用AI方法解决边缘云应用迁移难题，提升服务质量与成本效益
Analysis of AI Techniques for Orchestrating Edge-Cloud Application Migration
- 基于马尔可夫决策过程，对比多种AI规划与强化学习方法
- 将迁移问题建模为汉诺塔问题，分析不同状态空间定义下的性能差异
- 为未来计算连续体环境中的自动化迁移提供技术参考
边缘-云系统中的应用迁移可实现高质量服务与低成本交付。然而，自动化的迁移编排通常依赖启发式方法。本文从马尔可夫决策过程（MDP）出发，识别、分析并比较了若干前沿人工智能（AI）规划与强化学习（RL）方法，用于求解可建模为汉诺塔（ToH）问题的边缘-云应用迁移场景。我们提出一种基于状态空间定义的新分类体系，并据此分析对比模型。研究旨在理解现有技术在新兴计算连续体环境中实现应用迁移编排的能力。
原文摘要 · Abstract (English)
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.
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