# Analysis of AI Techniques for Orchestrating Edge-Cloud Application Migration

arXiv: https://arxiv.org/abs/2507.10119 · HTML: https://mlsanity.com/paper/2507.10119/

## 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.

## Builds on (most cited first, 8 of 8)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [A comparison of Vector Symbolic Architectures](https://mlsanity.com/paper/2001.11797/) (2020)
- [Plansformer: Generating Symbolic Plans using Transformers](https://mlsanity.com/paper/2212.08681/) (2022)
- [Forward-Backward Reinforcement Learning](https://mlsanity.com/paper/1803.10227/) (2018)
- [Continuous QoS-compliant Orchestration in the Cloud-Edge Continuum](https://mlsanity.com/paper/2310.02985/) (2023)
- [AI Planning: A Primer and Survey (Preliminary Report)](https://mlsanity.com/paper/2412.05528/) (2024)
- [A Taxonomy of Live Migration Management in Cloud Computing](https://mlsanity.com/paper/2112.02593/) (2021)

## 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.