modules into Protobuf
schemas (similar to translation to XML or JSON schemas for NETCONF
and RESTCONF respectively) but there is no required interoperabilty
between management via gRPC or any of the *CONF protocols.
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5.3.2. gRPC Protocol and Transport
The message encoding and exchange for gNMI, as the name implies, is
gRPC protocol [gRPC]. gRPC exclusively uses HTTP/2 [RFC7540] for
transport and relies on some aspects specific to HTTP/2 for its
operations (such as HTTP trailer fields). While not mandated by
gRPC, when used to transport gNMI data TLS is required for transport
security.
5.4. Intelligent Platform Management Interface (IPMI)
A lower-level remote management protocol, intended to be used to
manage hardware devices and network appliances below the operating
system (OS), is the Intelligent Platform Management Interface (IPMI)
standardized in [IPMI]. The IPMI is focused on health monitoring,
event logging, firmware management, and serial-over-LAN (SOL) remote
console access in a "pre-OS or OS-absent" host environment. The IPMI
operates over a companion Remote Management Control Protocol (RMCP)
for messaging, which itself can use UDP for transport.
Because the IPMI and RCMP are tailored to low-level and well-
connected devices within a datacenter, with typical workflows
requiring many messaging round trips or low-latency interactive
sessions, they are not suitable for operation over a challenged
network.
5.5. Autonomic Networking
The future of network operations requires more autonomous behavior
including self-configuration, self-management, self-healing, and
self-optimization. One approach to support this is termed Autonomic
Networking [RFC7575].
There is a large and growing set of work within the IETF focused on
developing an Autonomic Networking Integrated Model and Approach
(ANIMA). The ANIMA work has developed a comprehensive reference
model for distributing autonomic functions across multiple nodes in
an autonomic networking infrastructure [RFC8993].
This work, focused on learning the behavior of distributed systems to
predict future events, is an emerging network management capability.
This includes the development of signalling protocols such as GRASP
[RFC8990] and the autonomic control plane (ACP) [RFC8368].
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Both autonomic and challenged networks require similar degrees of
autonomy. However, challenged networks cannot provide the complex
coordination between nodes and distributed supporting infrastructure
necessary for the frequent data exchanges for negotiation, learning,
and bootstrapping associated with the above capabilities.
There is some emerging work in ANIMA as to how disconnected devices
might join and leave the autonomic control plane over time. However,
this work is solving an important, but different, problem than that
encountered by challenged networks.
5.6. Deep Space Autonomy
Outside of the terrestrial networking community, there are existing
and established remote management systems used for deep space mission
operations. Examples of two of these are for the New Horizons
mission to Pluto [NEW-HORIZONS] and the DART mission to the asteroid
Dimorphos [DART].
The DTNMA has some heritage in the concepts of deep space autonomy,
but each of those mission instantiations use mission-specific data
encoding, messaging, and transport as well as mission-specific (or
heavily mission-tailored) modeling concepts and languages. Part of
the goal of the DTNMA is to take the proven concepts from these
missions and standardize the messaging syntax as well as a modular
data modeling method.
6. Motivation for New Features
The future of network management will involve autonomous and
autonomic functions operating on both managed and managing devices.
However, the development of distributed autonomy for coordinated
learning and event reaction is different from a managed device
operating without connectivity to a managing node.
Management mechanisms that provide DTNMA desirable properties do not
currently exist. This is not surprising since autonomous management
in the context of a challenged networking environment is an emerging
use case.
In particular, a management architecture is needed that provides the
following new features.
Open Loop Control: Freedom from a request-response architecture,
API, or other presumption of timely round-trip communications.
This is particularly important when managing networks that are not
built over an HTTP or TCP/TLS infrastructure.
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Standard Autonomy Model: An autonomy model that allows for standard
expressions of policy to guarantee deterministic behavior across
devices and vendor implementations.
Compressible Model Structure: A data model that allows for very
compact encodings by defining and exploiting common structures for
data schemas.
Combining these new features with existing mechanisms for message
data exchange (such as BP), data representations (such as CBOR) and
data modeling languages (such as YANG) will form a pragmatic approach
to defining challenged network management.
7. Reference Model
There are a multitude of ways in which both existing and emerging
network management protocols, APIs, and applications can be
integrated for use in challenged environments. However, expressing
the needed behaviors of the DTNMA in the context of any of these pre-
existing components risks conflating systems requirements,
operational assumptions, and implementation design constraints.
7.1. Important Concepts
This section describes a network management concept for challenged
networks (generally) and those conforming to the DTN architecture (in
particular). The goal of this section is to describe how DTNMA
services provide DTNMA desirable properties.
| NOTE: This section assumes a BPv7 underlying network transport.
| Bundles are the baseline transport protocol data units of the
| DTN architecture. Additionally, they may be used in a variety
| of network architectures beyond the DTN architecture.
| Therefore, assuming bundles is a convenient way of scoping
| DTNMA to any network or network architecture that relies on
| BPv7 features.
Similar to other network management architectures, the DTNMA draws a
logical distinction between a managed device and a managing device.
Managed devices use a DA to manage resident applications. Managing
devices use a DM to both monitor and control DAs.
| NOTE: The terms "managing" and "managed" represent logical
| characteristics of a device and are not, themselves, mutually
| exclusive. For example, a managed device might, itself, also
| manage some other device in the network. Therefore, a device
| may support either or both of these characteristics.
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The DTNMA differs from some other management architectures in three
significant ways, all related to the need for a device to self-manage
when disconnected from a managing device.
Pre-shared Definitions: Managing and managed devices should operate
using pre-shared data definitions and models. This implies that
static definitions should be standardized whenever possible and
that managing and managed devices may need to negotiate
definitions during periods of connectivity.
Agent Self-Management: A managed device may find itself disconnected
from its managing device. In many challenged networking
scenarios, a managed device may spend the majority of its time
without a regular connection to a managing device. In these
cases, DAs manage themselves by applying pre-shared policies
received from managing devices.
Command-Based Interface: Managing devices communicate with managed
devices through a command-based interface. Instead of exchanging
variables, objects, or documents, a managing device issues
commands to be run by a managed device. These commands may create
or update variables, change data stores, or impact the managed
device in ways similar to other network management approaches.
The use of commands is, in part, driven by the need for DAs to
receive updates from both remote management devices and local
autonomy. The use of controls for the implementation of commands
is discussed in more detail in Section 9.5.
7.2. Model Overview
A DTNMA reference model is provided in Figure 1 below. In this
reference model, applications and services on a managing device
communicate with a DM which uses pre-shared definitions to create a
set of policy directives that can be sent to a managed device's DA
via a command-based interface. The DA provides local monitoring and
control (commanding) of the applications and services resident on the
managed device. The DA also performs local data fusion as necessary
to synthesize data products (such as reports) that can be sent back
to the DM when appropriate.
DTNMA Reference Model
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Managed Device Managing Device
+----------------------------+ +-----------------------------+
| +------------------------+ | | +-------------------------+ |
| |Applications & Services | | | | Applications & Services | |
| +----------^-------------+ | | +-----------^-------------+ |
| | | | | |
| +----------v-------------+ | | +-----------v-------------+ |
| | DTNMA +-------------+ | | | | +-----------+ DTNMA | |
| | AGENT | Monitor and | | |Commanding | | | Policy | MANAGER | |
| | | Control | | |<==========| | | Encoding | | |
| | +------+-------------+ | | | | +-----------+-------+ | |
| | |Admin | Data Fusion | | |==========>| | | Reporting | Admin | | |
| | +------+-------------+ | | Reporting | | +-----------+-------+ | |
| +------------------------+ | | +-------------------------+ |
+----------------------------+ +-----------------------------+
^ ^
| Pre-Shared Definitions |
| +---------------------------+ |
+--------| - Autonomy Model |--------+
| - Application Data Models |
| - Runtime Data Stores |
+---------------------------+
Figure 1
This model preserves the familiar concept of "managers" resident on
managing devices and "agents" resident on managed devices. However,
the DTNMA model is unique in how the DM and DA operate. The DM is
used to pre-configure DAs in the network with management policies.
it is expected that the DAs, themselves, perform monitoring and
control functions on their own. In this way, a properly configured
DA may operate without a reliable connection back to a DM.
7.3. Functional Elements
The reference model illustrated in Figure 1 implies the existence of
certain logical components whose roles and responsibilities are
discussed in this section.
7.3.1. Managed Applications and Services
By definition, managed applications and services reside on a managed
device. These software entities can be controlled through some
interface by the DA and their state can be sampled as part of
periodic monitoring. It is presumed that the DA on the managed
device has the proper data model, control interface, and permissions
to alter the configuration and behavior of these software
applications.
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7.3.2. DTNMA Agent (DA)
A DA resides on a managed device. As is the case with other network
management approaches, this agent is responsible for the monitoring
and control of the applications local to that device. Unlike other
network management approaches, the agent accomplishes this task
without a regular connection to a DTNMA Manager.
The DA performs three major functions on a managed device: the
monitoring and control of local applications, production of data
analytics, and the administrative control of the agent itself.
7.3.2.1. Monitoring and Control
DAs monitor the status of applications running on their managed
device and selectively control those applications as a function of
that monitoring. The following components are used to perform
monitoring and control on an agent.
Rules Database:
Each DA maintains a database of policy expressions that form
rules of behavior of the managed device. Within this
database, each rule of behavior is a tuple of a stimulus and
a response. Within the DTNMA, these rules are the embodiment
of policy expressions received from DMs and evaluated at
regular intervals by the autonomy engine. The rules database
is the collection of active rules known to the DA.
Autonomy Engine:
The DA autonomy engine monitors the state of the managed
device looking for pre-defined stimuli and, when encountered,
issuing a pre-defined response. To the extent that this
function is driven by the rules database, this engine acts as
a policy execution engine. This engine may also be directly
configured by managers during periods of connectivity for
actions separate from those in the rules database (such as
enabling or disabling sets of rules). Once configured, the
engine may function without other access to any managing
device. This engine may also reconfigure itself as a
function of policy.
Application Control Interfaces:
DAs support control interfaces for all managed applications.
Control interfaces are used to alter the configuration and
behavior of an application. These interfaces may be custom
for each application, or as provided through a common
framework such as provided by an operating system.
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7.3.2.2. Data Fusion
DAs generate new data elements as a function of the current state of
the managed device and its applications. These new data products may
take the form of individual data values, or new collections of data
used for reporting. The logical components responsible for these
behaviors are as follows.
Application Data Interfaces:
DAs support mechanisms by which important state is retrieved
from various applications resident on the managed device.
These data interfaces may be custom for each application, or
as provided through a common framework such as provided by an
operating system.
Data Value Generators:
DAs may support the generation of new data values as a
function of other values collected from the managed device.
These data generators may be configured with descriptions of
data values and the data values they generate may be included
in the overall monitoring and reporting associated with the
managed device.
Report Generators:
DAs may, as appropriate, generate collections of data values
and provide them to whatever local mechanism takes
responsibility for their eventual transmission (or expiration
and removal). Reports can be generated as a matter of policy
or in response to the handling of critical events (such as
errors), or other logging needs. The generation of a report
is independent of whether there exists any connectivity
between a DA and a DM.
7.3.2.3. Administration
DAs perform a variety of administrative services in support of their
configuration. The significant such administrative services are as
follows.
Manager Mapping:
The DTNMA allows for a many-to-many relationship amongst
DTNMA Agents and Managers. A single DM may configure
multiple DAs, and a single DA may be configured by multiple
DMs. Multiple managers may exist in a network for at least
two reasons. First, different managers may exist to control
different applications on a device. Second, multiple
managers increase the likelihood of an agent encountering a
manager when operating in a sparse or challenged environment.
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While the need for multiple managers is required for
operating in a dynamically partitioned network, this
situation allows for the possibility of conflicting
information from different managers. Implementations of the
DTNMA should consider conflict resolution mechanisms. Such
mechanisms might include analyzing managed content, time,
agent location, or other relevant information to select one
manager input over other manager inputs.
Data Verifiers:
DAs might handle large amounts of data produced by various
sources, to include data from local managed applications,
remote managers, and self-calculated values. DAs should
ensure, when possible, that externally generated data values
have the proper syntax and semantic constraints (e.g., data
type and ranges) and any required authorization.
Access Controllers:
DAs support authorized access to the management of individual
applications, to include the administrative management of the
agent itself. This means that a manager may only set policy
on the agent pursuant to verifying that the manager is
authorized to do so.
7.3.3. Managing Applications and Services
Managing applications and services reside on a managing device and
serve as the both the source of DA policy statements and the target
of DA reporting. They may operate with or without an operator in the
loop.
Unlike management applications in unchallenged networks, these
applications cannot exert closed-loop control over any managed device
application. Instead, they exercise open-loop control by producing
policies that can be configured and enforced on managed devices by
DAs.
| NOTE: Closed-loop control in this context refers to the
| practice of waiting for a response from a managed device prior
| to issuing new commands to that device. These "loops" may be
| closed quickly (in milliseconds) or over much longer periods (
| hours, days, years). The alternative to closed-loop control is
| open-loop control, where the issuance of new commands is not
| dependent on receiving responses to previous commands.
| Additionally, there might not be a 1-1 mapping between commands
| and responses. A DA may, for example, produce a single
| response that captures the end state from applying multiple
| commands.
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7.3.4. DTNMA Manager (DM)
A DM resides on a managing device. This manager provides an
interface between various managing applications and services and the
DAs that enforce their policies. In providing this interface, DMs
translate between whatever native interface exists to various
managing applications and the autonomy models used to encode
management policy.
The DM performs three major functions on a managing device: policy
encoding, reporting, and administration.
7.3.4.1. Policy Encoding
DMs translate policy directives from managing applications and
services into standardized policy expressions that can be recognized
by DAs. The following logical components are used to perform this
policy encoding.
Application Control Interfaces:
DMs support control interfaces for managing applications.
These control interfaces are used to receive desired policy
statements from applications. These interfaces may be custom
for each application, or provided through a common framework,
protocol, or operating system.
Policy Encoders:
DAs implement a standardized autonomy model comprising
standardized data elements. This allows the open-loop
control structures provided by managing applications to be
represented in a common language. Policy encoders perform
this encoding function.
Policy Aggregators:
DMs collect multiple encoded policies into messages that can
be sent to DAs over the network. This implies the proper
addressing of agents and the creation of messages that
support store-and-forward operations. It is recommended that
control messages be packaged using BP bundles when there may
be intermittent connectivity between DMs and DAs.
7.3.4.2. Reporting
DMs receive reports on the status of managed devices during periods
of connectivity with the DAs on those devices. The following logical
components are needed to implement reporting capabilities on a DM.
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Report Collectors:
DMs receive reports from DAs in an asynchronous manner. This
means that reports may be received out of chronological order
and in ways that are difficult or impossible to associate
with a specific policy from a managing application. DMs
collect these reports and extract their data in support of
subsequent data analytics.
Data Analyzers:
DMs review sets of data reports from DAs with the purpose of
extracting relevant data to communicate with managing
applications. This may include simple data extraction or may
include more complex processing such as data conversion, data
fusion, and appropriate data analytics.
Application Data Interfaces:
DMs support mechanisms by which data retrieved from agent may
be provided back to managing devices. These interfaces may
be custom for each application, or as provided through a
common framework, protocol, or operating system.
7.3.4.3. Administration
Managers in the DTNMA perform a variety of administrative services in
support of their proper configuration and operation. This includes
the following logical components.
Agent Mappings:
The DTNMA allows DMs to communicate with multiple DAs.
However, not every agent in a network is expected to support
the same set of Application Data Models or otherwise have the
same set of managed applications running. For this reason,
DMs determine individual DA capabilities to ensure that only
appropriate Controls are sent to a DA.
Data Verifiers:
DMs handle large amounts of data produced by various sources,
to include data from managing applications and DAs. DMs
should ensure, when possible, that data values received from
DAs over a network have the proper syntax and semantic
constraints (e.g., data type and ranges) and any required
authorization.
Access Controllers:
DMs should only send Controls to agents when the manager is
configured with appropriate access to both the agent and the
applications being managed.
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7.3.5. Pre-Shared Definitions
A consequence of operating in a challenged environment is the
potential inability to negotiate information in real-time. For this
reason, the DTNMA requires that managed and managing devices operate
using pre-shared definitions rather than relying on data definition
negotiation.
The three types of pre-shared definitions in the DTNMA are the DA
autonomy model, managed application data models, and any runtime data
shared by managers and agents.
Autonomy Model:
A DTNMA autonomy model represents the data elements and
associated autonomy structures that define the behavior of
the agent autonomy engine. A standardized autonomy model
allows for individual implementations of DAs, and DMs to
interoperate. A standardized model also provides guidance to
the design and implementation of both managed and managing
applications.
Application Data Models:
As with other network management architectures, the DTNMA
pre-supposes that managed applications (and services) define
their own data models. These data models include the data
produced by, and Controls implemented by, the application.
These models are expected to be static for individual
applications and standardized for applications implementing
standard protocols.
Runtime Data Stores:
Runtime data stores, by definition, include data that is
defined at runtime. As such, the data is not pre-shared
prior to the deployment of DMs and DAs. Pre-sharing in this
context means that DMs and DAs are able to define and
synchronize data elements prior to their operational use in
the system. This synchronization happens during periods of
connectivity between DMs and DAs.
8. Desired Services
This section provides a description of the services provided by DTNMA
components on both managing and managed devices. These service
descriptions differ from other management descriptions because of the
unique characteristics of the DTNMA operating environment.
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| Predicate autonomy, asynchronous data transport, and
| intermittent connectivity require new techniques for device
| management. Many of the services discussed in this section
| attempt to provide continuous operation of a managed device
| through periods of no connectivity.
8.1. Local Monitoring and Control
DTNMA monitoring is associated with the agent autonomy engine. The
term monitoring implies regular access to information such that state
changes may be acted upon within some response time period. Within
the DTNMA, connections between a managed and managing device are
unable to provide such a connection and, thus, monitoring functions
are performed on the managed device.
Predicate autonomy on a managed device should collect state
associated with the device at regular intervals and evaluate that
collected state for any changes that require a preventative or
corrective action. Similarly, this monitoring may cause the device
to generate one or more reports destined to the managing device.
Similar to monitoring, DTNMA control results in actions by the agent
to change the state or behavior of the managed device. All control
in the DTNMA is local control. In cases where there exists a timely
connection to a manager, received Controls are still run through the
autonomy engine. In this case, the stimulus is the direct receipt of
the Control and the response is to immediately run the Control. In
this way, there is never a dependency on a session or other stateful
exchange with any remote entity.
8.2. Local Data Fusion
DTNMA Fusion services produce new data products from existing state
on the managed device. These fusion products can be anything from
simple summations of sampled counters to complex calculations of
behavior over time.
Fusion is an important service in the DTNMA because fusion products
are part of the overall state of a managed device. Complete
knowledge of this overall state is important for the management of
the device, particularly in a stimulus-response system whose stimuli
are evaluated against this state. In particular, the predicates of
rules on a DA may refer to fused data.
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In-situ data fusion is an important function as it allows for the
construction of intermediate summary data, the reduction of stored
and transmitted raw data, possibly fewer predicates in rule
definitions, and otherwise insulates the data source from conclusions
drawn from that data.
While some fusion is performed in any management system, the DTNMA
requires fusion to occur on the managed device itself. If the
network is partitioned such that no connection to a managing device
is available, fusion happens locally. Similarly, connections to a
managing device might not remain active long enough for round-trip
data exchange or may not have the bandwidth to send all sampled data.
| NOTE: While data fusion is an important function within the
| DTNMA, it is expected that the storage and transmission of raw
| (or pre-fused) data remains a capability of the system. In
| particular, raw data can be useful for debugging managed
| devices, understanding complex interactions and underlying
| conditions, and tuning for better performance and/or better
| outcomes.
8.3. Remote Configuration
DTNMA configuration services update the local configuration of a
managed device with the intent to impact the behavior and
capabilities of that device. The change of device configurations is
a common service provided by many network management systems. The
DTNMA has a unique approach to configuration for the following
reasons.
The DTNMA configuration service is unique in that the selection of
managed device configurations occurs, itself, as a function of the
state of the device. This implies that management proxies on the
device store multiple configuration functions that can be applied as
needed without consultation from a managing device.
| This approach differs from the management concept of selecting
| from multiple datastores in that DTNMA configuration functions
| can target individual data elements and can calculate new
| values from local device state.
When detecting stimuli, the agent autonomy engine supports a
mechanism for evaluating whether application monitoring data or
runtime data values are recent enough to indicate a change of state.
In cases where data has not been updated recently, it may be
considered stale and not used to reliably indicate that some stimulus
has occurred.
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8.4. Remote Reporting
DTNMA reporting services collect information known to the managed
device and prepare it for eventual transmission to one or more
managing devices. The contents of these reports, and the frequency
at which they are generated, occurs as a function of the state of the
managed device, independent of the managing device.
Once generated, it is expected that reports might be queued pending a
connection back to a managing device. Therefore, reports need to be
differentiable as a function of the time they were generated.
| NOTE: When reports are queued pending transmission, the overall
| storage capacity at the queuing device needs to be considered.
| There may be cases where queued reports can be considered
| expired either because they have been queued for too long, or
| because they have been replaced by a newer report. When a
| report is considered expired, it may be considered for removal
| and, thus, never transmitted. This consideration is expected
| to be part of the implementation of the queuing device and not
| the responsibility of the reporting function within the DTNMA.
When reports are sent to a managing device over a challenged network,
they may arrive out of order due to taking different paths through
the network or being delayed due to retransmissions. A managing
device should not infer meaning from the order in which reports are
received.
Reports may or may not be associated with a specific Control. Some
reports may be annotated with the Control that caused the report to
be generated. Sometimes, a single report will represent the end
state of applying multiple Controls.
8.5. Authorization
Both local and remote services provided by the DTNMA affect the
behavior of multiple applications on a managed device and may
interface with multiple managing devices.
Authorization services enforce the potentially complex mapping of
other DTNMA services amongst managed and managing devices in the
network. For example, fine-grained access control can determine
which managing devices receive which reports, and what Controls can
be used to alter which managed applications.
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This is particularly beneficial in networks that either deal with
multiple administrative entities or overlay networks that cross
administrative boundaries. Allowlists, blocklists, key-based
infrastructures, or other schemes may be used for this purpose.
9. Logical Autonomy Model
An important characteristic of the DTNMA is the shift in the role of
a managing device. In the DTNMA, managers configure the autonomy
engines on agents, and it is the agents that provide local device
management. One way to describe the behavior of the agent autonomy
engine is to describe the characteristics of the autonomy model it
implements.
This section describes a logical autonomy model in terms of the
abstract data elements that would comprise the model. Defining
abstract data elements allows for an unambiguous discussion of the
behavior of an autonomy model without mandating a particular design,
encoding, or transport associated with that model.
9.1. Overview
A managing autonomy capability on a potentially disconnected device
needs to behave in both an expressive and deterministic way.
Expressivity allows for the model to be configured for a wide range
of future situations. Determinism allows for the forensic
reconstruction of device behavior as part of debugging or recovery
efforts. It also is necessary to ensure predictable behavior.
| NOTE: The use of predicate logic and a stimulus-response system
| does not conflict with the use of higher-level autonomous
| function or the incorporation of machine learning.
| Specifically, the DTNMA deterministic autonomy model can
| coexist with other autonomous functions managing applications
| and network services.
|
| An example of such co-existence is the use of the DTNMA model
| to ensure a device stays within safe operating parameters while
| a less deterministic machine learning model directs smaller
| behaviors for the device.
The DTNMA autonomy model is a rule-based model in which individual
rules associate a pre-identified stimulus with a pre-configured
response to that stimulus.
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Stimuli are identified using one or more predicate logic expressions
that examine aspects of the state of the managed device. Responses
are implemented by running one or more procedures on the managed
device.
In its simplest form, a stimulus is a single predicate expression of
a condition that examines some aspect of the state of the managed
device. When the condition is met, a predetermined response is
applied. This behavior can be captured using the construct:
IF <condition 1> THEN <response 1>;
In more complex forms, a stimulus may include both a common condition
shared by multiple rules and a specific condition for each individual
rule. If the common condition is not met, the evaluation of the
specific condition of each rule sharing the common condition can be
skipped. In this way, the total number of predicate evaluations can
be reduced. This behavior can be captured using the construct:
IF <common condition> THEN
IF <specific condition 1> THEN <response 1>
IF <specific condition 2> THEN <response 2>
IF <specific condition 3> THEN <response 3>
| NOTE: The DTNMA model remains a stimulus-response system,
| regardless of whether a common condition is part of the
| stimulus. However, it is recommended that implementations
| incorporate a common condition because of the efficiency
| provided by such a bulk evaluation.
|
| NOTE: One use of a stimulus "common condition" is to associated
| the condition with an on-board event such as the expiring of a
| timer or the changing of a monitored value.
|
| NOTE: The DTNMA does not prescribe when to evaluate rule
| stimuli. Implementations may choose to evaluate rule stimuli
| at periodic intervals (such as 1Hz or 100Hz). When stimuli
| include on-board events, implementations may choose to perform
| an immediate evaluation at the time of the event rather than
| waiting for a periodic evaluation.
DTNMA Autonomy Model
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Managed Applications | DTNMA Agent | DTNMA Manager
+---------------------+--------------------------------+--------------+
| +---------+ |
| | Local | | Encoded
| | Rule DB |<-------------------- Policy
| +---------+ | Expressions
| ^ |
| | |
| v |
| +----------+ +---------+ |
Monitoring Data------>| Agent | | Runtime | |
| | Autonomy |<-->| Data |<---- Definitions
Application Control<------| Engine | | Store | |
| +----------+ +---------+ |
| | |
| +-------------------------> Reports
| |
Figure 2
The flow of data into and out of the agent autonomy engine is
illustrated in Figure 2. In this model, the autonomy engine stores
the combination of stimulus conditions and associated responses as a
set of "rules" in a rules database. This database is updated through
the execution of the autonomy engine and as configured from policy
statements received by managers.
Stimuli are detected by examining the state of applications as
reported through application monitoring interfaces and through any
locally-derived data. Local data is calculated in accordance with
definitions also provided by managers as part of the runtime data
store.
Responses to stimuli are run as updated to the rules database,
updated to the runtime data store, Controls sent to applications, and
the generation of reports.
9.2. Model Characteristics
There are several practical challenges to the implementation of a
distributed rule-based system. Large numbers of rules may be
difficult to understand, deconflict, and debug. Rules whose
conditions are given by fused or other dynamic data may require data
logging and reporting for deterministic offline analysis. Rule
differences across managed devices may lead to oscillating effects.
This section identifies those characteristics of an autonomy model
that might help implementations mitigate some of these challenges.
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There are a number of ways to represent data values, and many data
modeling languages exist for this purpose. When considering how to
model data in the context of the DTNMA autonomy model there are some
modeling features that should be present to enable functionality.
There are also some modeling features that should be prevented to
avoid ambiguity.
Traditional network management approaches favor flexibility in their
data models. The DTNMA stresses deterministic behavior that supports
forensic analysis of agent activities "after the fact". As such, the
following statements should be true of all data representations
relating to DTNMA autonomy.
Strong Typing: The predicates and expressions that comprise the
autonomy services in the DTNMA should require strict data typing.
This avoids errors associated with implicit data conversions and
helps detect misconfiguration.
Acyclic Dependency: Many dependencies exist in an autonomy model,
particularly when combining individual expressions or results to
create complex behaviors. Implementations that conform to the
DTNMA need to prevent circular dependencies.
Fresh Data: Autonomy models operating on data values presume that
their data inputs represent the actionable state of the managed
device. If a data value has failed to be refreshed within a time
period, autonomy might incorrectly infer an operational state.
Regardless of whether a data value has changed, DTNMA
implementations should provide some indicator of whether the data
value is "fresh" meaning that it still represents the current
state of the device.
Pervasive Parameterization: Where possible, autonomy model objects
should support parameterization to allow for flexibility in the
specification. Parameterization allows for the definition of
fewer unique model objects and also can support the substitution
of local device state when exercising device control or data
reporting.
Configurable Cardinality: The number of data values that can be
supported in a given implementation is finite. For devices
operating in challenged environments, the number of supported
objects may be far fewer than that which can be supported by
devices in well-resourced environments. DTNMA implementations
should define limits to the number of supported objects that can
be active in a system at one time, as a function of the resources
available to the implementation.
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Control-Based Updates: The agent autonomy engine changes the state
of the managed device by running Controls on the device. This is
different from other approaches where the behavior of a managed
device is updated only by updated configuration values, such as in
a table or datastore. Altering behavior via one or more Controls
allows checking all pre-conditions before making changes as well
as providing more granularity in the way in which the device is
updated. Where necessary, Controls can be defined to perform bulk
updates of configuration data so as not to lose that update
modality. One important update pre-condition is that the system
is not performing an action that would prevent the update (such as
currently applying a competing update).
9.3. Data Value Representation
The expressive representation of simple data values is fundamental to
the successful construction and evaluation of predicates in the DTNMA
autonomy model. When defining such values, there are useful
distinctions regarding how values are identified and whether values
are generated internal or external to the autonomy model.
A DTNMA data value should combine a base type (e.g., integer, real,
string) representation with relevant semantic information. Base
types are used for proper storage and encoding. Semantic information
allows for additional typing, constraint definitions, and mnemonic
naming. This expanded definition of data value allows for better
predicate construction and evaluation, early type checking, and other
uses.
Data values may further be annotated based on whether their value is
the result of a DA calculation or the result of some external process
on the managed device. For example, operators may with to know which
values can be updated by actions on the DA versus which values (such
as sensor readings) cannot be reliably changed because they are
calculated external to the DA.
9.4. Data Reporting
The DTNMA autonomy model should, as required, report on the state of
its managed device (to include the state of the model itself). This
reporting should be done as a function of the changing state of the
managed device, independent of the connection to any managing device.
Queuing reports allows for later forensic analysis of device
behavior, which is a desirable property of DTNMA management.
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DTNMA data reporting consists of the production of some data report
instance conforming to a data report schema. The use of schemas
allows a report instance to identify the schema to which it conforms
in lieu of carry that structure in the instance itself. This
approach can significantly reduce the size of generated reports.
| NOTE: The DTNMA data reporting concept is intentionally
| distinct from the concept of exchanging data stores across a
| network. It is envisioned that a DA might generate a data
| report instance of a data report schema at regular intervals or
| in response to local events. In this model, many report
| schemas may be defined to capture unique, relevant combinations
| of known data values rather than sending bulk data stores off-
| platform for analysis.
|
| NOTE: It is not required that data report schemas be tabular in
| nature. Individual implementations might define tabular
| schemas for table-like data and other report schemas for more
| heterogeneous reporting.
9.5. Command Execution
The agent autonomy engine requires that managed devices issue
commands on themselves as if they were otherwise being controlled by
a managing device. The DTNMA implements commanding through the use
of Controls and macros.
Controls represent parameterized, predefined procedures run by the DA
either as directed by the DM or as part of a rule response from the
DA autonomy engine. Macros represent ordered sequences of Controls.
Controls are conceptually similar to RPCs in that they represent
parameterized functions run on the managed device. However, they are
conceptually dissimilar from RPCs in that they do not have a concept
of a return code because they operate over an asynchronous transport.
The concept of return code in an RPC implies a synchronous
relationship between the caller of the procedure and the procedure
being called, which might not be possible within the DTNMA.
The success or failure of a Control may be handled locally by the
agent autonomy engine. Local error handling is particularly
important in this architecture given the potential for long periods
of disconnectivity between a DA and a DM. The failure of one or more
Controls on a DA represent part of the state of the DA and,
therefore, able to trigger rules as part of the Agent autonomy
engine.
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The impact of a Control is externally observable via the generation
and eventual examination of data reports produced by the managed
device.
The failure of certain Controls might leave a managed device in an
undesired state. Therefore, it is important that there be
consideration for Control-specific recovery mechanisms (such as a
rollback or safing mechanism). When a Control that is part of a
macro (such as in an autonomy response) fails, there may be a need to
implement a safe state for the managed device based on the nature of
the failure.
| NOTE: The use of the term Control in the DTNMA is derived in
| part from the concept of Command and Control (C2) where control
| implies the operational instructions undertaken to implement
| (or maintain) a commanded objective. The DA autonomy engine
| controls a managed device to allow it to fulfill some purpose
| as commanded by a (possibly disconnected) managing device.
|
| For example, attempting to maintain a safe internal thermal
| environment for a spacecraft is considered "thermal control"
| (not "thermal commanding") even though thermal control involves
| sending commands to heaters, louvers, radiators, and other
| temperature-affecting components.
|
| Even when Controls are received from a managing device with the
| intent to be run immediately, the control-vs-command
| distinction still applies. The Control being run on the
| managed device is in service of the command received from the
| managing device to immediately change the local state of the
| device.
9.6. Predicate Autonomy Rules
As discussed in Section 9.1, the DTNMA rule-based stimulus-response
system associates stimulus detection with a predetermined response.
Rules may be categorized based on whether their stimuli include
generic statements of managed device state or whether they are
optimized to only consider the passage of time on the device.
State-based rules are those whose stimulus is based on the evaluated
state of the managed device. Time-based rules are a unique subset of
state-based rules whose stimulus is given only by a time-based event.
Implementations might create different structures and evaluation
mechanisms for these two different types of rules to achieve more
efficient processing on a platform.
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10. Use Cases
Using the autonomy model defined in Section 9, this section describes
flows through sample configurations conforming to the DTNMA. These
use cases illustrate remote configuration, local monitoring and
control, multiple manager support, and data fusion.
10.1. Notation
The use cases presented in this section are documented with a
shorthand notation to describe the types of data sent between
managers and agents. This notation, outlined in Table 1, leverages
the definitions of autonomy model components defined in Section 9.
+==================+===================================+===========+
| Term | Definition | Example |
+==================+===================================+===========+
| EDD# | Externally Defined Data - a data | EDD1, |
| | value defined external to the DA. | EDD2 |
+------------------+-----------------------------------+-----------+
| V# | Variable - a data value defined | V1 = EDD1 |
| | internal to the DA. | + 7 |
+------------------+-----------------------------------+-----------+
| EXPR | Predicate expression - used to | V1 > 5 |
| | define a rule stimulus. | |
+------------------+-----------------------------------+-----------+
| ID | DTNMA Object Identifier. | V1, EDD2 |
+------------------+-----------------------------------+-----------+
| ACL# | Enumerated Access Control List. | ACL1 |
+------------------+-----------------------------------+-----------+
| DEF(ACL,ID,EXPR) | Define ID from expression. Allow | DEF(ACL1, |
| | managers in ACL to see this ID. | V1, EDD1 |
| | | + EDD2) |
+------------------+-----------------------------------+-----------+
| PROD(P,ID) | Produce ID according to predicate | PROD(1s, |
| | P. P may be a time period (1s) | EDD1) |
| | or an expression (EDD1 > 10). | |
+------------------+-----------------------------------+-----------+
| RPT(ID) | A report instance containing data | RPT(EDD1) |
| | named ID. | |
+------------------+-----------------------------------+-----------+
Table 1: Terminology
These notations do not imply any implementation approach. They only
provide a succinct syntax for expressing the data flows in the use
case diagrams in the remainder of this section.
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10.2. Serialized Management
This nominal configuration shows a single DM interacting with
multiple DAs. The control flows for this scenario are outlined in
Figure 3.
Serialized Management Control Flow
+-----------+ +---------+ +---------+
| DTNMA | | DTNMA | | DTNMA |
| Manager A | | Agent A | | Agent B |
+----+------+ +----+----+ +----+----+
| | |
|-----PROD(1s, EDD1)--->| | (1)
|----------------------------PROD(1s, EDD1)-->|
| | |
| | |
|<-------RPT(EDD1)------| | (2)
|<----------------------------RPT(EDD1)-------|
| | |
| | |
|<-------RPT(EDD1)------| |
|<----------------------------RPT(EDD1)-------|
| | |
| | |
|<-------RPT(EDD1)------| |
|<----------------------------RPT(EDD1)-------|
| | |
Figure 3
In a serialized management scenario, a single DM interacts with
multiple DAs.
In this figure, the DTNMA Manager A sends a policy to DTNMA Agents A
and B to report the value of an EDD (EDD1) every second in (step 1).
Each DA receives this policy and configures their respective autonomy
engines for this production. Thereafter, (step 2) each DA produces a
report containing data element EDD1 and sends those reports back to
the DM.
This behavior continues without any additional communications from
the DM and without requiring a connection between the DA and DM.
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10.3. Intermittent Connectivity
Building from the nominal configuration in Section 10.2, this
scenario shows a challenged network in which connectivity between
DTNMA Agent B and the DM is temporarily lost. Control flows for this
case are outlined in Figure 4.
Challenged Management Control Flow
+-----------+ +---------+ +---------+
| DTNMA | | DTNMA | | DTNMA |
| Manager A | | Agent A | | Agent B |
+----+------+ +----+----+ +----+----+
| | |
|-----PROD(1s, EDD1)--->| | (1)
|----------------------------PROD(1s, EDD1)-->|
| | |
| | |
|<-------RPT(EDD1)------| | (2)
|<----------------------------RPT(EDD1)-------|
| | |
| | |
|<-------RPT(EDD1)------| |
|<----------------------------RPT(EDD1)-------|
| | |
| | |
|<-------RPT(EDD1)------| |
| | RPT(EDD1)| (3)
| | |
| | |
|<-------RPT(EDD1)------| |
| | RPT(EDD1)| (4)
| | |
| | |
|<-------RPT(EDD1)------| |
|<----------------RPT(EDD1), RPT(EDD1)--------| (5)
| | |
Figure 4
In a challenged network, DAs store reports pending a transmit
opportunity.
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In this figure, DTNMA Manager A sends a policy to DTNMA Agents A and
B to produce an EDD (EDD1) every second in (step 1). Each DA
receives this policy and configures their respective autonomy engines
for this production. Produced reports are transmitted when there is
connectivity between the DA and DM (step 2).
At some point, DTNMA Agent B loses the ability to transmit in the
network (steps 3 and 4). During this time period, DA B continues to
produce reports, but they are queued for transmission. This queuing
might be done by the DA itself or by a supporting transport such as
BP. Eventually (and before the next scheduled production of EDD1),
DTNMA Agent B is able to transmit in the network again (step 5) and
all queued reports are sent at that time. DTNMA Agent A maintains
connectivity with the DM during steps 3-5, and continues to send
reports as they are generated.
10.4. Open-Loop Reporting
This scenario illustrates the DTNMA open-loop control paradigm, where
DAs manage themselves in accordance with policies provided by DMs,
and provide reports to DMs based on these policies.
The control flow shown in Figure 5, includes an example of data
fusion, where multiple policies configured by a DM result in a single
report from a DA.
Consolidated Management Control Flow
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+-----------+ +---------+ +---------+
| DTNMA | | DTNMA | | DTNMA |
| Manager A | | Agent A | | Agent B |
+----+------+ +----+----+ +----+----+
| | |
|-----PROD(1s, EDD1)--->| | (1)
|----------------------------PROD(1s, EDD1)-->|
| | |
| | |
|<-------RPT(EDD1)------| | (2)
|<----------------------------RPT(EDD1)-------|
| | |
| | |
|----------------------------PROD(1s, EDD2)-->| (3)
| | |
| | |
|<-------RPT(EDD1)------| |
|<--------------------------RPT(EDD1,EDD2)----| (4)
| | |
| | |
|<-------RPT(EDD1)------| |
|<--------------------------RPT(EDD1,EDD2)----|
| | |
Figure 5
A many-to-one mapping between management policy and device state
reporting is supported by the DTNMA.
In this figure, DTNMA Manager A sends a policy statement in the form
of a rule to DTNMA Agents A and B, which instructs the DAs to produce
a report with EDD1 every second (step 1). Each DA receives this
policy, which is stored in its respective Rule Database, and
configures its Autonomy Engine. Reports are transmitted by each DA
when produced (step 2).
At a later time, DTNMA Manager A sends an additional policy to DTNMA
Agent B, requesting the production of a report for EDD2 every second
(step 3). This policy is added to DTNMA Agent B's Rule Database.
Following this policy update, DTNMA Agent A will continue to produce
EDD1 and DTNMA Agent B will produce both EDD1 and EDD2 (step 4).
However, DTNMA Agent B may provide these values to the DM in a single
report rather than as 2 independent reports. In this way, there is
no direct mapping between the single consolidated report sent by
DTNMA Agent B (step 4) and the two different policies sent to DTNMA
Agent B that caused that report to be generated (steps 1 and 3).
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10.5. Multiple Administrative Domains
The managed applications on a DA may be controlled by different
administrative entities in a network. The DTNMA allows DAs to
communicate with multiple DMs in the network, such as in cases where
there is one DM per administrative domain.
Whenever a DM sends a policy expression to a DA, that policy
expression may be associated with authorization information. One
method of representing this is an ACL.
| The use of an ACL in this use case does not imply the DTNMA
| requires ACLs to annotate policy expressions. Further, the
| inclusion of ACLs in the policy expressions themselves is for
| representation purposes only, as ACLs are internal to DAs and
| not supplied explicitly in messaging. ACLs and their
| representation in this context are for example purposes only.
The ability of one DM to access the results of policy expressions
configured by some other DM will be limited to the authorization
annotations of those policy expressions.
An example of multi-manager authorization is illustrated in Figure 6.
Multiplexed Management Control Flow
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+-----------+ +---------+ +-----------+
| DTNMA | | DTNMA | | DTNMA |
| Manager A | | Agent A | | Manager B |
+-----+-----+ +----+----+ +-----+-----+
| | |
|---DEF(ACL1,V1,EDD1*2)--->|<---DEF(ACL2, V2, EDD2*2)---| (1)
| | |
|---PROD(1s, V1)---------->|<---PROD(1s, V2)------------| (2)
| | |
|<--------RPT(V1)----------| | (3)
| |--------RPT(V2)------------>|
|<--------RPT(V1)----------| |
| |--------RPT(V2)------------>|
| | |
| |<---PROD(1s, V1)------------| (4)
| | |
| |----ERR(V1 no perm.)------->|
| | |
|--DEF(NULL,V3,EDD3*3)---->| | (5)
| | |
|---PROD(1s, V3)---------->| | (6)
| | |
| |<----PROD(1s, V3)-----------|
| | |
|<--------RPT(V3)----------|--------RPT(V3)------------>| (7)
|<--------RPT(V1)----------| |
| |--------RPT(V2)------------>|
|<-------RPT(V3)-----------|--------RPT(V3)------------>|
|<-------RPT(V1)-----------| |
| |--------RPT(V2)------------>|
Figure 6
Multiple DMs may interface with a single DA, particularly in complex
networks.
In this figure, both DTNMA Managers A and B send policies to DTNMA
Agent A (step 1). DM A defines a variable (V1) whose value is given
by the mathematical expression (EDD1 * 2) and is associated with an
ACL (ACL1) that restricts access to V1 to DM A only. Similarly, DM B
defines a variable (V2) whose value is given by the mathematical
expression (EDD2 * 2) and associated with an ACL (ACL2) that
restricts access to V2 to DM B only.
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Both DTNMA Managers A and B also send policies to DTNMA Agent A to
report on the values of their variables at 1 second intervals (step
2). Since DM A can access V1 and DM B can access V2, there is no
authorization issue with these policies and they are both accepted by
the autonomy engine on Agent A. Agent A produces reports as
expected, sending them to their respective managers (step 3).
Later (step 4) DM B attempts to configure DA A to also report to it
the value of V1. Since DM B does not have authorization to view this
variable, DA A does not include this in the configuration of its
autonomy engine and, instead, some indication of permission error is
included in any regular reporting back to DM B.
DM A also sends a policy to Agent A (step 5) that defines a variable
(V3) whose value is given by the mathematical expression (EDD3 * 3)
and is not associated with an ACL, indicating that any DM can access
V3. In this instance, both DM A and DM B can then send policies to
DA A to report the value of V3 (step 6). Since there is no
authorization restriction on V3, these policies are accepted by the
autonomy engine on Agent A and reports are sent to both DM A and B
over time (step 7).
10.6. Cascading Management
There are times where a single network device may serve as both a DM
for other DAs in the network and, itself, as a device managed by
someone else. This may be the case on nodes serving as gateways or
proxies. The DTNMA accommodates this case by allowing a single
device to run both a DA and DM.
An example of this configuration is illustrated in Figure 7.
Data Fusion Control Flow
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---------------------------------------
| Node B |
| |
+-----------+ | +-----------+ +---------+ | +---------+
| DTNMA | | | DTNMA | | DTNMA | | | DTNMA |
| Manager A | | | Manager B | | Agent B | | | Agent C |
+---+-------+ | +-----+-----+ +----+----+ | +----+----+
| | | | | |
|--------------DEF(NULL,V0,EDD1+EDD2)-->| | | (1)
|--------------PROD(1s,V0)------------->| | |
| | | | | |
| | |--PROD(1s,EDD1)-->| | | (2)
| | |---------------------PROD(1s,EDD2)-->| (2)
| | | | | |
| | | | | |
| | |<----RPT(EDD1)----| | | (3)
| | |<--------------------RPT(EDD2)-------| (3)
| | | | | |
|<-------------RPT(V0)------------------| | | (4)
| | | | | |
| | | | | |
| |
| |
---------------------------------------
Figure 7
A device can operate as both a DTNMA Manager and an Agent.
In this example, we presume that DA B is able to sample a given EDD
(EDD1) and that DA C is able to sample a different EDD (EDD2). Node
B houses DM B (which controls DA C) and DA B (which is controlled by
DM A). DM A must periodically receive some new value that is
calculated as a function of both EDD1 and EDD2.
First, DM A sends a policy to DA B to define a variable (V0) whose
value is given by the mathematical expression (EDD1 + EDD2) without a
restricting ACL. Further, DM A sends a policy to DA B to report on
the value of V0 every second (step 1).
DA B can require the ability to monitor both EDD1 and EDD2. However,
the only way to receive EDD2 values is to have them reported back to
Node B by DA C and included in the Node B runtime data stores.
Therefore, DM B sends a policy to DA C to report on the value of EDD2
(step 2).
DA C receives the policy in its autonomy engine and produces reports
on the value of EDD2 every second (step 3).
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DA B may locally sample EDD1 and EDD2 and uses that to compute values
of V0 and report on those values at regular intervals to DM A (step
4).
While a trivial example, the mechanism of associating fusion with the
Agent function rather than the Manager function scales with fusion
complexity. Within the DTNMA, DAs and DMs are not required to be
separate software implementations. There may be a single software
application running on Node B implementing both DM B and DA B roles.
11. IANA Considerations
This document requires no IANA actions.
12. Security Considerations
Security within a DTNMA exists in at least two layers: security in
the data model and security in the messaging and encoding of the data
model.
Data model security refers to the validity and accessibility of data
elements. For example, a data element might be available to certain
DAs or DMs in a system, whereas the same data element may be hidden
from other DAs or DMs. Both verification and authorization
mechanisms at DAs and DMs are important to achieve this type of
security.
| NOTE: One way to provide finer-grained application security is
| through the use of Access Control Lists (ACLs) that would be
| defined as part of the configuration of DAs and DMs. It is
| expected that many common data model tools provide mechanisms
| for the definition of ACLs and best practices for their
| operational use.
The exchange of information between and amongst DAs and DMs in the
DTNMA is expected to be accomplished through some secured messaging
transport.
13. Informative References
[ASN.1] International Organization for Standardization,
"Information processing systems - Open Systems
Interconnection - Specification of Abstract Syntax
Notation One (ASN.1)", International Standard 8824,
December 1987.
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[DART] Tropf, B. T., Haque, M., Behrooz, N., and C. Krupiarz,
"The DART Autonomy System", 2023,
<https://ieeexplore.ieee.org/abstract/document/10207457>.
[gNMI] OpenConfig, "gRPC Network Management Interface (gNMI)",
May 2023, <https://www.openconfig.net/docs/gnmi/gnmi-
specification/>.
[gRPC] gRPC Authors, "gRPC Documentation", 2024,
<https://grpc.io/docs/>.
[I-D.ietf-core-comi]
Veillette, M., Van der Stok, P., Pelov, A., Bierman, A.,
and C. Bormann, "CoAP Management Interface (CORECONF)",
Work in Progress, Internet-Draft, draft-ietf-core-comi-16,
4 September 2023, <https://datatracker.ietf.org/doc/html/
draft-ietf-core-comi-16>.
[I-D.ietf-core-sid]
Veillette, M., Pelov, A., Petrov, I., Bormann, C., and M.
Richardson, "YANG Schema Item iDentifier (YANG SID)", Work
in Progress, Internet-Draft, draft-ietf-core-sid-24, 22
December 2023, <https://datatracker.ietf.org/doc/html/
draft-ietf-core-sid-24>.
[I-D.rfernando-protocol-buffers]
Stuart, S. and R. Fernando, "Encoding rules and MIME type
for Protocol Buffers", Work in Progress, Internet-Draft,
draft-rfernando-protocol-buffers-00, 11 October 2012,
<https://datatracker.ietf.org/doc/html/draft-rfernando-
protocol-buffers-00>.
[IPMI] Intel, Hewlett-Packard, NEC, and Dell, "Intelligent
Platform Management Interface Specification, Second
Generation", October 2013,
<https://www.intel.la/content/dam/www/public/us/en/
documents/specification-updates/ipmi-intelligent-platform-
mgt-interface-spec-2nd-gen-v2-0-spec-update.pdf>.
[NEW-HORIZONS]
Moore, R. C., "Autonomous safeing and fault protection for
the New Horizons mission to Pluto", March 2007,
<https://www.sciencedirect.com/science/article/pii/
S0094576507000604>.
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[RFC2578] McCloghrie, K., Ed., Perkins, D., Ed., and J.
Schoenwaelder, Ed., "Structure of Management Information
Version 2 (SMIv2)", STD 58, RFC 2578,
DOI 10.17487/RFC2578, April 1999,
<https://www.rfc-editor.org/info/rfc2578>.
[RFC2982] Kavasseri, R., Ed., "Distributed Management Expression
MIB", RFC 2982, DOI 10.17487/RFC2982, October 2000,
<https://www.rfc-editor.org/info/rfc2982>.
[RFC3165] Levi, D. and J. Schoenwaelder, "Definitions of Managed
Objects for the Delegation of Management Scripts",
RFC 3165, DOI 10.17487/RFC3165, August 2001,
<https://www.rfc-editor.org/info/rfc3165>.
[RFC3410] Case, J., Mundy, R., Partain, D., and B. Stewart,
"Introduction and Applicability Statements for Internet-
Standard Management Framework", RFC 3410,
DOI 10.17487/RFC3410, December 2002,
<https://www.rfc-editor.org/info/rfc3410>.
[RFC3411] Harrington, D., Presuhn, R., and B. Wijnen, "An
Architecture for Describing Simple Network Management
Protocol (SNMP) Management Frameworks", STD 62, RFC 3411,
DOI 10.17487/RFC3411, December 2002,
<https://www.rfc-editor.org/info/rfc3411>.
[RFC3414] Blumenthal, U. and B. Wijnen, "User-based Security Model
(USM) for version 3 of the Simple Network Management
Protocol (SNMPv3)", STD 62, RFC 3414,
DOI 10.17487/RFC3414, December 2002,
<https://www.rfc-editor.org/info/rfc3414>.
[RFC3416] Presuhn, R., Ed., "Version 2 of the Protocol Operations
for the Simple Network Management Protocol (SNMP)",
STD 62, RFC 3416, DOI 10.17487/RFC3416, December 2002,
<https://www.rfc-editor.org/info/rfc3416>.
[RFC3417] Presuhn, R., Ed., "Transport Mappings for the Simple
Network Management Protocol (SNMP)", STD 62, RFC 3417,
DOI 10.17487/RFC3417, December 2002,
<https://www.rfc-editor.org/info/rfc3417>.
[RFC3418] Presuhn, R., Ed., "Management Information Base (MIB) for
the Simple Network Management Protocol (SNMP)", STD 62,
RFC 3418, DOI 10.17487/RFC3418, December 2002,
<https://www.rfc-editor.org/info/rfc3418>.
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[RFC4838] Cerf, V., Burleigh, S., Hooke, A., Torgerson, L., Durst,
R., Scott, K., Fall, K., and H. Weiss, "Delay-Tolerant
Networking Architecture", RFC 4838, DOI 10.17487/RFC4838,
April 2007, <https://www.rfc-editor.org/info/rfc4838>.
[RFC4949] Shirey, R., "Internet Security Glossary, Version 2",
FYI 36, RFC 4949, DOI 10.17487/RFC4949, August 2007,
<https://www.rfc-editor.org/info/rfc4949>.
[RFC5591] Harrington, D. and W. Hardaker, "Transport Security Model
for the Simple Network Management Protocol (SNMP)",
STD 78, RFC 5591, DOI 10.17487/RFC5591, June 2009,
<https://www.rfc-editor.org/info/rfc5591>.
[RFC5592] Harrington, D., Salowey, J., and W. Hardaker, "Secure
Shell Transport Model for the Simple Network Management
Protocol (SNMP)", RFC 5592, DOI 10.17487/RFC5592, June
2009, <https://www.rfc-editor.org/info/rfc5592>.
[RFC5652] Housley, R., "Cryptographic Message Syntax (CMS)", STD 70,
RFC 5652, DOI 10.17487/RFC5652, September 2009,
<https://www.rfc-editor.org/info/rfc5652>.
[RFC5953] Hardaker, W., "Transport Layer Security (TLS) Transport
Model for the Simple Network Management Protocol (SNMP)",
RFC 5953, DOI 10.17487/RFC5953, August 2010,
<https://www.rfc-editor.org/info/rfc5953>.
[RFC6241] Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
and A. Bierman, Ed., "Network Configuration Protocol
(NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,
<https://www.rfc-editor.org/info/rfc6241>.
[RFC6242] Wasserman, M., "Using the NETCONF Protocol over Secure
Shell (SSH)", RFC 6242, DOI 10.17487/RFC6242, June 2011,
<https://www.rfc-editor.org/info/rfc6242>.
[RFC6991] Schoenwaelder, J., Ed., "Common YANG Data Types",
RFC 6991, DOI 10.17487/RFC6991, July 2013,
<https://www.rfc-editor.org/info/rfc6991>.
[RFC7228] Bormann, C., Ersue, M., and A. Keranen, "Terminology for
Constrained-Node Networks", RFC 7228,
DOI 10.17487/RFC7228, May 2014,
<https://www.rfc-editor.org/info/rfc7228>.
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[RFC7252] Shelby, Z., Hartke, K., and C. Bormann, "The Constrained
Application Protocol (CoAP)", RFC 7252,
DOI 10.17487/RFC7252, June 2014,
<https://www.rfc-editor.org/info/rfc7252>.
[RFC7540] Belshe, M., Peon, R., and M. Thomson, Ed., "Hypertext
Transfer Protocol Version 2 (HTTP/2)", RFC 7540,
DOI 10.17487/RFC7540, May 2015,
<https://www.rfc-editor.org/info/rfc7540>.
[RFC7575] Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
Networking: Definitions and Design Goals", RFC 7575,
DOI 10.17487/RFC7575, June 2015,
<https://www.rfc-editor.org/info/rfc7575>.
[RFC7589] Badra, M., Luchuk, A., and J. Schoenwaelder, "Using the
NETCONF Protocol over Transport Layer Security (TLS) with
Mutual X.509 Authentication", RFC 7589,
DOI 10.17487/RFC7589, June 2015,
<https://www.rfc-editor.org/info/rfc7589>.
[RFC7950] Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language",
RFC 7950, DOI 10.17487/RFC7950, August 2016,
<https://www.rfc-editor.org/info/rfc7950>.
[RFC7951] Lhotka, L., "JSON Encoding of Data Modeled with YANG",
RFC 7951, DOI 10.17487/RFC7951, August 2016,
<https://www.rfc-editor.org/info/rfc7951>.
[RFC8040] Bierman, A., Bjorklund, M., and K. Watsen, "RESTCONF
Protocol", RFC 8040, DOI 10.17487/RFC8040, January 2017,
<https://www.rfc-editor.org/info/rfc8040>.
[RFC8199] Bogdanovic, D., Claise, B., and C. Moberg, "YANG Module
Classification", RFC 8199, DOI 10.17487/RFC8199, July
2017, <https://www.rfc-editor.org/info/rfc8199>.
[RFC8294] Liu, X., Qu, Y., Lindem, A., Hopps, C., and L. Berger,
"Common YANG Data Types for the Routing Area", RFC 8294,
DOI 10.17487/RFC8294, December 2017,
<https://www.rfc-editor.org/info/rfc8294>.
[RFC8341] Bierman, A. and M. Bjorklund, "Network Configuration
Access Control Model", STD 91, RFC 8341,
DOI 10.17487/RFC8341, March 2018,
<https://www.rfc-editor.org/info/rfc8341>.
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[RFC8342] Bjorklund, M., Schoenwaelder, J., Shafer, P., Watsen, K.,
and R. Wilton, "Network Management Datastore Architecture
(NMDA)", RFC 8342, DOI 10.17487/RFC8342, March 2018,
<https://www.rfc-editor.org/info/rfc8342>.
[RFC8368] Eckert, T., Ed. and M. Behringer, "Using an Autonomic
Control Plane for Stable Connectivity of Network
Operations, Administration, and Maintenance (OAM)",
RFC 8368, DOI 10.17487/RFC8368, May 2018,
<https://www.rfc-editor.org/info/rfc8368>.
[RFC8639] Voit, E., Clemm, A., Gonzalez Prieto, A., Nilsen-Nygaard,
E., and A. Tripathy, "Subscription to YANG Notifications",
RFC 8639, DOI 10.17487/RFC8639, September 2019,
<https://www.rfc-editor.org/info/rfc8639>.
[RFC8641] Clemm, A. and E. Voit, "Subscription to YANG Notifications
for Datastore Updates", RFC 8641, DOI 10.17487/RFC8641,
September 2019, <https://www.rfc-editor.org/info/rfc8641>.
[RFC8990] Bormann, C., Carpenter, B., Ed., and B. Liu, Ed., "GeneRic
Autonomic Signaling Protocol (GRASP)", RFC 8990,
DOI 10.17487/RFC8990, May 2021,
<https://www.rfc-editor.org/info/rfc8990>.
[RFC8993] Behringer, M., Ed., Carpenter, B., Eckert, T., Ciavaglia,
L., and J. Nobre, "A Reference Model for Autonomic
Networking", RFC 8993, DOI 10.17487/RFC8993, May 2021,
<https://www.rfc-editor.org/info/rfc8993>.
[RFC9171] Burleigh, S., Fall, K., and E. Birrane, III, "Bundle
Protocol Version 7", RFC 9171, DOI 10.17487/RFC9171,
January 2022, <https://www.rfc-editor.org/info/rfc9171>.
[RFC9172] Birrane, III, E. and K. McKeever, "Bundle Protocol
Security (BPSec)", RFC 9172, DOI 10.17487/RFC9172, January
2022, <https://www.rfc-editor.org/info/rfc9172>.
[RFC9254] Veillette, M., Ed., Petrov, I., Ed., Pelov, A., Bormann,
C., and M. Richardson, "Encoding of Data Modeled with YANG
in the Concise Binary Object Representation (CBOR)",
RFC 9254, DOI 10.17487/RFC9254, July 2022,
<https://www.rfc-editor.org/info/rfc9254>.
[xml-infoset]
World Wide Web Consortium, "XML Information Set (Second
Edition)", February 2004,
<https://www.w3.org/TR/2004/REC-xml-infoset-20040204/>.
Birrane, et al. Expires 31 August 2024 [Page 59]
Internet-Draft DTNMA February 2024
[XPath] World Wide Web Consortium, "XML Path Language (XPath)
Version 1.0", November 1999,
<http://www.w3.org/TR/1999/REC-xpath-19991116>.
Acknowledgements
Brian Sipos of the Johns Hopkins University Applied Physics
Laboratory (JHU/APL) provided excellent technical review of the DTNMA
concepts presented in this document and additional information
related to existing network management techniques.
Authors' Addresses
Edward J. Birrane
Johns Hopkins Applied Physics Laboratory
Email: Edward.Birrane@jhuapl.edu
Sarah E. Heiner
Johns Hopkins Applied Physics Laboratory
Email: Sarah.Heiner@jhuapl.edu
Emery Annis
Johns Hopkins Applied Physics Laboratory
Email: Emery.Annis@jhuapl.edu
Birrane, et al. Expires 31 August 2024 [Page 60]
