Analysis of AI Techniques for Orchestrating Edge-Cloud Application Migration

TL;DR

This paper reviews AI planning and reinforcement learning methods for automating edge-cloud application migration, focusing on their modeling, classification, and effectiveness in complex migration scenarios.

Contribution

It introduces a new classification framework for AI models based on state space and compares state-of-the-art techniques for edge-cloud migration tasks.

Findings

AI methods can effectively model migration as Towers of Hanoi problems

Reinforcement learning approaches show promising results in dynamic environments

Classification helps identify suitable AI techniques for specific migration challenges

Abstract

Application migration in edge-cloud system enables high QoS and cost effective service delivery. However, automatically orchestrating such migration is typically solved with heuristic approaches. Starting from the Markov Decision Process (MDP), in this paper, we identify, analyze and compare selected state-of-the-art Artificial Intelligence (AI) planning and Reinforcement Learning (RL) approaches for solving the class of edge-cloud application migration problems that can be modeled as Towers of Hanoi (ToH) problems. We introduce a new classification based on state space definition and analyze the compared models also through this lense. The aim is to understand available techniques capable of orchestrating such application migration in emerging computing continuum environments.