COINRG L. M. Contreras
Internet-Draft Telefonica
Intended status: Informational M. Boucadair
Expires: 25 April 2024 Orange
D. Lopez
Telefonica
C. J. Bernardos
Universidad Carlos III de Madrid
23 October 2023
An Evolution of Cooperating Layered Architecture for SDN (CLAS) for
Compute and Data Awareness
draft-contreras-coinrg-clas-evolution-02
Abstract
This document proposes an extension to the Cooperating Layered
Architecture for Software-Defined Networking (SDN) by including
compute resources and data analysis processing capabilities.
Status of This Memo
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This Internet-Draft will expire on 25 April 2024.
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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 Analysis
Awareness . . . . . . . . . . . . . . . . . . . . . . . . 5
4.1. Compute Stratum . . . . . . . . . . . . . . . . . . . . . 5
4.2. Data Analysis Plane . . . . . . . . . . . . . . . . . . . 5
4.3. Extended CLAS Architecture . . . . . . . . . . . . . . . 6
5. Discussion on Research Aspects of the Proposed
Architecture . . . . . . . . . . . . . . . . . . . . . . 7
5.1. Discussion Related to the Compute Stratum . . . . . . . . 8
5.2. Discussion Related to the Data Analysis Plane . . . . . . 8
6. Applicability scenarios . . . . . . . . . . . . . . . . . . . 9
6.1. Cloud-edge Continuum . . . . . . . . . . . . . . . . . . 9
6.2. Network-application Integration . . . . . . . . . . . . . 10
7. Communication between strata (and planes) . . . . . . . . . . 10
7.1. Communication between Applications and Service Stratum . 10
7.2. Communication between Service Stratum and Connectivity
Stratum . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.3. Communication between Service stratum and Compute
Stratum . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.4. Communication between Connectivity stratum and Compute
stratum . . . . . . . . . . . . . . . . . . . . . . . . . 11
8. TODO for next versions of this document . . . . . . . . . . . 11
9. Security Considerations . . . . . . . . . . . . . . . . . . . 12
10. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 12
11. References . . . . . . . . . . . . . . . . . . . . . . . . . 12
11.1. Normative References . . . . . . . . . . . . . . . . . . 12
11.2. Informative References . . . . . . . . . . . . . . . . . 12
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 15
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 15
1. Introduction
Telecommunication networks are evolving towards a tight integration
of interconnected compute environments, offering specifically
capabilities for the instantiation of virtualized network functions
interworking with physical variants of other network functions,
altogether used to build and deliver services.
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Moreover, network operations are endorsing automation (e.g.,
[RFC8969]) and programmability (e.g., [RFC7149][RFC7426]) with the
introduction of closed-loop mechanisms, intent declarations,
Artificial Intelligence (AI) and Machine Learning (ML) techniques to
facilitate informed (proactive) decisions as well as predictive
behaviors enabling consistent 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 telemetry data
generation and analysis) and the interaction among components
operating the network. Existing approaches (e.g. [RFC8969]) only
focus on the networking aspects (i.e., connectivity) without
sufficient consideration of both compute domain and data analysis.
data analysis.
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.
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* The Resource plane, that comprises the resources for either the
transport or the service functions.
Figure 1 illustrates the original CLAS architecture.
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 Analysis Awareness
The CLAS architecture was initially conceived from the perspective of
exploiting the advantages of network programmability in operational
networks.
The evolution of current networks and the services they support 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 evidence-driven techniques, such as Analytics,
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 architecture 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. Data Analysis Plane
Data Analysis is usually part of the management plane. In order to
insist on the “learning” matters, this document defines Data Analysis
as a dedicated plane as it streams sensitive data that is
instrumental for the CLAS strata.
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 Data Analysis
plane is proposed to complement the already existing planes per
stratum.
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The Data Analysis plane is in charge of handling the data
specificities of each stratum. Thus, the Data Analysis 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 Data Analysis plane in
the compute stratum concentrates on data related to the computing
capabilities in use (e.g., CPU load, RAM usage, storage utilization,
etc) [OpenStack]. Finally, the Data Analysis 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 || |
| \/ |
| +--------------+ ........................... |
| | Data Analysis| . SDN Intelligence . |
| | Pl. |<===>. . |
| +-----/\-------+ . +--------------+ . |
| || . | Mgmt. Pl. | . |
| || . +--------------+ | . |
| +-----\/-------+ . | Control Pl. |-----+ . |
| | Resource Pl. | . | | . |
| | |<===>. +--------------+ . |
| +--------------+ ........................... |
| /\ /\ |
| || || |
+--------------------------------||-------------||---+
Standard API -- || -- ||
+--------------------------------||-----+ ||
| Compute Stratum || | ||
| \/ | ||
| +----------+ ................... | ||
| | Data Ana-| . SDN . | Std. ||
| | lysis Pl.|<==>. Intelligence . | API ||
| +-----/\---+ . +----------+ . | -- || --
| || . | Mgmt. Pl.| . | ||
| || . +----------+ | . | ||
| +-----\/---+ . | Control |-+ . | ||
| | Resource | . | Plane | . | ||
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| | Plane |<==>. +----------+ . | ||
| +----------+ ................... | ||
+----------------------------------/\---+ ||
Standard API -- || -- ||
+-------------------||-----------||-----+
| Connectivity || || |
| Stratum || || |
| \/ \/ |
| +----------+ ................... |
| | Data Ana-| . SDN . |
| | lysis Pl.|<==>. Intelligence . |
| +-----/\---+ . +----------+ . |
| || . | Mgmt. Pl.| . |
| || . +----------+ | . |
| +-----\/---+ . | Control |-+ . |
| | Resource | . | Plane | . |
| | Plane |<==>. +----------+ . |
| +----------+ ................... |
+---------------------------------------+
Figure 2: Extended CLAS architecture
The relationship among the Connectivity and Compute strata is not
hierarchical in any sense. Both are at the same level and there is
no dependecy of one of them over the other. A more simplistic
representation of the different starta is presented in Figure 3 with
less details regarding the planes for the sake of clarity.
Service Stratum
A A
| |
--------|-----------|-------
| |
V V
Connectivity <---> Compute
Stratum Stratum
Figure 3: Simple representation of the extended CLAS architecture
5. Discussion on Research Aspects of the Proposed Architecture
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5.1. Discussion Related to the Compute Stratum
The inclusion of the Compute Stratum extends the resource layer/plane
in a manner that the network (i.e., including processing capabilities
and the associated connectivity) can be programmed consistently and
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. Discussion Related to the Data Analysis Plane
One of the aspects to investigate is the application of evidence-
driven, “smart” management (most notably, based on AI) to network
management and control. There are multiple issues to consider:
* Telemetry data generation and context information.
* The lifecycle of data flows in the closed loop, in both
directions,from network to management and vice versa.
* The flows controlling the behavior (policies/intents), as defined
by network admins, and potentially users, towards the management
elements.
* Feedback (i.e., predictions, suggested actions, etc) patterns from
management elements to network administrators, and users.
* Metadata models to represent sources and consumers of data at the
Data Analysis plane, supporting the dynamic attachment of new
sources and consumers, including data composition elements.
* Flow patterns and models facilitating the cooperation among
distinct Data Analysis planes, implying knowledge sharing among
different segments, and data and knowledge aggregation at
different strata of control.
* Security and privacy issues regarding the usage of data flows,
considering their provenance and potential attestation methods.
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.
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6. Applicability scenarios
This section describes deployment scenarios suitable for the CLAS
architecture evolution.
6.1. Cloud-edge Continuum
More and more, computing facilities are being deployed by network
service providers to satisfy a number of use cases requiring of
distributed compute processing capabilities (e.g., for micro-service
instantiation), some of them as edge nodes because of the need of
proximity. Use cases in [I-D.irtf-coinrg-use-cases] exemplify those
needs.
Such distributed computing facilities form what is known as cloud-
edge continuum. Those distributed facilities need to be
interconnected for accomplishing end-to-end services, based in the
interaction of multiple applications of service functions placed in
different compute nodes for performance or resource efficiency
reasons.
Current ways of deploying services follow the cloud-native approach
of instantiating a set of micro-services that can be located at
different compute nodes. Typical cloud management systems such as
Kubernetes take care of the allocation of cloud-edge resources across
distinct nodes or clusters. For the networking part, it is necessary
to interact with network controllers capable of providing the
necessary connectivity with certain guarantees.
The extended CLAS architecture represents a framework where the
cloud-native resources can be handled in combination with the
connectivity part, assuring the service not only at the provisioning
phase but during the complete service lifecycle, and supporting them
across different domains.
Features that can expected to be satisfied in this type of scenarios
are:
* Overall resource optimization of system resources at different
levels (i.e., compute, network, etc). This can imply a process of
learning and inferring status based on historical resource usage
data.
* Assurance of Service Level Objectives (SLOs) by acting on either
the compute or the connectivity parts. This can motivate the need
of compute workloads migration along the service lifetime between
compute nodes, requiring to adapt the connectivity to the new
placement.
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* Secure transfer of data across the cloud-edge continuum, with the
necessary isolation of services among users, and the required
Confidentiality and integrity properties, which can imply the
application of isolation capabilities trustworthiness verification
in both compute and connectivity strata.
6.2. Network-application Integration
Nowadays applications take service decisions mostly decoupled from
network status and conditions. Similarly, the network is not aware
of applications needs, so that is not possible in certain cases to
satisfy application needs. Thus, emerges the need of further
collaboration or integration between applications and the network.
[RFC9419] discusses principles for designing mechanisms allowing
application - network collaboration.
Such collaboration can proactively or reactively trigger actions in
the network at run time. Features that can expected to be satisfied
in this type of scenarios are:
* Monitoring information that could be relevant for either the
application or the network. The monitoring information will be a
composition of information from both compute and connectivity
strata.
* Exposure of capabilities from the network, including (and even
combining) both the compute and connectivity strata.
* Usage of metadata for either the connectivity or the processing of
the information at service level
7. Communication between strata (and planes)
The communication between strata (and the planes within) is
represented in Figure 2 by means of generic standard APIs. An
initial or preliminary analysis of possible means of communicationg
strata is provided here.
This is not an exhaustive exercise but an effort to concretize
examples of communication mechanisms. Further versions of the
document will refine this initial exercise.
7.1. Communication between Applications and Service Stratum
The following mechanisms can be identified as communication means
between Applications and Service Stratum.
* Connectivity Provisioning Negotiation Protocol (CPNP) [RFC8921]
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* Interconnection Intents
[I-D.contreras-nmrg-interconnection-intents]
* Slice intent [I-D.contreras-nmrg-transport-slice-intent]
* Selection of proper edge for service placement
[I-D.contreras-alto-service-edge]
* Composition of service function chains
[I-D.lcsr-alto-service-functions]
7.2. Communication between Service Stratum and Connectivity Stratum
Similarly, the following are potential mechanisms for communication
between Service Stratum and Connectivity Stratum
* Framework for Automating Service and Network Management [RFC8969],
as well as the models referenced there
* IETF Network Slice Service model
[I-D.ietf-teas-ietf-network-slice-nbi-yang]
* Service function aware TE topology model
[I-D.ietf-teas-sf-aware-topo-model]
7.3. Communication between Service stratum and Compute Stratum
Between both Service and Compute Stratum the follwoing mechanisms
could be used.
* Data Center aware TE topology model
[I-D.llc-teas-dc-aware-topo-model]
* Cloud-based solutions (e.g., Kubernetes)
7.4. Communication between Connectivity stratum and Compute stratum
Finally, the direct communication between COnnectivity and Compute
strata can be realized by mechanisms as follows:
* Traffic steering with service function awareness (work in progress
in CATS WG)
8. 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:
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* Deployment scenarios (including legacy ones).
* Potential use cases (specially in alignment with on-going
activities in COINRG / NMRG).
9. 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 Data Analysis plane on the
data management imposes additional security concerns.
(TODO: elaborate on data-related security issues).
10. IANA Considerations
This document has no IANA actions.
11. References
11.1. 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>.
11.2. Informative References
[I-D.contreras-alto-service-edge]
Contreras, L. M., Randriamasy, S., Ros-Giralt, J., Perez,
D. A. L., and C. E. Rothenberg, "Use of ALTO for
Determining Service Edge", Work in Progress, Internet-
Draft, draft-contreras-alto-service-edge-10, 13 October
2023, <https://datatracker.ietf.org/doc/html/draft-
contreras-alto-service-edge-10>.
[I-D.contreras-nmrg-interconnection-intents]
Contreras, L. M. and P. Lucente, "Interconnection
Intents", Work in Progress, Internet-Draft, draft-
contreras-nmrg-interconnection-intents-03, 24 October
2022, <https://datatracker.ietf.org/doc/html/draft-
contreras-nmrg-interconnection-intents-03>.
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[I-D.contreras-nmrg-transport-slice-intent]
Contreras, L. M., Demestichas, P., and J. Tantsura, "IETF
Network Slice Intent", Work in Progress, Internet-Draft,
draft-contreras-nmrg-transport-slice-intent-06, 24 October
2022, <https://datatracker.ietf.org/doc/html/draft-
contreras-nmrg-transport-slice-intent-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-02, 13 March 2023,
<https://datatracker.ietf.org/doc/html/draft-francois-
nmrg-ai-challenges-02>.
[I-D.ietf-opsawg-service-assurance-yang]
Claise, B., Quilbeuf, J., Lucente, P., Fasano, P., and T.
Arumugam, "A YANG Data Model 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>.
[I-D.ietf-teas-ietf-network-slice-nbi-yang]
Wu, B., Dhody, D., Rokui, R., Saad, T., and J. Mullooly,
"A YANG Data Model for the IETF Network Slice Service",
Work in Progress, Internet-Draft, draft-ietf-teas-ietf-
network-slice-nbi-yang-08, 23 October 2023,
<https://datatracker.ietf.org/doc/html/draft-ietf-teas-
ietf-network-slice-nbi-yang-08>.
[I-D.ietf-teas-sf-aware-topo-model]
Bryskin, I., Liu, X., Lee, Y., Guichard, J., Contreras, L.
M., Ceccarelli, D., Tantsura, J., and D. Shytyi, "SF Aware
TE Topology YANG Model", Work in Progress, Internet-Draft,
draft-ietf-teas-sf-aware-topo-model-11, 12 March 2023,
<https://datatracker.ietf.org/doc/html/draft-ietf-teas-sf-
aware-topo-model-11>.
[I-D.irtf-coinrg-use-cases]
Kunze, I., Wehrle, K., Trossen, D., Montpetit, M., de Foy,
X., Griffin, D., and M. Rio, "Use Cases for In-Network
Computing", Work in Progress, Internet-Draft, draft-irtf-
coinrg-use-cases-04, 30 June 2023,
<https://datatracker.ietf.org/doc/html/draft-irtf-coinrg-
use-cases-04>.
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[I-D.lcsr-alto-service-functions]
Contreras, L. M., Randriamasy, S., and X. Liu, "ALTO
extensions for handling Service Functions", Work in
Progress, Internet-Draft, draft-lcsr-alto-service-
functions-02, 13 March 2023,
<https://datatracker.ietf.org/doc/html/draft-lcsr-alto-
service-functions-02>.
[I-D.llc-teas-dc-aware-topo-model]
Lee, Y., Liu, X., and L. M. Contreras, "DC aware TE
topology model", Work in Progress, Internet-Draft, draft-
llc-teas-dc-aware-topo-model-03, 10 July 2023,
<https://datatracker.ietf.org/doc/html/draft-llc-teas-dc-
aware-topo-model-03>.
[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>.
[RFC8921] Boucadair, M., Ed., Jacquenet, C., Zhang, D., and P.
Georgatsos, "Dynamic Service Negotiation: The Connectivity
Provisioning Negotiation Protocol (CPNP)", RFC 8921,
DOI 10.17487/RFC8921, October 2020,
<https://www.rfc-editor.org/rfc/rfc8921>.
[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>.
[RFC9419] Arkko, J., Hardie, T., Pauly, T., and M. Kühlewind,
"Considerations on Application - Network Collaboration
Using Path Signals", RFC 9419, DOI 10.17487/RFC9419, July
2023, <https://www.rfc-editor.org/rfc/rfc9419>.
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Internet-Draft CLAS Evolution October 2023
[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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