Cited by 1,353Cloud Computing and Resource ManagementDistributed and Parallel Computing SystemsEnergy Efficiency in Computing

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Resource Management with Deep Reinforcement Learning

Mao, Hongzi, Alizadeh, Mohammad, Menache, Ishai, Kandula, Srikanth·2016·Proceedings of the 15th ACM Workshop on Hot Topics in Networks

DOI 10.1145/3005745.3005750

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Abstract

Resource management problems in systems and networking often manifest as difficult online decision making tasks where appropriate solutions depend on understanding the workload and environment. Inspired by recent advances in deep reinforcement learning for AI problems, we consider building systems that learn to manage resources directly from experience. We present DeepRM, an example solution that translates the problem of packing tasks with multiple resource demands into a learning problem. Our initial results show that DeepRM performs comparably to state-of-the-art heuristics, adapts to different conditions, converges quickly, and learns strategies that are sensible in hindsight.

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AutoDefense: Reinforcement Learning Based Autoreactive Defense Against Network Attacks
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Potential of Applying kNN with Soft Walltime to Improve Scheduling Performance
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A Data-Driven Approach to Dynamically Adjust Resource Allocation for Compute ClustersPreprint
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Learning Individual Potential-Based Rewards in Multiagent Reinforcement Learning
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AdaGap: An adaptive gap-aware resource allocation strategy for GPU sharing in heterogeneous clusters
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Optimizing Resource Scheduling in Computing Power Networks for Low-Consumption Big Data Analytics
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QoS aware deep reinforcement learning technique for task scheduling in cloud computing
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Deep Reinforcement Learning based Node Pairing Scheme in Edge-Chain for IoT Applications
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Policy Gradient-based Deep Reinforcement Learning for Deadline-aware Transfer over Wide Area Networks
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An Ensemble Learning-Based HPC Multi-Resource Demand Prediction Model for Hybrid Clusters
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Background

Authors: Mao, H., Alizadeh, M., Menache, I., & Kandula, S.
Affiliations: Massachusetts Institute of Technology (US)
Microsoft (United States) (US)
ORCID: Alizadeh, Mohammad
Menache, Ishai
Kandula, Srikanth
Venue: Proceedings of the 15th ACM Workshop on Hot Topics in Networks
Publisher: ACM
Published: 2016-11-09
Pages: 50-56
DOI: 10.1145/3005745.3005750
OpenAlex: W2546571074
Cited by: 1,353
References: 41
Citations by year: 2026: 171 · 2025: 181 · 2024: 98 · 2023: 140 · 2022: 151 · 2021: 191 · 2020: 180 · 2019: 155 · 2018: 62 · 2017: 24
FWCI: 68.24
Type: conference paper
Language: en
Open access: closed
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: ACM Workshop on Hot Topics in Networks (HotNets) (publishedVersion)
Topics: Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
Physical Sciences › Computer Science › Computer Networks and Communications › Distributed and Parallel Computing Systems
Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
Keywords: resource management, deep reinforcement learning, task scheduling, resource allocation, workload modeling, online decision making, experiential learning

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Resource Management with Deep Reinforcement Learning

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