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

TL;DR

This paper introduces an online learning framework for real-time SLA decomposition in multi-domain network slices, improving adaptability and accuracy over static methods by continuously updating risk models with recent data.

Contribution

It extends previous work by developing an online, adaptive approach using gradient descent and FIFO buffers for dynamic SLA decomposition in evolving network systems.

Findings

Outperforms static SLA decomposition methods in accuracy and resilience.

Demonstrates robustness under data limitations and changing network conditions.

Provides a detailed complexity analysis of the proposed framework.

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.