mlsanityData to 14 Oct 2026

Towards VM Rescheduling Optimization Through Deep Reinforcement Learning

arXiv 2505.17359 ↗First posted 23 May 2025cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Modern industry-scale data centers need to manage a large number of virtual machines (VMs). Due to the continual creation and release of VMs, many small resource fragments are scattered across physical machines (PMs). To handle these fragments, data centers periodically reschedule some VMs to alternative PMs, a practice commonly referred to as VM rescheduling. Despite the increasing importance of VM rescheduling as data centers grow in size, the problem remains understudied. We first show that, unlike most combinatorial optimization tasks, the inference time of VM rescheduling algorithms significantly influences their performance, due to dynamic VM state changes during this period. This causes existing methods to scale poorly. Therefore, we develop a reinforcement learning system for VM rescheduling, VM2RL, which incorporates a set of customized techniques, such as a two-stage framework that accommodates diverse constraints and workload conditions, a feature extraction module that captures relational information specific to rescheduling, as well as a risk-seeking evaluation enabling users to optimize the trade-off between latency and accuracy. We conduct extensive experiments with data from an industry-scale data center. Our results show that VM2RL can achieve a performance comparable to the optimal solution but with a running time of seconds. Code and datasets are open-sourced: https://github.com/zhykoties/VMR2L_eurosys, https://drive.google.com/drive/folders/1PfRo1cVwuhH30XhsE2Np3xqJn2GpX5qy.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 27 of its 66 references that are in the map. First posted on arXiv 23 May 2025.

Builds on

27 of 66 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024A Safe and Data-efficient Model-based Reinforcement Learning System for HVAC Control16 Jul
  2. 2024Go Beyond Black-box Policies: Rethinking the Design of Learning Agent for Interpretable and Verifiable HVAC Control29 Feb

Other references · 25

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  7. 2019Exact Combinatorial Optimization with Graph Convolutional Neural Networkscited by 318
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  9. 2020Reinforcement Learning for Combinatorial Optimization: A Surveycited by 236
  10. 2018Learning by Playing - Solving Sparse Reward Tasks from Scratchcited by 167
  11. 2018Learning Scheduling Algorithms for Data Processing Clusterscited by 134
  12. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 120
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  2. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 88
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  4. 2017Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Methodcited by 31
  5. 2020Reinforcement Learning for Variable Selection in a Branch and Bound Algorithmcited by 29
  6. 2020AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learningcited by 24
  7. 2021Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attentioncited by 13
  8. 2021Solving Large-Scale Granular Resource Allocation Problems Efficiently with POPcited by 13
  9. 2019Reinforcement Learning Driven Heuristic Optimizationcited by 12
  10. 2023Multi-zone HVAC Control with Model-Based Deep Reinforcement Learningcited by 6
  11. 2023Optimizing Irrigation Efficiency using Deep Reinforcement Learning in the Fieldcited by 6
  12. 2023Exploring Deep Reinforcement Learning for Holistic Smart Building Controlcited by 4
  13. 2024MARLP: Time-series Forecasting Control for Agricultural Managed Aquifer Rechargecited by 3

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