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Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC)

Masoud Shokrnezhad, Hao Yu, Tarik Taleb, Richard Li, Kyunghan Lee, Jaeseung Song, Cedric Westphal

TL;DR

The paper addresses the challenge of provisioning computing and networking resources under stringent QoS/E in 6G for Metaverse-like services. It introduces Adaptable CNC (ACNC), a framework that combines continual learning with joint orchestration, uses dimensionality reduction and context detection to manage dynamic states, and integrates an End-to-End orchestrator with SRv6-based deterministic routing. Key contributions include the ACNC architecture (Resources, Domain Orchestrators, E2E Orchestrator, Service Orchestrator), a graph-based RL approach for path and instance selection, and a Metaverse scenario demonstrating deterministic provisioning via SRv6. The findings show that graph-aware DDQL agents can achieve near-optimal energy efficiency and higher profits than baselines, supporting scalable MaaS provisioning in highly dynamic environments, with future work focusing on digital twins and rigorous end-to-end evaluation.

Abstract

In the context of advancing 6G, a substantial paradigm shift is anticipated, highlighting comprehensive everything-to-everything interactions characterized by numerous connections and stringent adherence to Quality of Service/Experience (QoS/E) prerequisites. The imminent challenge stems from resource scarcity, prompting a deliberate transition to Computing-Network Convergence (CNC) as an auspicious approach for joint resource orchestration. While CNC-based mechanisms have garnered attention, their effectiveness in realizing future services, particularly in use cases like the Metaverse, may encounter limitations due to the continually changing nature of users, services, and resources. Hence, this paper presents the concept of Adaptable CNC (ACNC) as an autonomous Machine Learning (ML)-aided mechanism crafted for the joint orchestration of computing and network resources, catering to dynamic and voluminous user requests with stringent requirements. ACNC encompasses two primary functionalities: state recognition and context detection. Given the intricate nature of the user-service-computing-network space, the paper employs dimension reduction to generate live, holistic, abstract system states in a hierarchical structure. To address the challenges posed by dynamic changes, Continual Learning (CL) is employed, classifying the system state into contexts controlled by dedicated ML agents, enabling them to operate efficiently. These two functionalities are intricately linked within a closed loop overseen by the End-to-End (E2E) orchestrator to allocate resources. The paper introduces the components of ACNC, proposes a Metaverse scenario to exemplify ACNC's role in resource provisioning with Segment Routing v6 (SRv6), outlines ACNC's workflow, details a numerical analysis for efficiency assessment, and concludes with discussions on relevant challenges and potential avenues for future research.

Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC)

TL;DR

The paper addresses the challenge of provisioning computing and networking resources under stringent QoS/E in 6G for Metaverse-like services. It introduces Adaptable CNC (ACNC), a framework that combines continual learning with joint orchestration, uses dimensionality reduction and context detection to manage dynamic states, and integrates an End-to-End orchestrator with SRv6-based deterministic routing. Key contributions include the ACNC architecture (Resources, Domain Orchestrators, E2E Orchestrator, Service Orchestrator), a graph-based RL approach for path and instance selection, and a Metaverse scenario demonstrating deterministic provisioning via SRv6. The findings show that graph-aware DDQL agents can achieve near-optimal energy efficiency and higher profits than baselines, supporting scalable MaaS provisioning in highly dynamic environments, with future work focusing on digital twins and rigorous end-to-end evaluation.

Abstract

In the context of advancing 6G, a substantial paradigm shift is anticipated, highlighting comprehensive everything-to-everything interactions characterized by numerous connections and stringent adherence to Quality of Service/Experience (QoS/E) prerequisites. The imminent challenge stems from resource scarcity, prompting a deliberate transition to Computing-Network Convergence (CNC) as an auspicious approach for joint resource orchestration. While CNC-based mechanisms have garnered attention, their effectiveness in realizing future services, particularly in use cases like the Metaverse, may encounter limitations due to the continually changing nature of users, services, and resources. Hence, this paper presents the concept of Adaptable CNC (ACNC) as an autonomous Machine Learning (ML)-aided mechanism crafted for the joint orchestration of computing and network resources, catering to dynamic and voluminous user requests with stringent requirements. ACNC encompasses two primary functionalities: state recognition and context detection. Given the intricate nature of the user-service-computing-network space, the paper employs dimension reduction to generate live, holistic, abstract system states in a hierarchical structure. To address the challenges posed by dynamic changes, Continual Learning (CL) is employed, classifying the system state into contexts controlled by dedicated ML agents, enabling them to operate efficiently. These two functionalities are intricately linked within a closed loop overseen by the End-to-End (E2E) orchestrator to allocate resources. The paper introduces the components of ACNC, proposes a Metaverse scenario to exemplify ACNC's role in resource provisioning with Segment Routing v6 (SRv6), outlines ACNC's workflow, details a numerical analysis for efficiency assessment, and concludes with discussions on relevant challenges and potential avenues for future research.
Paper Structure (22 sections, 6 figures, 1 table)

This paper contains 22 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Fundamentals
  3. The Interwoven Cloud-Network Harmony
  4. The Horizon of Ever-Changing Services
  5. The Wisdom of Continuous Conetx-Awareness
  6. Adaptable Computing-Network Convergence
  7. The Parties Involved
  8. The Architecture of Adaptable Converged Orchestration
  9. Resources
  10. Domain Orchestrators
  11. The E2E Orchestrator
  12. The Service Orchestrator
  13. Integrating ACNC with SRv6
  14. The Workflow
  15. Performance Evaluation
  16. ...and 7 more sections

Figures (6)

  • Figure 1: A lakeside holographic meeting room in the Metaverse enabled by a cloud-network integrated infrastructure powered by technologies including deterministic networking, time-sensitive networking, and intelligent medium access control, with users connected via 6G connections shokrnezhad2023TMCCLMA.
  • Figure 2: The Architecture of Adaptable Computing-Network Convergence (ACNC).
  • Figure 3: The process of state construction and reduction within the ACNC framework.
  • Figure 4: A) A typical Metaverse use case incorporating an ACNC-enabled holographic meeting, and B) the demonstration of deterministic service provisioning enabled by SRv6 in ACNC.
  • Figure 5: The flowchart of E2E orchestration within the ACNC framework.
  • ...and 1 more figures