| Delay-Tolerant Networking | E. Birrane |
| Internet-Draft | Johns Hopkins Applied Physics Laboratory |
| Intended status: Informational | August 20, 2015 |
| Expires: February 21, 2016 |
Asynchronous Management Architecture
draft-birrane-dtn-ama-01
This document describes the motivation, desirable properties, system model, roles/responsibilities, and component models associated with an asynchronous management architecture (AMA) suitable for providing application-level network management services in a challenged networking environment. Challenged networks are those that require fault protection, configuration, and performance reporting while unable to provide human-in-the-loop operations centers with synchronous feedback in the context of administrative sessions. In such a context, networks must exhibit behavior that is both deterministic and autonomous while maintaining compatibility with existing network management protocols and operational concepts.
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This document presents an Asynchronous Management Architecture (The AMA) providing application-layer network management services over links where delivery delays prevent timely communications between a network operator and a managed device. These delays may be caused by long signal propagations or frequent link disruptions (such as described in [RFC4838]) or by non-environmental delay drivers such as unavailability of network operators, administrative delays, or delays caused by quality-of-service prioritizations and service-level agreements.
This document describes the motivation, rationale, desirable properties, and roles/responsibilities associated with an asynchronous management architecture (AMA) suitable for providing network management services in a challenged networking environment. These descriptions should be of sufficient specificity such that an implementing Network Management Protocol (NMP) conformant with this architecture will operate successfully in a challenged networking environment.
An AMA is necessary as the assumptions inherent to the architecture and design of synchronous management tools and techniques fail in challenged network scenarios. Absent an asynchronous management approach, network operators must either adapt to scaling outages of common network management functionality or, more often, must invest time and resources to evolve a challenged network into a well-connected, low-latency network. In some cases such evolution is merely a costly way to over-resource a network. In other cases, such evolution is impossible given physical limitations imposed by signal propagation delays, power, transmission technologies, and other phenomena. The ability to asynchronously manage asynchronous networks enables the large-scale deployment of such networks providing both enhanced technical capabilities and reduced deployment and operations costs. This document presents six sections that, together, describe an AMA suitable for enterprise management of asynchronous networks: motivation, service definitions, desirable properties, roles/responsibilities, system model, and logical component model. The purpose of each section is as follows.
It is assumed that any challenged network where network management would be usefully applied support basic services such as naming, addressing, security, fragmentation, and traditional network/session layer functions. Therefore, these items are not covered in this architectural document.
While likely that a challenged network will interface with a non-challenged network, this architecture does not address the concept of network management compatibility with traditional, non-challenged network management approaches. Implementing NMPs conformant with this architecture should examine compatibility with existing approaches as part of supporting nodes acting as gateways between network types.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in RFC 2119 [RFC2119].
This section identifies those terms critical to understanding the proper operation of the AMA. Whenever possible, these terms align in both word selection and meaning with their analogs from other management protocols.
The characteristics of challenged networks, to include those networks challenged by administrative or policy delays, do not conform to several assumptions made by current network management approaches. These assumptions include high-rate, high-available data, round-trip data exchange, and operator-in-the-loop operation. The inability of current approaches to provide network management services in a challenged network motivate the need for a new network management architecture focused on asynchronous, open-loop, autonomous control of network components.
A growing variety of link-challenged networks support packetization to increase data communications reliability without otherwise guaranteeing a simultaneous end-to-end path. Examples of such networks include Mobile Ad-Hoc Networks (MANets), Vehicular Ad-Hoc Networks (VANets), space-terrestrial internetworks, and heterogeneous networking overlays. Links in such networks are often unavailable due to attenuations, propagation delays, occultation, and other limitations imposed by energy and mass considerations. Data communications in such networks rely on store-and-forward and other queueing strategies to wait for the connectivity necessary to usefully advance a packet along its route.
Similarly, there also exist well-resourced networks that incur high message delivery delays due to non-environmental limitations. For example, networks whose operations centers are understaffed or where data volume and management requirements exceed the real-time cognitive load of operators or the associated operations console software support. Also, networks where policy prevents certain data users from utilizing existing bandwidth also create delayed and disrupted environments that create administratively controlled periods of no communication.
Regardless of the reason, during periods of no communications nodes must rely on fault-management and other autonomous mechanisms to ensure the safe operation of the node and its ability to usefully re-join the network at a later time. In cases of sparsely-populated networks, there may never be a practical concept of "the connected network" as most nodes may be disconnected most of the time. In such environments, defining a network in terms of instantaneous connectivity becomes impractical or impossible.
Specifically, challenged networks exhibit the following properties that may violate assumptions built into current approaches to network management.
Network management in non-challenged networks provides mechanisms for communicating locally-collected data from Agents to associated Managers, typically using a "pull" mechanism where data must be explicitly requested in order to be transmitted.
A near ubiquitous method for management in non-challenged networks today is the Simple Network Management Protocol (SNMP) [RFC3416]. SNMP utilizes a request/response model to set and retrieve data values such as host identifiers, link utilizations, error rates, counters, etc., between application software on Agents and Managers. Data may be directly sampled or consolidated into representative statistics. Additionally, SNMP supports a model for asynchronous notification messages, called traps, based on predefined triggering events. Thus, Managers can query Agents for status information, send new configurations, and be informed when specific events have occurred. Traps and query-able data are defined in one or more Managed Information Bases (MIBs) which define the information for a particular data standard, protocol, device, or application.
In challenged networks, the request/response method of data collection is neither efficient nor, at times, possible as it relies on sessions, round-trip latency, message retransmission, and ordered delivery. Adaptive modifications to SNMP to support challenged networks would alter the basic function of the protocol (data models, control flows, and syntax) so as to be functionally incompatible with existing SNMP installations. While a standard for networking, extending SNMP into this new domain is no more plausible than extending IP routing protocols into this domain.
The Network Configuration Protocol (NETCONF) provides device-level configuration capabilities [RFC6241]. so as to replace vendor-specific command line interface (CLI) configuration software. The XML-based protocol provides a remote procedure call (RPC) syntax such that any exposed functionality on an Agent can be exercised via a software application interface. NETCONF places no specific functional requirements or constraints on the capabilities of the Agent, which makes it a very flexible tool for configuring a homogeneous network of devices. However, NETCONF does place specific constraints on any underlying transport protocol: namely, a long-lived, reliable, low-latency sequenced data delivery session. This is a fundamental requirement given the RPC-nature of the operating concept, and it is unsustainable in a challenged network.
Ultimately, management approaches that rely on timely data exchange, such as those that rely on negotiated sessions or other synchronized acknowledgment, do not function in challenged network environments. Familiar examples of TCP/IP based management via closed-loop, synchronous messaging does not work when network disruptions increase in frequency and severity. While no protocol delivers data in the absence of a networking link, protocols that eliminate or drastically reduce overhead and end-point coordination require smaller transmission windows and continue to function when confronted with scaling delays and disruptions in the network.
Just as the concept of a loosely-confederated set of nodes changes the definition of a network, it also changes the operational concept of what it means to manage a network. When a network stops being a single entity exhibiting a single behavior, "network management" becomes large-scale "node management". Individual nodes must share the burden of implementing desirable behavior without reliance on a single oracle of configuration or other coordinating function such as an operator-in-the-loop.
This section identifies the type of services that must exist between Managers and Agents within an AMA. These services include configuration, reporting, parameterized control, and administration.
Configuration services update local information held by an Agent as it relates to managed applications and protocols. Such information refers to the data necessary to configure behavior in response to state and time changes on these devices. The local information configured through these services includes the data definitions from Application Data Models (ADMs), the specification of parameters associated with these models, and tactical data definitions defined by operators in the network.
New configurations received by a node must be validated to ensure that they do not conflict with other configurations at the node, or prevent the node from effectively working with other nodes in its region. In challenged networks there may not be sufficient time to prevent an erroneous or stale configuration from harming the flow of data through the network.
Examples of configuration service behavior include the following.
Reporting services collect state information from an Agent, such as performance information, and send this information to one or more Managers. The term "reporting" is used in place of the term "monitoring" as challenged networks cannot support closed-loop monitoring. Reports received by an Agent provide best-effort information to Managers.
Since a Manager is not actively "monitoring" an Agent, the Agent must make its own determination on when to send what reports based on its own local time and state information. Agents should produce reports of varying fidelity and with varying frequency based on thresholds and other information set as part of configuration services.
Examples of reporting service behavior include the following.
Control services provide mechanisms for an Agent to change its behavior using pre-defined, pre-configured responses from a Manager. By setting autonomous actions on Agents, Managers can "manage" the node asynchronously during periods of no communication. Agents must understand a finite set of pre-programmed functions related to the protocols and applications managed on the device. As such, controls comprise the basic autonomy mechanism within the AMA.
Similar to reporting services, controls are run based on the Agent's notion of time and state in accordance with directives provided by configuration services.
Examples of potential control service behavior include the following.
Administration services enforce the potentially complex mapping of configuration, reporting, and control services amongst Agents and Managers in the network. Fine-grained access control specifying which Managers may apply which services to which Agents may be necessary in networks dealing with multiple administrative entities or overlay networks crossing multiple administrative boundaries. Whitelists, blacklists, shared keys, PKI, or other schemes may be used for this purpose.
Examples of administration service behavior include the following.
As discussed, realizing necessary service definitions given the characteristics of challenged networks cannot be performed using current network management approaches and operational concepts. This section describes those desirable properties of an AMA that enable the implementation of service definitions in such networks. These properties include open-loop, intelligent push, asynchronous mechanisms that can scale as message delivery delays scale. Ultimately, a useful AMA MUST be built around the following five design principles.
Pull management mechanisms require that a Manager send a query to an Agent and then wait for the response to that query. This practice both implies a control-session between entities and increases the overall message traffic in the network. Challenged networks cannot guarantee timely roundtrip data-exchange and, in extreme cases, are comprised solely of uni-directional links. Therefore, pull mechanisms must be avoided in favor of push mechanisms.
Push mechanisms, in this context, refer to Agents making their own determinations relating to the information that should be sent to Managers. Such mechanisms do not require round-trip communications as Managers do not request each reporting instance; Managers need only request once, in advance, that information be produced in accordance with a pre-determined schedule or in response to a pre-defined state on the Agent. In this was information is "pushed" from Agents to Managers and the push is "intelligent" because it is based on some internal evaluation performed by the Agent.
Protocol designers must balance message size versus message processing time at sending and receiving nodes. Verbose representations of data simplify node processing whereas compact representations require additional activities to generate/parse the compacted message. There is no asynchronous management advantage to minimizing node processing time in a challenged network. However, there is a significant advantage to smaller message sizes in such networks. Compact messages require smaller periods of viable transmission for communication, incur less re-transmission cost, and consume less resources when persistently stored en-route in the network. AMPs should minimize PDUs whenever practical, to include packing and unpacking binary data, variable-length fields, and pre-configured data definitions.
Elements within the management system must be uniquely identifiable so that they can be individually manipulated. Identification schemes that are relative to system configuration make data exchange between Agents and Managers difficult as system configurations may change faster than nodes can communicate. For example, SNMP-managed systems often approximate associative array lookups by (1) querying a list of known array keys, (2) making a key-index map, and (3) then querying a specific index into the array based on that map. Ignoring the inefficiency of two pull requests, this mechanism fails when the Agent changes its key-index mapping between the first and second query. AMPs must find a way to uniquely identify such data that does not rely on system configuration, perhaps through parameterization of the initial query.
Tactical 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. Specifically, an Agent should not be required to transmit a large data set for a Manager that only wishes to calculate a smaller, inferred data set. The Agent should calculate the smaller data set on its own and transmit that instead. Since the identification of these smaller data sets is likely both tactical and in the context of a specific network deployment, AMPs must provide a mechanism for their definition.
AMA network functions must be achievable using only knowledge local to the Agent. Performance data production, reconfiguration, and other activity must be autonomously evaluated and implemented by the impacted node. Managers, rather than directing an Agent, configure the autonomy engine of the Agent to take its own action under the appropriate conditions in accordance with the Agent's notion of local state and time.
By definition, Agents reside on managed devices and Managers reside on managing devices. This section describes how these roles participate in the network management functions outlined in the prior section.
This section describes the notional data flows and control flows that illustrate how Managers and Agents within an AMA cooperate to perform network management services.
The AMA identifies three significant data flows: control flows from Managers to Agents, reports flows from Agents to Managers, and fusion reports from Managers to other Managers. These data flows are illustrated in Figure 1.
AMA Data Flows
+---------+ +------------------------+ +---------+
| Node A | | Node B | | Node C |
| | | | | |
|+-------+| |+-------+ +-------+| |+-------+|
|| ||=====>>||Manager|====>>| ||====>>|| ||
|| ||<<=====|| B |<<====|Agent B||<<====|| ||
|| || |+--++---+ +-------+| ||Manager||
|| Agent || +---||-------------------+ || C ||
|| A || || || ||
|| ||<<=========||==========================|| ||
|| ||===========++========================>>|| ||
|+-------+| |+-------+|
+---------+ +---------+
Figure 1
In this data flow, the Agent on node A receives configurations from Managers on nodes B and C, and replies with reports back to these Managers. Similarly, the Agent on node B interacts with the local Manager on node B and the remote Manager on node C. Finally, the Manager on node B may fuse data reports received from Agents at nodes A and B and send these fused reports back to the Manager on node C.
From this figure it is clear that there exist many-to-many relationships amongst Managers, amongst Agents, and between Agents and Managers. Note that Agents and Managers are roles, not necessarily differing software applications. Node A may represent a single software application fulfilling only the Agent role, whereas node B may have a single software application fulfilling both the Agent and Manager roles. The specifics of how these roles are realized is an implementation matter.
This section describes three common configurations of Agents and Managers and the flow of messages between them. These configurations involve local and remote management and data fusion.
The notation outlined in Table 1 describes the types of control messages exchanged between Agents and Managers.
| Term | Definition | Example |
|---|---|---|
| AD# | Atomic data definition, from ADM. | AD1 |
| CD# | Custom data definition. | CD1 = AD1 + CD0. |
| DEF([ACL], ID,EXPR) | Define id from expression. Allow managers in access control list (ACL) to request this id. | DEF([*], CD1, AD1 + AD2) |
| PROD(P,ID) | Produce ID according to predicate P. P may be a time period (1s) or an expression (AD1 > 10). | PROD(1s, AD1) |
| RPT(ID) | A report identified by ID. | RPT(AD1) |
This is a nominal configuration of network management where a Manager interacts with a set of Agents. The control flows for this are outlined in Figure 2.
Serialized Management Control Flow
+----------+ +---------+ +---------+
| Manager | | Agent A | | Agent B |
+----+-----+ +----+----+ +----+----+
| | |
|-----PROD(1s, AD1)---->| |(Step 1)
|----------------------------PROD(1s, AD1)--->|
| | |
| | |
|<-------RPT(AD1)-------| |(Step 2)
|<-----------------------------RPT(AD1)-------|
| | |
| | |
|<-------RPT(AD1)-------| |
|<-----------------------------RPT(AD1)-------|
| | |
| | |
|<-------RPT(AD1)-------| |
|<-----------------------------RPT(AD1)-------|
| | |
In a simple network, a Manager interacts with multiple Agents.
Figure 2
In this figure, the Manager configures Agents A and B to produce atomic data AD1 every second in (Step 1). At some point in the future, upon receiving and configuring this message, Agents A and B then build a report containing AD1 and send those reports back to the Manager in (Step 2).
Networks spanning multiple administrative domains may require multiple Managers (for example, one per domain). When a Manager defines custom reports/data to an Agent, that definition may be tagged with an access control list (ACL) to limit what other managers will be privy to this information. Managers in such networks SHOULD synchronize with those other Managers granted access to their custom data definitions. When Agents generate messages, they MUST only send messages to Managers according to these ACLs, if present. The control flows in this scenario are outlined in Figure 3.
Multiplexed Management Control Flow
+-----------+ +-------+ +-----------+
| Manager A | | Agent | | Manager B |
+-----+-----+ +---+---+ +-----+-----+
| | |
|--DEF(A,CD1,AD1*2)--->|<--DEF(B, CD2, AD2*2)-|(Step 1)
| | |
|---PROD(1s, CD1)----->|<---PROD(1s, CD2)-----|(Step 2)
| | |
|<-------RPT(CD1)------| |(Step 3)
| |--------RPT(CD2)----->|
|<-------RPT(CD1)------| |
| |--------RPT(CD2)----->|
| | |
| |<---PROD(1s, CD1)-----|(Step 4)
| | |
| |--ERR(CD1 no perm.)-->|
| | |
|--DEF(*,CD3,AD3*3)--->| |(Step 5)
| | |
|---PROD(1s, CD3)----->| |(Step 6)
| | |
| |<---PROD(1s, CD3)-----|
| | |
|<-------RPT(CD3)------|--------RPT(CD3)----->|(Step 7)
|<-------RPT(CD1)------| |
| |--------RPT(CD2)----->|
|<-------RPT(CD3)------|--------RPT(CD3)----->|
|<-------RPT(CD1)------| |
| |--------RPT(CD2)----->|
Complex networks require multiple Managers interfacing with Agents.
Figure 3
In more complex networks, Managers may choose to define custom reports and data definitions, and Agents may need to accept such definitions from multiple Managers. Custom data definitions may include an ACL that describes who may query and otherwise understand the custom definition. In (Step 1), Manager A defines CD1 only for A while Manager B defines CD2 only for B. Managers may, then, request the production of reports containing these custom definitions, as shown in (Step 2). Agents produce different data for different Managers in accordance with configured production rules, as shown in (Step 3). If a Manager requests an operation, such as a production rule, for a custom data definition for which the Manager has no permissions, a response consistent with the configured logging policy on the Agent should be implemented, as shown in (Step 4). Alternatively, as shown in (Step 5), a Manager may define custom data with no restrictions allowing all other Managers to request and use this definition. This allows all Managers to request the production of reports containing this definition, shown in (Step 6) and have all Managers receive this and other data going forward, as shown in (Step 7).
In some networks, Agents do not individually transmit their data to a Manager, preferring instead to fuse reporting data with local nodes prior to transmission. This approach reduces the number and size of messages in the network and reduces overall transmission energy expenditure. DTNMP supports fusion of NM reports by co-locating Agents and Managers on nodes and offloading fusion activities to the Manager. This process is illustrated in Figure 4.
Data Fusion Control Flow
+-----------+ +-----------+ +---------+ +---------+
| Manager A | | Manager B | | Agent B | | Agent C |
+---+-------+ +-----+-----+ +----+----+ +----+----+
| | | |
|--DEF(A,CD0,AD1+AD2)->| | |(Step 1)
|--PROD(AD1&AD2, CD0)->| | |
| | | |
| |--PROD(1s,AD1)-->| |(Step 2)
| |-------------------PROD(1s, AD2)->|
| | | |
| |<---RPT(AD1)-----| |(Step 3)
| |<-------------------RPT(AD2)------|
| | | |
|<-----RPT(A,CD0)------| | |(Step 4)
| | | |
Data fusion occurs amongst Managers in the network.
Figure 4
In this example, Manager A requires the production of a computed data set, CD0, from node B, as shown in (Step 1). The manager role understands what data is available from what agents in the subnetwork local to B, understanding that AD1 is available locally and AD2 is available remotely. Production messages are produced in (Step 2) and data collected in (Step 3). This allows the manager at node B to fuse the collected data reports into CD0 and return it in (Step 4). While a trivial example, the mechanism of associating fusion with the manager function rather than the agent function scales with fusion complexity, though it is important to reiterate that agent and manager designations are roles, not individual software components. There may be a single software application running on node B implementing both Manager B and Agent B roles.
This section enumerates the different kinds of information present in an asynchronously-managed network and describes how this information should be communicated in the context of an ADM.
The AMA notionally supports four basic types of information: Data, Controls, Literals, and Operators:
The AMA notionally defines three data classifications that describe the origins and multiplicity of data types within the system. These classifications are atomic, computed, and collection.
Each component of the DTNMP data model can be identified as a combination of type and category, as illustrated in Table 2. In this table type/category combinations that are unsupported are listed as N/A. Specifically, DTNMP does not support user-defined controls, constants, or operations; any value that specifies action on an agent MUST be pre-configured as part of an ADM.
| Data | Action | Literals | Operator | |
|---|---|---|---|---|
| Atomic | Primitive Value | Control | Literal | Operator |
| Computed | Computed Value | Rule | N/A | N/A |
| Collection | Report | Macro | N/A | N/A |
The eight elements of the AMA logical data model are described as follows.
Fundamental to any performance reporting function is the ability to measure the state of value on the Agent. Measurement may be accomplished through direct sampling of hardware, query against in-situ data stores, or other mechanisms that provide the initial quantification of state.
Primitive values serve as the "lingua franca" of the management system: the unit of information that cannot be otherwise created. As such, this information serves as the basis for any user-defined (computed) values in the system.
AMPs MAY consider the concept of the confidence of the primitive value as a function of time. For example, to understand at which point a measurement should be considered stale and need to be re-measured before acting on the associated data. For example, one approach to mitigate this concern is to measure values on-demand. Another approach is to populate a centralized data store of values and refresh that data store according to some pre-defined period.
While primitives provide the full, raw set of information available to Managers and Agents there is a performance optimization to pre-computed re-used combinations of these values. Computing new values as a function of measured values simplifies operator specifications and prevents Agent implementations from continuously re-calculating the same value each time it is used in a given time period.
For example, consider a sensor node which wishes to report a temperature averaged over the past 10 measurements. An Agent may either transmit all 10 measurements to a Manager, or calculate locally the average measurement and transmit the "fused" data. Clearly, the decision to reduce data volume is highly coupled to the nature of the science and the resources of the network. For this reason, the ability to define custom computations per deployment is necessary.
Periodically, or in accordance with local state changes, Agents must collect a series of measured values and computed values and communicate them back to Managers. This ordered collection of value information is noted in this architecture as a "report". In support of hierarchical definitions, reports may, themselves, contain other reports. It would be incumbent on an AMP implementation to guard against circular reference in report definitions.
Just as traditional network management approaches provide well-known identifiers for values, the AMA must provide a similar service for taking action on a node. Whereas several low-latency, high-availability approaches in networks can use approaches such as remote procedure calls (RPCs), challenged networks cannot provide a similar function - Managers cannot be in the processing loop of an Agent when the Agent is not in communication with the Manager.
Controls in a system are the combination of a well-known operation that can be taken by an Agent as well as any parameters that are necessary for the proper execution of that function. For specific applications or protocols a control specification (as a series of opcodes) can be published such that any implementing AMP accepts these opcodes and understands that sending the opcodes to an Agent supporting the application or protocol will properly execute the associated function. Parameters to such functions are provided in real-time by either Managers requesting that a control be run, pre-configured, or auto-populated by the Agent in-situ.
Often, a series of controls must be executed in concert to achieve a particular function, especially when controls represent more primitive operations for a particular application/protocol. In such scenarios, an ordered collection of controls can be specified as a "macro". In support of the hierarchical build-up of functionality, macros may, themselves, contain other macros, through it would be incumbent on an AMP implementation to guard against excessive recursion or other resource-intensive nesting.
Stimulus-response autonomy systems provide a way to pre-configure responses to anticipated events. Such a mapping from responses to events is advantageous in a challenged network for a variety of reasons, as listed below.
The logical unit of stimulus-response autonomy proposed in the AMA is a "rule" of the form:
IF stimulus THEN response
Where the set of such rules, when evaluated in some prioritized sequence, provides the full set of autonomous behavior for an Agent. Stimulus in such a system would either be a function of relative time, absolute time, or some mathematical expression comprising one or more values (measurement values or computed values).
Notably, in such a system, stimuli and responses from multiple applications and protocols may be combined to provide an expressive capability.
The act of computing values or evaluating the expressions that comprise a stimulus in a rule both require applying mathematical operations to data known to the management system.
Operators in the AMA represent enumerated mathematical operations applied to primitive and computed data in the AMA for the purpose of creating new values. Operations may be simple binary operations such as "A + B" or more complex functions such as sin(A) or avg(A,B,C,D).
Literals represent pre-configured constants in the AMA, such as well-known mathematical numbers (e.g., PI, E), or other useful data such as Epoch times. Literals also represent asserted primitive values used in expressions. For example, considering the expression (A = B + 10), A would be a computed value, B would be either computed value or a primitive value, + would be an operator, and 10 would be a literal.
Application data models (ADMs) specify the data associated with a particular application/protocol. The purpose of the ADM is to provide a guaranteed interface for the management of an application or protocol independent of the nuances of its software implementation. In this respect, the ADM is conceptually similar to the Managed Information Base (MIB) used by SNMP, but contains additional information relating to command opcodes and more expressive syntax for automated behavior.
Within the AMA, an ADM MUST define all well-known items necessary to manage the specific application or protocol. This includes the definitions of primitive values, measured values, reports, controls, macros, rules, literals, and operators.
At this time, this protocol has no fields registered by IANA.
Security within an AMA MUST exist in two layers: transport layer security and access control.
Transport-layer security addresses the questions of authentication, integrity, and confidentiality associated with the transport of messages between and amongst Managers and Agents in the AMA. This security is applied before any particular Actor in the system receives data and, therefore, is outside of the scope of this document.
Finer grain application security is done via ACLs which are defined via configuration messages and implementation specific.