Learn to Slice, Slice to Learn: Unveiling Online Optimization and Reinforcement Learning for Slicing AI Services

TL;DR

This paper aims to automate and optimize S2L by integrating the two concepts of L2S and S2L by using an intelligent slicing agent to solve S2L.

Abstract

In the face of increasing demand for zero-touch networks to automate network management and operations, two pivotal concepts have emerged: "Learn to Slice" (L2S) and "Slice to Learn" (S2L). L2S involves leveraging Artificial intelligence (AI) techniques to optimize network slicing for general services, while S2L centers on tailoring network slices to meet the specific needs of various AI services. The complexity of optimizing and automating S2L surpasses that of L2S due to intricate AI services' requirements, such as handling uncontrollable parameters, learning in adversarial conditions, and achieving long-term performance goals. This paper aims to automate and optimize S2L by integrating the two concepts of L2S and S2L by using an intelligent slicing agent to solve S2L. Indeed, we choose two candidate slicing agents, namely the Exploration and Exploitation (EXP3) and Deep Q-Network (DQN) from the Online Convex Optimization (OCO) and Deep Reinforcement Learning (DRL) frameworks, and compare them. Our evaluation involves a series of carefully designed experiments that offer valuable insights into the strengths and limitations of EXP3 and DQN in slicing for AI services, thereby contributing to the advancement of zero-touch network capabilities.

Figures (2)

• Figure 1: S2L architecture based on latest O-RAN architecture oranarch
• Figure 2: Convergence and adaptation performance of DQN and EXP3: (a) shows Case 1 convergence and adaption of the different models, (b) and (c) shows average accuracy for both algorithms along with steps equivalence for Case 1 and Case 2 respectively and (d) shows incurred costs for both cases.