Internet DRAFT - draft-contreras-coinrg-clas-evolution
draft-contreras-coinrg-clas-evolution
COINRG L. M. Contreras
Internet-Draft Telefonica
Intended status: Informational M. Boucadair
Expires: 4 September 2023 Orange
D. Lopez
Telefonica
C. J. Bernardos
Universidad Carlos III de Madrid
3 March 2023
An Evolution of Cooperating Layered Architecture for SDN (CLAS) for
Compute and Data Awareness
draft-contreras-coinrg-clas-evolution-00
Abstract
This document proposes an extension to the Cooperating Layered
Architecture for Software-Defined Networking (SDN) by including
compute resources and data processing.
Status of This Memo
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This Internet-Draft will expire on 4 September 2023.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions and Definitions . . . . . . . . . . . . . . . . . 3
3. Cooperating Layered Architecture for Software-Defined
Networking (CLAS) . . . . . . . . . . . . . . . . . . . . 3
4. Augmentation of CLAS with Compute and Data Awareness . . . . 5
4.1. Compute Stratum . . . . . . . . . . . . . . . . . . . . . 5
4.2. Learning Plane . . . . . . . . . . . . . . . . . . . . . 5
4.3. Extended CLAS architecture . . . . . . . . . . . . . . . 6
5. Discusion on research aspects of the proposed architecture . 7
5.1. Discusion related to the Compute Stratum . . . . . . . . 7
5.2. Discusion related to the Learning Plane . . . . . . . . . 7
6. TODO for next versions of this document . . . . . . . . . . . 8
7. Security Considerations . . . . . . . . . . . . . . . . . . . 8
8. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 8
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 8
9.1. Normative References . . . . . . . . . . . . . . . . . . 8
9.2. Informative References . . . . . . . . . . . . . . . . . 8
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 10
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10
1. Introduction
Current telecommunication networks are evolving towards a tight
integration of interconnected compute environments, offering
capabilities for the instantiation of virtualized network functions
interworking with physical variants of other network functions,
altogether used to build and deliver services.
Moreover, network operations are complementing the capabilities of
automation (e.g., [RFC8969]) and programmability (e.g.,
[RFC7149][RFC7426]) with the introduction of Artificial Intelligence
(AI) and Machine Learning (ML) techniques to facilitate informed
decisions as well as predictive behaviors enabling consistent closed
loop automation.
It is then necessary to provide a network management framework that
could incorporate these technical components, structuring the
different concerns (i.e., connectivity, processing and data analysis)
and the interaction among components operating the network. Existing
approaches, e.g. [RFC8969] only focus on the networking (i.e.,
connectivity) part without consideration of both compute domain and
data analysis.
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This document describes an evolution of the Cooperating Layered
Architecture for Software-Defined Networking (CLAS) [RFC8597] to
include the aforementioned aspects into the architecture.
2. Conventions and Definitions
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
3. Cooperating Layered Architecture for Software-Defined Networking
(CLAS)
[RFC8597] describes an SDN architecture structured in two different
strata, namely Service Stratum and Transport Stratum. On one hand,
the Service Stratum contains the functions related to the provision
of services and the capabilities offered to external applications.
On the other hand, the Transport Stratum comprises the functions
focused on the transfer of data between the communication endpoints
(e.g., between end-user devices, between two service gateways, etc.).
Each of the strata is structured in different planes, as follows:
* The Control plane, which centralizes the control functions of each
stratum and directly controls the corresponding resources.
* The Management plane, logically centralizing the management
functions for each stratum, including the management of the
control and resource planes.
* The Resource plane, that comprises the resources for either the
transport or the service functions.
Figure 1 illustrates the original CLAS architecture.
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Applications
/\
||
||
+-------------------------------------||-------------+
| Service Stratum || |
| \/ |
| ........................... |
| . SDN Intelligence . |
| . . |
| +--------------+ . +--------------+ . |
| | Resource Pl. | . | Mgmt. Pl. | . |
| | |<===>. +--------------+ | . |
| | | . | Control Pl. | | . |
| +--------------+ . | |-----+ . |
| . | | . |
| . +--------------+ . |
| ........................... |
| /\ |
| || |
+-------------------------------------||-------------+
|| Standard
-- || -- API
||
+-------------------------------------||-------------+
| Transport Stratum || |
| \/ |
| ........................... |
| . SDN Intelligence . |
| . . |
| +--------------+ . +--------------+ . |
| | Resource Pl. | . | Mgmt. Pl. | . |
| | |<===>. +--------------+ | . |
| | | . | Control Pl. | | . |
| +--------------+ . | |-----+ . |
| . | | . |
| . +--------------+ . |
| ........................... |
| |
| |
+----------------------------------------------------+
Figure 1: Cooperating Layered Architecture for SDN {{RFC8597}}
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4. Augmentation of CLAS with Compute and Data Awareness
The CLAS architecture was initially conceived from the perspective of
exploiting the advantages of network programmability in operational
networks.
The evolution of current telecommunication services and networks are,
however, introducing new aspects:
* Considerations of distributed computing capabilities attached to
different points in the network, intended for hosting a variety of
services and applications usually in a virtualized manner (e.g.,
[I-D.contreras-alto-service-edge]).
* Introduction of Artificial Intelligence (AI) and Machine Learning
(ML) techniques in order to improve operations by means of closed
loop automation (e.g., [I-D.francois-nmrg-ai-challenges]).
With that in mind, this memo proposes augmentations to the original
CLAS architecure by adding the aforementioned aspects.
4.1. Compute Stratum
The CLAS architecture is extended by adding a new stratum, named
Compute Stratum. This stratum contains the control, management, and
resource planes related to the computing aspects. This additional
stratum cooperates with the other two in order to facilitate the
overall service provision in the network.
With this addition, and in order to be more explicit in the strata
scope, the previously named Transport Stratum is renamed as
Connectivity Stratum, representing the fact that this stratum
responsibility is focused on the overall connectivity supporting the
other two strata in the architecture.
4.2. Learning Plane
A further extension to the original CLAS architecture is related to
the need of collecting, processing and sharing relevant data from
each of the considered strata. With that purpose a Learning Plane is
proposed to complement the already existing planes per stratum.
The learning plane will be in charge of handling the data
specificities of each stratum. Thus, the learning plane in the
Service Stratum is focused on data relevant to the service as defined
by the application or service owner, usually in terms of service key
performance indicators (KPI) [TMV]. Then, the learning plane in the
compute stratum concentrates on data related to the computing
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capabilities in use (e.g., CPU load, RAM usage, storage utilization,
etc) [OpenStack]. Finally, the learning plane in the network stratum
is in charge of handling the monitoring and telemetry information
obtained from the network (e.g.,
[I-D.ietf-opsawg-service-assurance-yang]).
4.3. Extended CLAS architecture
Figure 2 presents the augmentation proposed showing the relationship
among strata.
Applications
/\
||
+-------------------------------------||-------------+
| Service Stratum || |
| \/ |
| +--------------+ ........................... |
| | Learning Pl. | . SDN Intelligence . |
| | |<===>. . |
| +-----/\-------+ . +--------------+ . |
| || . | Mgmt. Pl. | . |
| || . +--------------+ | . |
| +-----\/-------+ . | Control Pl. |-----+ . |
| | Resource Pl. | . | | . |
| | |<===>. +--------------+ . |
| +--------------+ ........................... |
| /\ /\ |
| || || |
+--------------------------------||-------------||---+
Standard API -- || -- ||
+--------------------------------||-----+ ||
| Compute Stratum || | ||
| \/ | ||
| +----------+ ................... | ||
| | Learning | . SDN . | Std. ||
| | Plane |<==>. Intelligence . | API ||
| +-----/\---+ . +----------+ . | -- || --
| || . | Mgmt. Pl.| . | ||
| || . +----------+ | . | ||
| +-----\/---+ . | Control |-+ . | ||
| | Resource | . | Plane | . | ||
| | Plane |<==>. +----------+ . | ||
| +----------+ ................... | ||
+----------------------------------/\---+ ||
Standard API -- || -- ||
+-------------------||-----------||-----+
| Connectivity || || |
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| Stratum || || |
| \/ \/ |
| +----------+ ................... |
| | Learning | . SDN . |
| | Plane |<==>. Intelligence . |
| +-----/\---+ . +----------+ . |
| || . | Mgmt. Pl.| . |
| || . +----------+ | . |
| +-----\/---+ . | Control |-+ . |
| | Resource | . | Plane | . |
| | Plane |<==>. +----------+ . |
| +----------+ ................... |
+---------------------------------------+
Figure 2: Extended CLAS architecture
5. Discusion on research aspects of the proposed architecture
5.1. Discusion related to the Compute Stratum
The inclusion of the Compute Stratum allows the extension of the
resource layer/plane in a manner that the network (i.e., including
processing capabilities adn the associated connectivity) can be
programmed consistently in an integrated way. This is very relevant
when evolving to network architectures pursuing the could-edge
continuum, even considering the extension to the very extreme edge.
Important to note. the aforementioned cloud-edge continuum could be
potentially constituted by resources from multiple administrative
domains. Enabling the management of multiple heterogeneous domains
in a so-called "frictionless" manner is the necessary to be explored.
5.2. Discusion related to the Learning Plane
One os the aspects to investigate is the application of AI to network
management and control. There are multiple flows to consider:
* Data in the closed loop such as the monitoring/telemetry flows
from network to AI as well as action/control from AI to network
* Flows related to AI behavior (policies/intents) as defined by the
network admins towards the AI
* Feedback (i.e., predictions, suggedtesd actions, etc) from AI to
network administrators
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* Flows facilitating the cooperation among distinct Learning Planes,
implying knowledge sharing among different segments, and knowledge
aggregation at different strata of control.
A potential way to follow is the definition of a common, model-based,
approach, also defining a recursive structure that could become a
generalization of the CLAS model.
6. TODO for next versions of this document
This version is a work-in-progress. Next versions of the document
will address some further aspects such as:
* Communication between strata (and planes).
* Deployment scenarios (including legacy ones).
* Potential use cases (specially in alignment with on-going
activities in COINRG / NMRG).
7. Security Considerations
Same security considerations as reflected in [RFC8597] with regards
to the strata architecture apply also here.
Apart from that, the introduction of the Learning plane on the data
management imposes additional security concerns.
(TODO: elaborate on data-related security issues).
8. IANA Considerations
This document has no IANA actions.
9. References
9.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/rfc/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/rfc/rfc8174>.
9.2. Informative References
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[I-D.contreras-alto-service-edge]
Contreras, L. M., Perez, D. A. L., Rothenberg, C. E., and
S. Randriamasy, "Use of ALTO for Determining Service
Edge", Work in Progress, Internet-Draft, draft-contreras-
alto-service-edge-06, 24 October 2022,
<https://datatracker.ietf.org/doc/html/draft-contreras-
alto-service-edge-06>.
[I-D.francois-nmrg-ai-challenges]
François, J., Clemm, A., Papadimitriou, D., Fernandes, S.,
and S. Schneider, "Research Challenges in Coupling
Artificial Intelligence and Network Management", Work in
Progress, Internet-Draft, draft-francois-nmrg-ai-
challenges-01, 24 October 2022,
<https://datatracker.ietf.org/doc/html/draft-francois-
nmrg-ai-challenges-01>.
[I-D.ietf-opsawg-service-assurance-yang]
Claise, B., Quilbeuf, J., Lucente, P., Fasano, P., and T.
Arumugam, "YANG Modules for Service Assurance", Work in
Progress, Internet-Draft, draft-ietf-opsawg-service-
assurance-yang-11, 3 January 2023,
<https://datatracker.ietf.org/doc/html/draft-ietf-opsawg-
service-assurance-yang-11>.
[RFC7149] Boucadair, M. and C. Jacquenet, "Software-Defined
Networking: A Perspective from within a Service Provider
Environment", RFC 7149, DOI 10.17487/RFC7149, March 2014,
<https://www.rfc-editor.org/rfc/rfc7149>.
[RFC7426] Haleplidis, E., Ed., Pentikousis, K., Ed., Denazis, S.,
Hadi Salim, J., Meyer, D., and O. Koufopavlou, "Software-
Defined Networking (SDN): Layers and Architecture
Terminology", RFC 7426, DOI 10.17487/RFC7426, January
2015, <https://www.rfc-editor.org/rfc/rfc7426>.
[RFC8597] Contreras, LM., Bernardos, CJ., Lopez, D., Boucadair, M.,
and P. Iovanna, "Cooperating Layered Architecture for
Software-Defined Networking (CLAS)", RFC 8597,
DOI 10.17487/RFC8597, May 2019,
<https://www.rfc-editor.org/rfc/rfc8597>.
[RFC8969] Wu, Q., Ed., Boucadair, M., Ed., Lopez, D., Xie, C., and
L. Geng, "A Framework for Automating Service and Network
Management with YANG", RFC 8969, DOI 10.17487/RFC8969,
January 2021, <https://www.rfc-editor.org/rfc/rfc8969>.
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[TMV] "Service performance measurement methods over 5G
experimental networks", May 2021.
Acknowledgments
This work has been partially funded by the European Union under
Horizon Europe projects NEMO (NExt generation Meta Operating system)
grant number 101070118, and CODECO (COgnitive, Decentralised Edge-
Cloud Orchestration), grant number 101092696.
Authors' Addresses
Luis M. Contreras
Telefonica
Ronda de la Comunicacion, s/n
28050 Madrid
Spain
Email: luismiguel.contrerasmurillo@telefonica.com
URI: http://lmcontreras.com
Mohamed Boucadair
Orange
35000 Rennes
France
Email: mohamed.boucadair@orange.com
Diego R. Lopez
Telefonica
Seville
Spain
Email: diego.r.lopez@telefonica.com
Carlos J. Bernardos
Universidad Carlos III de Madrid
Av. Universidad, 30
28911 Leganes, Madrid
Spain
Email: cjbc@it.uc3m.es
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