Internet DRAFT - draft-t2trg-iot-workspaces
draft-t2trg-iot-workspaces
Thing-to-Thing Research Group M. Burgess
Internet-Draft Independent Researcher
Intended status: Informational H. Wildfeuer
Expires: May 21, 2017 Cisco
November 17, 2016
Federated Multi-Tenant Service Architecture for an Internet of Things
draft-t2trg-iot-workspaces-00
Abstract
This draft describes architectural recommendations for a unified
concept of Cloud Computing and Internet of Things, based on tried and
tested principles from infrastructure science. We describe a
functional service architecture that may be applied in the manner of
a platform, from the smallest scale to the largest scale, using
vendor agnostic principles. The current draft is rooted in the
principles of Promise Theory[Bergstra1] and voluntary cooperation.
Status of This Memo
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This Internet-Draft will expire on May 21, 2017.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Requirements and Promises Language . . . . . . . . . . . . . 3
3. Definitions and concepts . . . . . . . . . . . . . . . . . . 3
4. Device interconnection . . . . . . . . . . . . . . . . . . . 4
5. Federation of agency . . . . . . . . . . . . . . . . . . . . 6
5.1. Ownership . . . . . . . . . . . . . . . . . . . . . . . . 7
5.2. Tenancy and separation of concerns . . . . . . . . . . . 7
5.3. Proximity of services to location-sensitive Things . . . 8
6. The concept of workspaces . . . . . . . . . . . . . . . . . . 8
6.1. Workspaces and namespaces . . . . . . . . . . . . . . . . 8
6.2. Workspaces promises . . . . . . . . . . . . . . . . . . . 9
6.3. Workspaces as resource clusters . . . . . . . . . . . . . 9
6.4. Workspace maintenance . . . . . . . . . . . . . . . . . . 10
7. Generic Promise-Oriented Architecture . . . . . . . . . . . . 11
7.1. Control . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.2. Services . . . . . . . . . . . . . . . . . . . . . . . . 12
7.3. Promises . . . . . . . . . . . . . . . . . . . . . . . . 13
7.4. Agents and their promises . . . . . . . . . . . . . . . . 13
7.5. Standard promises . . . . . . . . . . . . . . . . . . . . 14
7.6. Contextual policy-based adaptation . . . . . . . . . . . 14
7.7. Change of policy (system intent) . . . . . . . . . . . . 15
7.8. Separation of concerns versus timescales . . . . . . . . 15
7.9. Device roles per workspace or region . . . . . . . . . . 16
7.10. Connectivity and Network Policy . . . . . . . . . . . . . 17
8. Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
9. Summary and Outlook . . . . . . . . . . . . . . . . . . . . . 19
10. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 19
11. Security Considerations . . . . . . . . . . . . . . . . . . . 19
12. Normative References . . . . . . . . . . . . . . . . . . . . 20
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 21
1. Introduction
The scenario we call the Internet of Things (IoT) is an inflection
point in the development of information local and global
infrastructure. We know cloud computing as a commoditization of
primary infrastructure resources (also `things') for flexible
datacentre hosting. The facilitation of a common platform for the
next generation of global commerce presents a challenge of both
technological and human dimensions. Not only do we have to solve the
matter of technology at scale, we must also solve the matter of human
dignity and participation. This is a challenge that spans every
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layer of the software and networking stacks, yet can be described in
general terms without the need to specific implementations. That is
our goal in this (revised) draft. Only a few new ideas are needed to
synthesize this infrastructure, however several old technology
practices must be deprecated for scaling and security considerations.
A platform for society as a whole must be vendor agnostic at its
root, and must leave ample space for vendor specific creativity on
top. What distinguishes IoT from past scenarios is the prolific
contact surface it will expose to the physical world, embedding
devices pervasively in our close environments, and touching every
part of human life. At the time of writing, IoT has barely begun to
emerge in domestic and industrial settings; however, choices we make
now could help or hinder the development of an adequate platform over
the coming decades. The proposed architecture not only scales up to
large numbers, it also scales down to small devices of low
capability; from the largest installations to the smallest, and from
the tiniest amounts of data, to vast data-stores collected by
scientific computing at the limits of possibility.
2. Requirements and Promises Language
The term "PROMISE", "PROMISES" in this document are to be interpreted
as described in Promise Theory [Bergstra1]
When used, 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].
3. Definitions and concepts
IP endpoint A hardware or software agent that is IP addressable, via
a TCP/IP capable interface.
Static endpoint A hardware or software agent with an IP address
(prefix and subnet) that is fixed over the timescale of
application service interactions.
Mobile endpoint A hardware or software agent whose IP address
location can change on the timescale of application service
interactions.
Application server/service Any agent that promises to respond to
requests, from external parties, and perform services of
any kind, on a timescale that we may call the application
service timescale.
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Multi-tenant application service A collection of agents housed as
tenants within a single host device, each offering
different services, with potentially different timescales.
Client application An agent that consumes data from an application
service, requested either by imposed query or by promised
schedule.
Application Programmer Interface (API) A set of interfaces that
expose remote agents to accessing and overriding of the
local autonomy of agents.
Standalone Thing (FFD) A full function device (FFD)[OneM2M], with an
IP address, that can present its own service gateway or
interface to the IP network.
Peripheral Thing (RFD) A reduced function device (RFD)[OneM2M], with
no IP address, that attaches to a host gateway device as a
peripheral, over an arbitrary network (USB, PCIe, CANbus,
Profibus, ModBus, wireless sensor network, etc). Devices
are addressable, only through the gateway service. This
includes portmapped devices.
Embedded network Any network (IP or non-IP) that is non-IP routed,
i.e. contained within a host endpoint as part of a black
box, e.g. isolated NAT, device bus, serial channels.
Transducer An agent that consumes a service from another agent, and
provides a new service based on the consumed service, e.g.
a router, encrypter, compressor, etc.
Trust A unilateral policy assessment of one agent by another,
concerning its reliability in honouring promises. Trust is
not necessarily a transitive property.
Partial connectivity A device is said to have partial connectivity
if it is unavailable for intervals of time, e.g. due to
loss of connectivity, mobility, or power napping.
4. Device interconnection
A platform that brings computing closer to users, away from
specialized datacentres, must be based on plausible assumptions. We
assume that devices live in a partially connected environment, of
limited reliability: they MUST be fault tolerant to loss of
communications, both with other devices, in the course of providing
application services, and with trusted sources of information. The
minimum level of interdependency is recommended to facilitate this.
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For a nascent Internet of Things, our focus is naturally drawn to the
specialized leaf devices, where data may be produced or consumed. It
will take many years to commoditize these sensors and actuators, and
their local communication architectures. However, these are only
half the picture. `Thing' devices, by design, also communicate with
online services deployed `higher up', or `Northbound' in the system,
to offload analysis and decision-making. Their physical capabilities
thus place them into two broad categories:
Standalone devices These are assumed to connect by an IP (or layer
3) addressable underlay network. Connectivity is assumed
end-to-end, without reference to tunnels or software-
defined overlays. Policy-based routing is assumed to be
provided end-to-end, and fully decoupled from the hardware
and software running on devices. Routing paths may be
managed through a namespace registration abstraction which
we call workspaces. Segregation and firewalling of certain
network regions may be included as part of external network
governance design, but will not be considered here.
Peripherals These include bare sensors and actuators, which do not
possess sufficient onboard resources or software
interfaces, may attach to hosting standalone devices that
act as a gateway and IP (or layer 3) endpoint on their
behalf.
Transducers These pass-through devices transformers, converters,
encapsulation services, etc
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+------------------+
| FFD / Standalone |--> IP Endpoint
+------------------+
+------------------+
| RFD / Peripheral |--+
+------------------+ | +------------------+
+------| FFD / Standalone |--> IP or L3 endpoint
+------------------+ | +------------------+
| RFD / Peripheral |--+
+------------------+
Devices may be standalone (FFD), with service interfaces, or hosted
peripherals (RFD), where data are exposed through service interfaces
from other buses, e.g. USB, CANbus, MODbus, Profibus, etc.
Figure 1
Standalone devices are full stack devices that provide data oriented
services to data clients
Stand-alone devices and transducers can vary considerably in their
processing, memory, and connectivity constraints. This architecture
assumes a minimum resource level at the stand-alone device, but the
device must support `full stack' implementations. In practice, this
implies that they contain an embedded OS (e.g. Linux), and are
capable of running an agent providing secure service and connectivity
interfaces.
5. Federation of agency
Centralization of intent is a natural form of coordination. However,
centralization of command and control (e.g. by API) is not practical
in environments where the density of devices and overlapping concerns
reaches the level of a pervasive Internet of Things. Legacy
technology is pervasively centralized and top-down in nature:
requests for domain names and name services, IP address assignment,
change management of information records, cloud controllers, service
entry points, etc. The barriers to scalable autonomous activity are
high. The trend has been to delegate these activities to sub-
authorities (multiple queues) to limit scaling bottlenecks, which
improves queue latency but not minimum transaction time. In the
future, resource management can profit by propagating intentions and
desires from the bottom-up, i.e. from the many points of service
consumption, with localization to minimize queueing and flooding
contention of the independent needs.
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5.1. Ownership
Infrastructure ownership is an important issue in a multi-tenant
consumer environment. While some devices can be centrally managed by
providers (regardless of owner), many devices in an Internet of
Things will be owned by private individuals who will permit
management by centralized services. Devices may be managed by:
Their owners This applies in particular to personal consumer
electronics, phones, cars, domestic appliances, etc, where
users need to retain trusted ownership of their personal
belongings.
A service provider This applies to managed services, factory
machinery, fleet vehicles, set-top boxes placed in the
home, power controllers, etc, where users do not need to
interact with the devices on a management level, but there
is an advantage to placing a device as a local presence in
a smart environment.
5.2. Tenancy and separation of concerns
Federation of intent, aka multi-tenancy or diversity, all point to
the need for Special Interest Groups (SIG) or workgroups, who
specialize within organizations to develop expertise. Software
architectures following this pattern are sometimes called
microservice architectures. We shall introduce the notion of
`workspaces' as a federated infrastructure abstraction designed to
wrap one or more of these specialized services under an umbrella
abstraction that is easy to understand and work with. A goal of the
workspace is to expose only the working parts that need to interact
with consumers, e.g. in the same way that one does not expose the
inner components of a television or a car.
Federation is desirable along a number of lines:
o Geographic partitioning (location)
o Separation of timescales (fast and slow)
o By special interest group (functional)
See sections below for further information.
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5.3. Proximity of services to location-sensitive Things
Although user-facing devices, deployed in the field, may be separate
from the agencies processing their sensory data, or feeding them
guidance (e.g. as policies), it becomes increasingly impractical to
transport data over long distances between leaf devices and `cloud'
services as the density of deployed devices grows. The logical
outcome is therefore a decentralization of the processing cloud
itself, so as to bring all necessary resources close to the field-
deployed data sources themselves. To scale such a distribution, the
data services will naturally associate with private workspaces, which
bound the scope of data generated by Things.
6. The concept of workspaces
6.1. Workspaces and namespaces
Workspaces may be thought of as a modernization and generalization of
the familiar network domain concept. Workspaces go beyond
namespacing, to include federation, collaboration, and segmentation
of services. Currently, name domains are typically linked to simple
directory services (DNS, Active Directory, LDAP etc) for name-address
mapping. These are assigned from some top-down agency, either within
an organization or even beyond it, at a regional level. The demands
of multi-tenant environments, where shared resources and separate
business-processes mix and compete, make these older services less
than optimal, though not inherently flawed. It is awkward to
separate independent collaborative activities and then manage their
interactions on a need-to-know/need-to-do basis, without involving
multiple human interventions. Cloud APIs bring some improvement, by
exposing arbitrary capabilities to remote operation, but the
remoteness also brings risks and inefficiencies by exposing an attack
surface, and from lack of situational awareness of actual state.
Workspaces are related to the more familiar notion of namespaces in
information technology; however, namespaces refer mainly to priority
name-referencing of objects, without necessitating underlying
resource access segmentation. Workspaces MUST support multi-tenant
separation of concerns within a hosted hardware resource space.
Today, workspace-like facilities are commonly offered as user logins
on computer operating systems or online services, and quasi-
workspace-like facilities are offered by virtual private networks,
and VLANs, etc, in networking. However, resource management
platforms do not yet bring the same level of flexibility to
infrastructure. Typically, resources are managed by top-down
agencies who design and grant resource usage manually, leading to the
insertion of a human processing timescale into the flow of
automation.
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6.2. Workspaces promises
Workspaces need to be able to promise segmentation and privacy. This
involves some basic capabilities:
1. Authorization and user identification for resource access
control, within each workspace.
2. Multiple compatible user identities in case of a member node
belonging to several workspaces.
3. Resource separation that is respected by all levels of the
software interactions. This requires the support of operating
systems. i.e. segmentation at the network, application, and
operating system level. For example, Linux containers, systemd
containment, network virtualization, etc.
4. Service discovery, and registration of capabilities.
5. Resource mobility and availability tracking.
6. Scaling of information on a need-to-know/need-to-propagate basis,
avoiding costly data consistency protocols that broadcast data
updates, and lead to waiting.
6.3. Workspaces as resource clusters
At the time of this revision, many of the properties of workspaces
are being explored under the aegis of cloud application cluster
managers. Google's Kubernetes [K8S], for example, is presently the
most ambitious of these. It is plausible to imagine extending such a
system to cover the workspace proposal for both cloud and IoT
scenarios.
For a collaborative Internet of Things, where interests span many
issues from manufacturer interests, to personal ownership, service
provider concerns, functional responsibility, and security, etc, the
technologies for inter-group collaboration need to be modernized to
support logical segmentation, authenticated access, instrumented
delegation, shared name-service information, as well as private
naming, all across a converged palette of resources: compute,
network, storage and sensor-actuators. This is somewhat reminiscent,
but not identical, of the goals of Named Data Networking (NDN) [NDN],
which promotes the semantics of space above the details of its
interconnectivity.
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1. Workspaces imply scope; i.e. they may or may not be private, but
they must be self-contained and separable, in the manner of
namespaces.
2. Workspaces may or may not be associated with multiple tenants;
but they are associated with multiple functional or
organizational issues.
3. Workspaces represent a context for human activity, respecting
separation of concerns. We need not prejudge what aspects of
future activity need to embed software-based technologies, but it
seems safe to assume no limits. Example workspaces might
include: online stores and services, offices, the home, a
children's playground, a squash court, a shop, a factory floor,
buildings, districts, cities, an emergency communications
channel, a subsystem of hot and cold water pipes, a hotel dining
room, a personal drinks cabinet, etc.
Ubiquitous computing (the Internet of Things) is all about how
networked devices support a wider variety of workspaces than
industrial scale central services. As the density of device
resources (compute, storage, sensors, actuators) in a workplace or
home environment increases, isolation of regions, and mapping of
resources to responsible or interested parties become more difficult
problems, both to implement and to understand.
A detailed description of workspaces will be given separately
[WORKSPC].
6.4. Workspace maintenance
The following characteristics describe compatible policy update
processes:
1. Devices subscribe to policy from a trusted workspace source,
download changes to the policy model when they can, and cache it
locally so that it is always available.
2. Local agents implement cached policy, without any dependence on
remote communication, and in a fault tolerant fashion. The
failure to keep one promise should have minimal impact on the
ability to keep others.
3. By verifying promises continuously, the agent that runs on each
standalone device will know (or be able to calculate) its
operational context, and can decide which promises are needed
from the policy model, and whether or not to keep the promises.
This scales O(1), i.e. without bottleneck.
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4. Each promise that documents and intended outcome of the system is
verified and measured in the process, providing immediate and
statistical feedback to policy designers about the success of the
policy in describing a stable desired outcome.
7. Generic Promise-Oriented Architecture
The properties we are looking for in workspaces, suggest an
architecture based on the principles of promise theory; such a
promise-oriented architecture is described implicitly in [DSOM2005]
and [Bergstra1]. It lays out a generic `bottom up' management
concept, in which devices each have the responsibility for their own
state and roles. It resembles Service Oriented Architecture (SOA)
superficially, without reference to specific technologies,
implementations or protocols, and relates to the modern notion of
microservices [MicroS]
By formulating architecture from the bottom up, one can easily
account for multi-contextual concerns, from developer concerns about
realtime software updates (Continuous Delivery and DevOps etc), to
operational service scaling, governance, and security, in a way that
top-down schemes cannot easily achieve.
The relationship between a generic promise-oriented architecture and
the concept of a workspace is that the former provides a necessary
and sufficient basis for implementing the latter. Workspaces are
expected to be a friendly interface to the underlying promise
architecture, separating interior and exterior promises cleanly and
intuitively.
7.1. Control
A promise-oriented architecture communicates (e.g. intent and data)
by authenticated publish-subscribe (aka "pull") methods, for security
and predictability. In a workspace, devices MUST not accept control
commands imposed upon them by remote "push" methods, as this exposes
a security risk and may lead to inconclusive results during
uncoordinated pushes (multithreaded access). In the vernacular usage
of "control plane" and "data plane", control is asserted through
agreed service level policies, and data are exchanged within services
to carry out functions.
Every standalone device operates autonomously, with policy guidance
from its owner, without direct external intervention. To form a
workspace, any standalone device can give up that autonomy to a
trusted manager, offering policy updates as a service. Workspaces
separate interior and exterior promises about resources and their
accessibility: they are a priori opaque from the exterior, and
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transparent from the interior. By joining a workspace, any device
(whether a cloud server or an IoT embedded FFD, subordinates itself
to a bounded policy domain with a private namespace. Policy
determines whether a given member of the workspace will expose public
service entry points, or will be entirely anonymous to exterior
agents. This is very close to the community namespace idea currently
being implemented in the Kubernetes cloud cluster manager [K8S]. One
can imagine a meta-cluster of such cloud clusters, which are designed
to tolerate partial network reliability, as forming a basis for the
Internet of Things alongside more traditional high reliability
datacentre environments. Currently, cluster managers assume top-down
ownership, rather than autonomous self-management, and expose more
complexity through APIs than is appropriate for average engineers.
+--------------------------+-----------------------+
| Workspaces | Cloud clusters |
+--------------------------+-----------------------+
| Bootstrap server | Cluster master node |
| Standalone device | Application Pod |
| Intermittent network | Reliable network |
| Network by directory | Network by overlay |
| Converged infrastructure | Siloed infrastructure |
| Bottom up | Top down |
| Unreliable network | Reliable network |
| Node members anywhere | Nodes in datacentre |
| Master policy source | Master controller |
| Need to know | Full consistency |
+--------------------------+-----------------------+
Rough correspondence between contemporary cloud clusters and proposed
workspace concept: workspaces are principally a bottom-up, self-
service collaboration over multiple clusters with more diverse
hardware and software.
Figure 2
7.2. Services
All devices provide services with varying degrees of sophistication.
Peripheral devices serve data or actuators to host devices, and
standalone devices expose functions to one another as software
services. Each server plays a role to be composed into the wider
system.
Services may be used both for basic infrastructure support, and for
driving user applications. No limitations need be stated about
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applications. Each fully functional, standalone device is free to
host any application services. The result is superficially similar
to the Service Oriented Architecture [SOA], but without reference to
a specific technology or methodology. In modern parlance, the model
is an example of microservices [MicroS].
Data collection services are also best implemented with pull methods,
for resource-light scalability and security. However, extremely
limited application devices might initially struggle to support this
mode of operation.
Service scaling is a task for workspaces. Public (exterior) services
can be provided in a standardized manner, through accessible points
of entry, whose name information is propagated publicly, analogous to
a DNS directory. Workspaces can hide internals (e.g. vendor or
implementation specific details, private internal services, load
sharing parallelism.
Interior name services deal with the registration and propagation of
information between workspace members, on a need-to-know basis. This
is never visible to the exterior network.
7.3. Promises
The basic atom of bottom-up policy is a promise. Each promise
consists of three things:
A `promiser' i.e. a resource that will affect a change by keeping
its promise to the system, e.g. a file, a process, a
transaction, a measurement, device settings, etc.
A description body i.e. the desired-outcome that is achieved when
the promise is kept. This SHOULD be implemented in a
convergent, idempotent manner, (see [CFENGINE],
[CONVERGE]).
A context in which the promise applies, based on time, location, type
and group membership of the devices referred to in the
model.
7.4. Agents and their promises
In a promise architecture, every device is contextually evaluated and
integrated from the bottom up, according to the promises is keeps,
e.g. the services it provides, its behaviours and properties, etc.
Thus every device is modelled by its individual degree of agency to
act as a proxy for human intent (policy).
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Standalone devices are assumed to be equipped with policy-keeping
software agents. Peripheral devices, such as sensors or actuators,
are assumed to be integral parts of the standalone devices, and hence
maintainable by the their software agents.
NO system MUST push changes or data to such agents ad hoc, without a
documented promise to accept; thereafter, `fault tolerance' demands
that we reject the word `must' from most descriptions, and replace it
with `promise of best effort', as to reply on perfect behaviour leads
to brittle systems with unrealistic expectations. For human safety
in a rapidly expanding sphere of human involvement, the only `must'
is for each agent to be stable and self-correcting, subject to the
guidance of policy.
7.5. Standard promises
The following characteristics describe the cooperation between
agents:
1. Standalone devices promise to bootstrap to some trusted
bootservice, i.e. register to one or more workspaces.
2. Standalone devices promise to refuse direct commands imposed from
network peers (as mentioned above).
3. Policy consists of a collection of promises that apply in
labelled contexts, each of which describes a unique desired end-
state.
4. Promises are kept in a convergent manner, so that all promise-
keeping actions lead to the desired end-state, no matter what the
initial state of the device.
5. Agents that live on every device have drivers/renderers and make
all changes without remote communication.
7.6. Contextual policy-based adaptation
Each policy agent promises to maintain a context evaluator that
computes a set of classifying `tags' or `labels' that characterize
the state of the agent. This is updated every time the agent
verifies policy, as its state may change as a result of repairs.
These may be used as conditionals for distributed policy-based
decision-making.
Contextual labels characterize the device, its environment, and its
location and time. The labels can then be used in policy to make
certain promises apply only in specific contexts.
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When promises, within a policy, are tagged by issue or context,
agents can select those that apply to its condition, within a larger
trust relationship implied by policy sourcing. This simplifies logic
and promotes stability, as evidenced by experience with software
agents [CFENGINE].
7.7. Change of policy (system intent)
Policy change can be initiated from within a workspace, subject to a
defined quality assurance, or fit-for-purpose review. Thus change of
infrastructure may be instigated from the bottom-up also, as a self-
service request.
1. Human operators (owners or managers) decide on a policy model for
all devices in an organization or policy group. This may be
informed by the feedback about the success rate of previously
kept promises.
2. The changes are edited into a model, which consists of a
collection of promises that should be kept by all resources on
all devices.
3. Changes are checked and tested before publishing.
4. Once changes are approved, they are published by a policy service
for download at the convenience of the standalone device.
7.8. Separation of concerns versus timescales
Infrastructure stability is supported by a separation of systems into
agencies that act in alignment with specific, separable timescales.
Separation of fast and slow timescales avoids tight coupling and
associated complex behaviours and should be considered a priority for
maintaining safe, stable systems for human dependence.
Systems scale along two broad lines, which a promise-oriented
architecture helps to resolve:
Dynamical scaling Workload timescales concern the quantitative
activity of the system: how fast requests are handled, how
quickly service is delivered, and promises are kept.
Semantic (functional) scaling Semantics are normally the concern of
software engineers and system designers. This facilitates
functional understanding. It is a form of human interface
or knowledge management. It is sometimes at odds with the
needs of dynamical scaling.
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Changes to semantics should generally be slow compared to the
workload related dynamical activity, in order to maintain functional
stability. Cooperative design of workspaces may observe this
principle to foster functional stability and workload efficiency.
7.9. Device roles per workspace or region
A number of functional roles are required to maintain a service
lifecycle in a distributed environment. Making these roles self-
managed within each workspace is how one scales the diversity of
human intent and concerns. Roles are defined by the kinds of
promises kept by devices:
Bootstrap server To provide trusted need-to-know data and local
contacts so that clients can begin working within a policy
domain.
Bootstrap client To accept essential directory information on trust
in order to join a local policy domain.
Policy server To deliver current policy from an authorized source,
appropriate for each client (tenancy terms) from its global
perspective
Policy client To subscribe to the policy, selectively, depending on
context from its local perspective.
Data server data server (aka ``Thing'') To offer a catalogue of data
streams to different tenants This includes sensors,
actuators.
Data Client To subscribe to the policy, selectively, depending on
context from its local perspective.
Identity server Manufacturer User Description service is promised by
all Things providing a URI that points to a description of
the device, its serial number characteristics, service
details etc.
Identity client Identity clients promise to make use of data schemas
and encodings involved in the interpretation of data
pertaining to the device.
Directory service A mutual binding service of published-subscribed
registry information, enabling mutual discovery of interior
services.
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"Control data" "Application data"
+--------------------------------------------------------------+
|+------------------+ +------------------+ +----------------- +| +-----------------+
|| Bootstrap server | | Policy server | | Directory server || | Data client(s) |
|+------------------+ +------------------+ +----------------- +| +-----------------+
+--------|---------------------|----------------------|--------+ |
| | | |
+----------------+ | | |
| | | |
+------------------+ | | | |
| FFD / Standalone | | | | |
| Bootstrap client|--+ | | |
| Policy client |-------+ | |
| Directory server|------------------------------+ |
| Data client |--------------------------------------------------+
+------------------+
"Thing(s)"
The roles in each collaborative workspace. Devices at the bottom of
the figure typically coordinate through workspace services hosted in
the "cloud" or any nearby compute resource. Efficiency suggests
avoiding long data paths, instead moving computational resources
closer to data collection points.
Figure 3
Bootstrapping new devices into a workspace represents the beginning
of a device lifecycle. Devices must begin with the location of a
known bootstrap server. Devices must also promise to advertise their
nature and capabilities, called `identification'. This may include
Manufacturer Usage Description (MUD) identifiers [MUD].
7.10. Connectivity and Network Policy
So far, much as been said on how the application devices provide
services via promises, and how system intent can be described and
orchestrated via policy. There is also a connectivity (transport)
fabric for these devices that operates on a set of promises that
underly the described service framework, i.e. the network. Each
network endpoint can be seen as providing its own set of promises
that are used by other network elements to deliver routing and
switching capabilities [PromiseNet]. The simplest form of SDN is
simply name registration and route management.
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Intent driven networking is becoming more relevant as Software
Defined Networking (SDN) deployments proliferate. In the described
IoT architecture, service policies that describe the IoT system
intent can be used as an input to derive partial network policies
(e.g. Group Based Policy or some other model-based approach), with
modulation by other data discovered from bootstrapping, etc. The
figure below illustrates the relationship between the service and
network layer policies for IoT.
+--------------------+
| IoT Service Policy |
+--------------------+
|
+---------------------+ | +--------------------+
| Topology / Location | | | Orchestration |
| +-+-+
| Bootstrap data | | | Organization policy|
+---------------------+ | +--------------------+
|
\|/
v
+--------------------+
| IoT SDN policy |
+--------------------+
Service policy could be partially rendered as an SDN baseline for
simplifying dependency management. The simplest form of SDN is
simply name registration and route management.
Figure 4
8. Remarks
The architecture, described in this draft, enables densely clustered
IT resources to form arbitrary self-service communities that span
local or wide area networks. This is decouples a logical patchwork
of segments on top of a plain end-to-end IP network. By basing on
principles of fault-tolerance, including publish-subscribe
dissemination semantics, this may be scaled, without bottleneck, by
only the well-known methods currently employed by the World Wide Web.
IPv6 and successors will play a key role in recapturing network
simplicity from the many workarounds that have been stacked on top of
IPv4 and its limitations. However, currently missing are adequate
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directory services to support a transparent workspace concept. The
present Internet architecture is still geared principally towards a
shared single-tenant, top-down management model, with host authority
at the top. Top down methods require the leaf domains to trust (and
hence always be exposed to attack from) the layers high up in the
network. However, shrink-wrapping workspace boundaries closer around
their private resources, this management can be simplified, speeded
up, and become less exposed.
9. Summary and Outlook
The issues discussed and laid out in this draft address key issues of
scalability, fault tolerance, separation of concerns, and federation
of intent within networked information systems. The platform,
described here, is a synthesis of well-known techniques, and is
deliberately aligned with the needs of agile commercial spaces, as
well as large industrial distributions, and small domestic needs. We
purposely leave open vendor specific concerns, which can easily fit
into the described architecture, on top of this common set of
principles.
Interest in using IT to stimulate smart spaces (homes, buildings,
vehicles, cities, etc., necessitates a scalable approach to
interactive services, in which the service clients are not only
humans but sensors and actuators. This cannot be scaled reliably
without the segmentation of spacetime itself. Centralized cloud
controllers, as we understand them today, cannot plausibly manage
stable services for a society to rely on. We propose extending the
notion of cloud and IoT to become a single seamless vision, without
centralization as the core paradigm.
10. Acknowledgments
We are grateful for helpful conversations with K. Burns, M.
Dvorkin, D. Maluf, and E. Lear.
11. Security Considerations
With a pervasive contact surface onto both the Internet and the real
world, security is obvious a major concern. Experience with
pervasive frameworks like [CFENGINE], as well as theoretical studies
of pull-based architectures, suggest that the promise-oriented pull-
only architecture can reduce the exposure to denial of service
attacks and data-based overflow attacks, by rejecting all external
data sent without invitation. Moreover, the tie-in between service
and network policy reduces the likelihood of errors in policy across
the layers.
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Workspaces can play a role too here, as a shrink-wrapping of service
scope around minimal set of endpoints, thus reducing the logical
contact surface for data communications, and publishing information
purely on a need-to-know basis. We take is for granted that
workspace data are encrypted with workspace authorized credentials.
12. Normative References
[Bergstra1]
Bergstra, J. and M. Burgess, "Promise Theory: Principles
and Applications", 2013.
[CFENGINE]
Burgess, M., "A site configuration engine, Computing
Systems", 1995.
[CONVERGE]
Burgess, M., "Configurable immunity model of evolving
configuration management, Science of Computer
Programming", 2004.
[DSOM2005]
Burgess, M., "An Approach to Understanding Policy Based on
Autonomy and Voluntary Cooperation, Lecture Notes in
Computer Science", 2005.
[K8S] Various, , "Kubernetes, Open Source Project", 2015-.
[MicroS] Richardson, C., "Pattern: Microservices Architecture",
2014.
[MUD] Lear, E., "Manufacturer Usage Description", 2015.
[NDN] Zhang, L., Afanasyev, A., Burke, J., Jacobson, V., Claffy,
K., Crowly, P., Papadopoulos, C., Wang, L., and B. Zhang,
"Named Data Networking", 2014.
[OneM2M] OneM2M, , "Standards for M2M and the Internet of Things",
2015.
[PromiseNet]
Borrill, P., Burgess, M., Craw, T., and M. Dvorkin, "A
Promise Theory of Networking", 2014.
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<http://www.rfc-editor.org/info/rfc2119>.
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[SOA] Open Group, , "SOA Reference Architecture Technical
Standard : Basic Concepts", 2016.
[WORKSPC] Burgess, M., Dvorkin, M., and K. Burns, "Self-Service
Workspaces for Federated IT Infrastructure", 2016.
Authors' Addresses
Mark Burgess
Independent Researcher
Oslo
Norway
Herb Wildfeuer
Cisco
San Jose
USA
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