# SLA Decomposition for Network Slicing: A Deep Neural Network Approach

arXiv: https://arxiv.org/abs/2407.15288 · HTML: https://mlsanity.com/paper/2407.15288/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2024.

## Built on by (most cited first, 3 of 3)

- [Online SLA Decomposition: Enabling Real-Time Adaptation to Evolving Network Systems](https://mlsanity.com/paper/2408.08968/) (2024)
- [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)

For a network slice that spans multiple technology and/or administrative domains, these domains must ensure that the slice's End-to-End (E2E) Service Level Agreement (SLA) is met. Thus, the E2E SLA should be decomposed to partial SLAs, assigned to each of these domains. Assuming a two level management architecture consisting of an E2E service orchestrator and local domain controllers, we consider that the former is only aware of historical data of the local controllers' responses to previous slice requests, and captures this knowledge in a risk model per domain. In this study, we propose the use of Neural Network (NN) based risk models, using such historical data, to decompose the E2E SLA. Specifically, we introduce models that incorporate monotonicity, applicable even in cases involving small datasets. An empirical study on a synthetic multidomain dataset demonstrates the efficiency of our approach.