ETSI GR ENI 017 V2.2.1 (2024-06)

Experiential Networked Intelligence (ENI); Overview of Prominent Control Loop Architectures

RGR/ENI-0017v221_Ctrlloop Arch

General Information

• Status: Not Published
• Technical Committee: ENI - Experiential Networked Intelligence
• Current Stage: 12 - Citation in the OJ (auto-insert)
• Due Date: 18-Jun-2024
• Completion Date: 20-Jun-2024

Standards Content (Sample)

GROUP REPORT
Experiential Networked Intelligence (ENI);
Overview of Prominent Control Loop Architectures
Disclaimer
The present document has been produced and approved by the Experiential Networked Intelligence (ENI) ETSI Industry
Specification Group (ISG) and represents the views of those members who participated in this ISG.
It does not necessarily represent the views of the entire ETSI membership.

2 ETSI GR ENI 017 V2.2.1 (2024-06)

Reference
RGR/ENI-0017v221_Ctrllloop Arch
Keywords
cognitive, control
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3 ETSI GR ENI 017 V2.2.1 (2024-06)
Contents
Intellectual Property Rights . 4
Foreword . 4
Modal verbs terminology . 4
Executive summary . 4
1 Scope . 5
2 References . 5
2.1 Normative references . 5
2.2 Informative references . 5
3 Definition of terms, symbols and abbreviations . 6
3.1 Terms . 6
3.2 Symbols . 7
3.3 Abbreviations . 7
4 Prominent Control Loop Architectures . 8
4.1 Introduction . 8
4.2 Definition . 8
4.3 Types of Control Loops . 8
4.3.1 Open. 8
4.3.2 Closed . 9
4.3.3 Hierarchical Closed. 9
4.3.4 Distributed Closed . 9
4.3.5 Adaptive Closed . 9
4.3.6 Federated Closed . 10
4.3.7 Cognitive Closed . 10
4.4 Prominent Control Loop Architectural Styles . 10
4.4.1 OODA . 10
4.4.2 MAPE-K . 11
4.4.3 FOCALE . 11
4.4.4 GANA . 12
4.4.5 COMPA . 14
4.4.6 Cognitive Control Loops (FOCALE v3) . 15
4.4.7 Comparison . 15
4.5 Domains and Control Loops . 19
4.5.1 Introduction. 19
4.5.2 Administrative Domains and Control Loops . 19
4.5.3 Management Domains and Control Loops . 19
4.5.4 Collaborating Control Loops in the Same System . 19
4.5.5 Collaborating Control Loops in Different Systems . 19
5 Summary and Recommendations . 20
History . 21

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Intellectual Property Rights
Essential patents
IPRs essential or potentially essential to normative deliverables may have been declared to ETSI. The declarations
pertaining to these essential IPRs, if any, are publicly available for ETSI members and non-members, and can be
found in ETSI SR 000 314: "Intellectual Property Rights (IPRs); Essential, or potentially Essential, IPRs notified to
ETSI in respect of ETSI standards", which is available from the ETSI Secretariat. Latest updates are available on the
ETSI Web server (https://ipr.etsi.org/).
Pursuant to the ETSI Directives including the ETSI IPR Policy, no investigation regarding the essentiality of IPRs,
including IPR searches, has been carried out by ETSI. No guarantee can be given as to the existence of other IPRs not
referenced in ETSI SR 000 314 (or the updates on the ETSI Web server) which are, or may be, or may become,
essential to the present document.
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Foreword
This Group Report (GR) has been produced by ETSI Industry Specification Group (ISG) Experiential Networked
Intelligence (ENI).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.
Executive summary
The present document specifies a high-level functional abstraction of the ENI System Architecture in terms of
Functional Blocks and External Reference Points. This includes describing how different classes of systems interact
with ENI. Processes, models, and detailed information are beyond the scope of the present document.

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1 Scope
The purpose of the present document is to provide further information on prominent control loop architectures that can
be used in modular system design. This will be applied to the ENI reference system architecture (and any other
applicable ETSI reports and standards). The present document will emphasize control loops that are adaptive and
cognitive. In release 2, the present document will provide further precisions on clause 4.4.4. It will also make any other
updates that are required.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long term validity.
The following referenced documents are not necessary for the application of the present document but they assist the
user with regard to a particular subject area.
[i.1] ETSI GS ENI 005 (V3.1.1): "Experiential Networked Intelligence (ENI); System Architecture".
[i.2] Strassner, J., Agoulmine, N., Lehtihet, E.: "FOCALE - A Novel Autonomic Networking
Architecture", ITSSA Journal 3(1), 64-79, 2007.
[i.3] Boyd, J. R.: "The Essence of Winning and Losing", June 1995.
[i.4] Strassner. J.: "Knowledge Representation, Processing, and Governance in the FOCALE
Autonomic Architecture", book chapter, 2011, Elsevier.
[i.5] Strassner, J.: "Policy-Based Network Management", Morgan Kaufman, ISBN 978-1558608597,
September 2003.
[i.6] MEF 78.1: "MEF Technical Specification: MEF Core Model", Strassner, J., editor, July 2020. ®
[i.7] IBM Autonomic Computing White Paper: "An architectural blueprint for autonomic computing".
[i.8] Strassner, J., van der Meer, S., Won-Ki Hong, J.: "The design of an Autonomic Element for
managing emerging networks and services", International Conference on Ultra Modern
Telecommunications, 2009.
[i.9] Minsky, M.: "The Society of Mind", Simon and Schuster, New York, 1988.
[i.10] R. Mitchell, J. McKim: "Design by Contract, by Example", Addison-Wesley, 2001, ISBN
0201634600.
[i.11] S. van der Meer: "Architectural Artefacts for Autonomic Distributed Systems - Contract
Language", in 6th IEEE Workshop on Engineering of Autonomic and Autonomous Systems
(EASe), April 14-16, 2009.
[i.12] ETSI TS 103 195-2: "Autonomic network engineering for the self-managing Future Internet (AFI);
Generic Autonomic Network Architecture; Part 2: An Architectural Reference Model for
Autonomic Networking, Cognitive Networking and Self-Management".
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[i.13] ETSI GR ENI 016: "Experiential Networked Intelligence (ENI); Functional Concepts for Modular
System Operation".
[i.14] Clark, D.D., Partridge, C., Ramming, J.C., and Wroclawski, J.T.: "A Knowledge Plane for the
Internet", August 2003.
3 Definition of terms, symbols and abbreviations
3.1 Terms
For the purposes of the present document, the terms given in ETSI GS ENI 005 [i.1] and following apply:
abstraction: hiding of unnecessary details to focus on data and information that is relevant for defining a particular
concept or process
administrative domain: domain that employs a set of common administrative processes to manage the behaviour of its
constituent Entities
NOTE: This is based on the definition in [i.6].
agent: computational process that implements the autonomous, communicating functionality of an application
architecture: set of rules and methods that describe the functionality, organization, and implementation of a system
cognition: process of understanding data and information and producing new data, information, and knowledge
cognition model: computer model of how cognitive processes, such as comprehension, action, and prediction, are
performed and influence decisions
context: collection of measured and inferred knowledge that describe the environment in which an entity exists or has
existed
data model: representation of concepts of interest to an environment in a form that is dependent on data repository,
data definition language, query language, implementation language, and/or protocol
NOTE: This definition is taken from [i.6].
decision making: set of processes that result in the selection of a set of actions to take from among several alternative
possible actions
domain: collection of Entities that share a common purpose
NOTE: Each constituent Entity in a Domain is both uniquely addressable and uniquely identifiable within that
Domain. This is based on the definition of an MCMDomain in [i.6].
entity: object in the environment being managed that has a set of unique characteristics and behaviour
NOTE: Objects are represented by classes in an information model.
formal: study of (typically linguistic) meaning of an object by constructing formal mathematical models of that object
and its attributes and relationships
information model: representation of concepts of interest to an environment in a form that is independent of data
repository, data definition language, query language, implementation language, and protocol
NOTE: This definition is taken from [i.6].
inferred knowledge: knowledge that was created based on reasoning, using evidence provided
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knowledge: analysis of data and information, resulting in an understanding of what the data and information mean
NOTE: Knowledge represents a set of patterns that are used to explain, as well as predict, what has happened, is
happening, or is possible to happen in the future; it is based on acquisition of data, information, and skills
through experience and education.
learning: process that acquires new knowledge and/or updates existing knowledge to optimize a function using sample
observations
logic: formal or informal language that evaluates a conclusion based on a set of premises
management domain: domain that uses a set of common Policies to govern its constituent Entities
NOTE: A Management Domain refines the notion of a Domain by adding three important behavioural features:
1) it defines a set of administrators that govern the set of Entities that it contains;
2) it defines a set of applications that are responsible for different governance operations, such as
monitoring, configuration, and so forth;
3) it defines a common set of management mechanisms, such as policy rules, that are used to govern
the behaviour of MCMManagedEntities contained in the MCMManagementDomain.
This is based on the definition of an MCMDomain in [i.6].
model: representation of the entities of a system, including their relationships and dependencies, using an established
set of rules and concepts
Model-Driven Engineering (MDE): approach in which models are central to all phases of the development and
implementation processes
ontology (for ENI): language, consisting of a vocabulary and a set of primitives, that enable the semantic
characteristics of a domain to be modelled
policy: set of rules that is used to manage and control the changing and/or maintaining of the state of one or more
managed objects
semantics: study of the meaning of something (e.g. a sentence or a relationship in a model)
situation: set of circumstances and conditions at a given time that may influence decision-making:
situation awareness: perception of data and behaviour that pertain to the relevant circumstances and/or conditions of a
system or process, the comprehension of the meaning and significance of these data and behaviours, and how processes,
actions, and new situations inferred from these data and processes are likely to evolve in the near future
3.2 Symbols
Void.
3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply:
AFI Autonomic Future Internet
AI Artificial Intelligence
AMC Autonomic Management and Control
API Application Programming Interface
COM Control, Orchestration and Management
COMPA Control, Orchestration, Management, Policy and Analytics
DE Decision Element
DEN Directory Enabled Networks
DENON Directory Enabled Networks ONtology
EMS Element Management System
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ESB Enterprise Service Bus
FB Functional Block
FOCALE Foundation - Observe - Compare - Act - Learn - rEason
FSM Finite State Machine
GANA Generic Autonomic Networking Architecture ®
IBM International Business Machines
KP Knowledge Plane
MAPE Model-Analyse-Plan-Execute
MAPE-K Model-Analyse-Plan-Execute-Knowledge
MBT Model-Based Translation
MBTS Model-Based Translation Services
MDE Model-Driven Engineering
ME Managed Element
NE Network Element
NMS Network Management System
ONIX Overlay Network for Information eXchange
OODA Observe-Orient-Decide-Act
OSS Operational Support System
QoE Quality of Experience
QoS Quality of Service
XML eXtensible Markup Language
4 Prominent Control Loop Architectures
4.1 Introduction
Most control loop architectures for adaptive and cognitive systems use both feedback (and feedforward) mechanisms.
These control loop signals play a critical role in not just stabilizing the system, but more importantly, providing
mechanisms for the system to learn experientially.
EXAMPLE: A simple feedback loop consists of taking past interactions with the environment and combining
them with current information to guide current and future interactions.
4.2 Definition
A control loop is a mechanism that senses the performance of an object or process being controlled to achieve desired
behaviour. ENI is concerned with different types of closed control loops, where the controlling action is dependent on
feedback from the object or process being controlled. In other words, closed loops use feedback to monitor and adjust
the behaviour of a system to achieve one or more goals.
4.3 Types of Control Loops
4.3.1 Open
An open control loop is a control loop whose controlling action is independent of the output of the object or process
being controlled. This type of control loop does not link the control action to the object or process being controlled (it
simply continues to apply the control action). This type of control loop will likely not be used in the ENI system.
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4.3.2 Closed
A closed control loop is a control loop whose controlling action is dependent on feedback from the object or process
being controlled. This type of control loop measures the difference between the actual and desired values of a set of
variables to adjust a set of parameters to change the behaviour of the system to bring the actual value closer to that of
the desired value.
Figure 4.3.2-1: An Exemplary Closed Control Loop
4.3.3 Hierarchical Closed
A hierarchical closed control loop is a control loop that is organized in the form of a tree. This organization enables
different decisions to be made by different nodes in the tree. In general, there is a set of supervisory closed control loops
that allocate tasks to subordinate closed control loops. Each subordinate closed control loop performs its tasks and
returns its result to its superordinate closed control loop. Advanced examples enable one of a group of designated closed
control loops to take control of the hierarchy dependent on goals and the environment. This is an example of a
self-organizing hierarchical closed control loop.
In general, the topmost closed control loop reasons about an abstract world model; its subordinate closed control loops
reason about increasingly more specific models, or portions of models.

Figure 4.3.3-1: An Exemplary Hierarchical set of Closed Control Loops
4.3.4 Distributed Closed
A distributed closed control loop is a closed control loop whose components are physically distributed among different
locations. Each component in a distributed closed control loop uses a message passing mechanism to communicate with
one or more other components of the distributed closed control loop.
4.3.5 Adaptive Closed
An adaptive closed control loop is a control loop whose controlling function adapts to the object or process being
controlled using parameter that are either unknown and/or vary over time. The parameters may be defined using a
model that defines the desired closed loop performance, or statistical analysis to build a mathematical model from
measured data.
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4.3.6 Federated Closed
A federated closed control loop is a set of semi-autonomous closed control loops that use formal agreements to govern
their interaction and behaviour. This includes rules to admit new members of the federation, as well as rules governing
the visibility and types of information that can be shared with other members of the federation. Each closed control loop
operates on the same goal using its own local data. Decisions from each closed control loop are then aggregated and
published.
4.3.7 Cognitive Closed
Cognition is the process of understanding data and information and producing new data, information, and knowledge. A
cognitive closed control loop selects data and behaviours to monitor that can help assess the status of achieving a set of
goals, and produce new data, information, and knowledge to facilitate the attainment of those goals.
4.4 Prominent Control Loop Architectural Styles
4.4.1 OODA
Col. John Boyd's control loop [i.3], [i.4] and [i.5] consists of four phases:
• Observe, Orient, Decide and Act (OODA).
It is shown in Figure 4.4.1-1 which is drawn to emphasize how orientation shapes observation, decision, and action.
While the loop appears to be sequential, this is merely for convenience. The orientation step is critical, as it determines
how observations, decisions, and actions are performed. Hence, observation, orientation, and action occur
simultaneously and continuously. As Boyd observed, people act according to how they perceive the world, as opposed
to how the world really is.
Figure 4.4.1-1: The OODA Control Loop
One of the strongest features of the OODA loop is to initiate or modify actions in response to observed events. If this
can be transformed into a machine-understandable form, then formal logic can be applied to examine all different
concurrent options to arrive at the best plan to achieve the goals of the mission. This is implemented in FOCALE,
which stands for Foundation - Observe - Compare - Act - Learn - rEason; it is an adaptive and cognitive control loop
(see [i.2] and [i.4]).
In stark contrast to other control loop architectures, OODA is a set of interacting loops, where observations in the
current context are filtered (the orient phase) to make them relevant.
The OODA loop was the inspiration and foundation for FOCALE, which is an enhanced version of OODA that features
the addition of cognition.
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4.4.2 MAPE-K ®
In [i.7], IBM (International Business Machines) defined the Monitor-Analyse-Plan-Execute, or MAPE, control loop.
Since all 4 functions depend on the Knowledge function, it is called Model-Analyse-Plan-Execute-Knowledge
(MAPE-K). It is shown in Figure 4.4.2-1.

Figure 4.4.2-1: The IBM MAPE-K Control Loop
Sensors and Effectors get data from and provide commands to both the entity being managed and to other elements of
the management system. The knowledge source implements a repository that provides access to knowledge according
to the interfaces of data and information to be used by the control loop. In addition, the four control loop functions
consume and generate knowledge.
4.4.3 FOCALE
FOCALE [i.2] and [i.4], which stands for Foundation - Observe - Compare - Act - Learn - rEason, was created to
automate the complex, manually-intensive configuration tasks of network devices. The main components of FOCALE
are shown in Figure 4.4.3-1. Each building block is connected using a distributed semantic Enterprise Service Bus
(ESB) that supports simple as well as semantic queries. The difference between a semantic ESB and a standard ESB is
that a semantic ESB can be used to orchestrate content, whereas standard ESBs are limited to orchestrating messages.
The FOCALE Autonomic Manager uses the semantic ESB to orchestrate behavior. It can support different types of
knowledge acquisition and distribution (e.g. push, pull, and scheduled) and performs common processing (e.g. semantic
annotation, filtering and storage) before content is delivered to components. This enables components to register interest
in knowledge in a more precise fashion, and thus reduce messaging overhead.
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Figure 4.4.3-1: A Simplified Version of the FOCALE Control Loop Architecture
FOCALE uses the DEN-ng information model and the DENON-ng ontologies to translate disparate sensed data into a
common networking lingua franca. DEN-ng is used to represent the static characteristics and behavior of entities;
DENON-ng is then used to augment this model with consensual meaning and definitions so that domain- and
vendor-specific concepts can be mapped into a common terminology. This enables facts extracted from sensor input
data to be reasoned about using ontology-based inferencing. This is the foundation for Cognitive Control Loops (see
clause 4.3.7) and the basis for model-driven decision-making (see [i.1] and [i.4]).
In FOCALE, sensor data is retrieved and translated from vendor- and device-specific data into a normalized form in
eXtensible Markup Language (XML) using model-based mapping and ontology-based reasoning. This is then analysed
to determine the current state of the managed entity. The curr
...