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 This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet- Drafts is at https://datatracker.ietf.org/drafts/current/. 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Expires 4 September 2023 [Page 1] Internet-Draft CLAS Evolution March 2023 extracted from this document must include Revised BSD License text as described in Section 4.e of the Trust Legal Provisions and are provided without warranty as described in the Revised BSD License. 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. Contreras, et al. Expires 4 September 2023 [Page 2] Internet-Draft CLAS Evolution March 2023 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. Contreras, et al. Expires 4 September 2023 [Page 3] Internet-Draft CLAS Evolution March 2023 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}} Contreras, et al. Expires 4 September 2023 [Page 4] Internet-Draft CLAS Evolution March 2023 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 Contreras, et al. Expires 4 September 2023 [Page 5] Internet-Draft CLAS Evolution March 2023 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 || || | Contreras, et al. Expires 4 September 2023 [Page 6] Internet-Draft CLAS Evolution March 2023 | 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 Contreras, et al. Expires 4 September 2023 [Page 7] Internet-Draft CLAS Evolution March 2023 * 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, . [RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174, May 2017, . 9.2. Informative References Contreras, et al. Expires 4 September 2023 [Page 8] Internet-Draft CLAS Evolution March 2023 [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, . [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, . [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, . [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, . [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, . [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, . [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, . Contreras, et al. Expires 4 September 2023 [Page 9] Internet-Draft CLAS Evolution March 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 Contreras, et al. Expires 4 September 2023 [Page 10]