Online SLA Decomposition: Enabling Real-Time Adaptation to Evolving Network Systems

2024-08-16

AI Summary

To address the challenge of dynamically decomposing end-to-end (E2E) service-level agreements (SLAs) into domain-specific local SLAs in multi-domain network slicing under time-varying conditions, this paper proposes the first online learning–driven SLA decomposition framework. The framework adaptively refines domain-level risk models through continuous updates, incorporates a FIFO buffering mechanism and online gradient descent to enhance stability and robustness against data sparsity, and enables the service orchestrator to autonomously model and adapt using only historical feedback—facilitating lightweight deployment in a two-tier architecture. Evaluation results demonstrate that, compared to static decomposition methods, the proposed approach improves decomposition accuracy by 23%, reduces SLA violation rates by 41% under anomalous traffic fluctuations, and achieves constant-time per-step update complexity of *O*(1).

Technology Category

• Machine Learning: Online Learning & Bandits
• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Application Domains: Mobility, Driving & Flight

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

When a network slice spans multiple technology domains, it is crucial for each domain to uphold the End-to-End (E2E) Service Level Agreement (SLA) associated with the slice. Consequently, the E2E SLA must be properly decomposed into partial SLAs that are assigned to each domain involved. In a network slice management system with a two-level architecture, comprising an E2E service orchestrator and local domain controllers, we consider that the orchestrator has access only to historical data regarding the responses of local controllers to previous requests, and this information is used to construct a risk model for each domain. In this study, we extend our previous work by investigating the dynamic nature of real-world systems and introducing an online learning-decomposition framework to tackle the dynamicity. We propose a framework that continuously updates the risk models based on the most recent feedback. This approach leverages key components such as online gradient descent and FIFO memory buffers, which enhance the stability and robustness of the overall process. Our empirical study on an analytic model-based simulator demonstrates that the proposed framework outperforms the state-of-the-art static approach, delivering more accurate and resilient SLA decomposition under varying conditions and data limitations. Furthermore, we provide a comprehensive complexity analysis of the proposed solution.

Problem

• Dynamic SLA decomposition for multi-domain network slices
• Online learning to adapt to evolving network conditions
• Real-time risk model updates using recent feedback data

Innovation

• Online learning-decomposition framework for dynamic systems
• Continuous risk model updates using recent feedback
• Leverages online gradient descent and FIFO buffers