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Analysis of AI Techniques for Orchestrating Edge-Cloud Application Migration

arXiv 2507.10119 ↗First posted 14 Jul 2025cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 14 Jul 2025.

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References · 8

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
  3. 2020A comparison of Vector Symbolic Architecturescited by 64
  4. 2022Plansformer: Generating Symbolic Plans using Transformerscited by 41
  5. 2018Forward-Backward Reinforcement Learningcited by 19
  6. 2023Continuous QoS-compliant Orchestration in the Cloud-Edge Continuumcited by 3
  7. 2024AI Planning: A Primer and Survey (Preliminary Report)cited by 3
  8. 2021A Taxonomy of Live Migration Management in Cloud Computingcited by 1

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  4. 2025Machine learning-based cloud resource allocation algorithms: a comprehensive comparative review
  5. 2025Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems
  6. 2025Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
  7. 2025Governed By Agents: A Survey On The Role Of Agentic AI In Future Computing Environments
  8. 2025Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review