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

arXiv: https://arxiv.org/abs/2408.08968 · HTML: https://mlsanity.com/paper/2408.08968/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [Selective Experience Replay for Lifelong Learning](https://mlsanity.com/paper/1802.10269/) (2018)
- [Online Learning: A Comprehensive Survey](https://mlsanity.com/paper/1802.02871/) (2018)
- [SLA Decomposition for Network Slicing: A Deep Neural Network Approach](https://mlsanity.com/paper/2407.15288/) (2024)

## Built on by (most cited first, 2 of 2)

- [RAILS: Risk-Aware Iterated Local Search for Joint SLA Decomposition and Service Provider Management in Multi-Domain Networks](https://mlsanity.com/paper/2502.06674/) (2025)
- [Cascaded Transformer for Robust and Scalable SLA Decomposition via Amortized Optimization](https://mlsanity.com/paper/2601.11859/) (2026)

## Abstract (arXiv)

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.