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Resource Management with Deep Reinforcement Learning
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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“Recently, RL combined with deep-learning has been shown as a promising alternative [294] that can be leveraged to tackle various resource management problems in practical settings.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [294]
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Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al. (2010) · Communications of the ACM · doi:10.1145/1721654.1721672 · Cited by 9,464
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Asynchronous Methods for Deep Reinforcement Learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1602.01783 · Cited by 7,132
Introduction to Reinforcement Learning
Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855
Research works printed but not linked: 20
- Agarwal S., 2012, NSDI, 281
- Ananthanarayanan G., 2010, In OSDI, number, 1, 24
- Bertsekas D. P., 1995, Decision and Control
- Chilimbi T., 2014, OSDI, 571
- Dong M., 2015, NSDI, 395
- Ghodsi A., 2011, NSDI'11, 323
- Heller B., 2010, NSDI'10
- Junchen J., 2015, SIGCOMM '15
- Kaelbling L. P., 1996, Journal of artificial intelligence research
- Mahadevan S., 1998, FLAIRS Conference
- Mnih V., 2016, CoRR
- Mnih V., 2013, CoRR
- Mnih V., 2015, Nature
- J. Schulman S. Levine P. Moritz M. I. Jordan and P. Abbeel. Trust region policy optimization. CoRR abs/1502.05477 2015. J. Schulman S. Levine P. Moritz M. I. Jordan and P. Abbeel. Trust region policy optimization. CoRR abs/1502.05477 2015. · arXiv
- 10.5555/551283 · DOI
- Sutton R. S., 1999, NIPS
- Winstein K., 2013, NSDI, 459
- Yi S., 2016, SIGCOMM
- Yin X., 2016, SIGCOMM '16
- Zhang W., 1995, IJCAI. Citeseer
Cited by
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BackgroundRAPID: Enabling fast online policy learning in dynamic public cloud environments
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BackgroundKaur, U., & Shalu, S. (2021) · Springer eBooks · doi:10.1007/978-3-030-67490-8_17 · Cited by 3
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Vrbaski, M., Bolić, M., & Majumdar, S. (2022) · International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud57274.2022.00026 · Cited by 3
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Jeong, Y.-S., & Yon, Y.-H. (2020) · Personal and Ubiquitous Computing · doi:10.1007/s00779-020-01459-3 · Cited by 3
“Mao et al. proposed solutions that translate the parts required by different resources into RL problems in the network environment; the RL models have experimented with showing performance similar to the state-of-the-art Huystic solution [20].”
MethodA Deep Q-Learning Approach for GPU Task Scheduling
Luley, R. S., & Qiu, Q. (2020) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec43674.2020.9286238 · Cited by 3
“Mao et al. [21] applies the DRL method to a resource management problem, which is similar to our proposed solution, called DeepRM.”
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Krämer, P., Diederich, P., Kramer, C., Pries, R., Kellerer, W., & Blenk, A. (2021) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 3
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“optimization of overall pipelines using, among others, meta-reasoning techniques to ensure an overall optimization of the pipeline [1, 18, 21].”
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Scheinert, D., Becker, S., Bader, J., Thamsen, L., Will, J., & Kao, O. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020860 · Cited by 3
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Filho, J. S., Cavalcante, D. M., Moreira, L. O., & Machado, J. C. (2020) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5675 · Cited by 3
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BackgroundMethodAn End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing PlatformsPreprint
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Liu, L. (2019) · arXiv (Cornell University) · Cited by 3
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Kao, S.-C., & Krishna, T. (2021) · arXiv (Cornell University) · Cited by 3
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Dridi, S. (2024) · doi:10.31219/osf.io/svykj · Cited by 3
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Dereventsov, A., Daws, J., & Webster, C. (2024) · Lecture notes in computational science and engineering · doi:10.1007/978-3-031-55060-7_5 · Cited by 4
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“However, the inspiration for our work has been from the work of Hongzi M. et al. in [22] wherein the state space has been represented pictorially using tabular representation for each space and then fed into a deep reinforcement learning network to find the optimal scheduling policy for a given…”
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BackgroundYuan, Y., Lu, T., Xue, J., & Song, Y. (2024) · World Symposium on Software Engineering (WSSE) · doi:10.1145/3698062.3698099 · Cited by 2
“We utilize a partially observable Markov decision process(POMDP) as the modeling framework for multi-agent reinforcement learning [11].”
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Yang, G., Wu, W., Sun, A., Sun, Y., Sun, T., & Si, P. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3565898 · Cited by 2
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BackgroundLi, B., Qiu, S., Ye, H., Guo, Y., Wang, H., & Bai, J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109673 · Cited by 2
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“In this regard, various methodologies have been explored for a number of years with the aim of minimizing some of these challenges, and as a consequence model-free controllers have emerged [1]-[5].”
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Can LLMs only talk? Experimental studies on task scheduling with Large Language Models
Li, M., Chen, Z., Zhou, H., Wang, Z., Chen, Y., Zhao, B., Ouyang, X., & Su, J. (2025) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn65249.2025.11133882 · Cited by 2
Piratla, V. S. A., Saxena, S., Bhatia, S., & Kumar, N. (2025) · Intelligent Cybersecurity Conference (ICSC) · doi:10.1109/icsc65596.2025.11140338 · Cited by 4
“Reinforcement learning optimizes routing in real-time [99].”
Yang, B., Liu, B., Han, Y., Meng, X., Wang, Y., Yang, H., & Xiao, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103996 · Cited by 3
Deep Reinforcement Learning Based Sensor Data Management for Vehicles
Moon, J., Cheong, M., Yeom, I., & Woo, H. (2019) · International Conference on Information Networking · doi:10.1109/icoin.2019.8718108 · Cited by 2
Speeding up AutoTuning of the Memory Management Options in Data Analytics
Kunjir, M. (2020) · Distributed and Parallel Databases · doi:10.1007/s10619-019-07281-y · Cited by 2
“Recent attempts at auto-tuning systems have either focussed on building What-If performance models [17, 40, 42] or, more popularly, on training ML-based performance models [2, 15, 28, 44–46, 50, 52].”
BackgroundA distributed reinforcement learning approach for energy and congestion-aware edge networks
Sacco, A., Esposito, F., & Marchetto, G. (2020) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/3386367.3431670 · Cited by 2
Chen, Q., Zhu, M., Shen, T., Gu, J., Yan, C., & Gu, P. (2020) · Asia Communications and Photonics Conference · doi:10.1364/acpc.2020.m4a.185 · Cited by 2
“Recently, reinforce learning (RL) has demonstrated fabulous level in large-scale control tasks and successfully been applied in the field of resource allocation problems such as multi-resource management in computer systems [5].”
BackgroundTenant-Aware Slice Admission Control Using Neural Networks-Based Policy Agent
Batista, P., Khan, S. N., Öhlén, P., & Junior, A. B. D. R. K. (2019) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-25748-4_2 · Cited by 2
“Aligned with previous work [7, 10], our RL agent is using a policy network, which has a configurable number of inputs and hidden layers and two outputs, representing the probability of accepting and the probability of rejecting the slice.”
MethodEfficient Practice for Deep Reinforcement Learning
Kancharla, V. S. S. R. T., & Lee, M. (2019) · IEEE Symposium Series on Computational Intelligence · doi:10.1109/ssci44817.2019.9003129 · Cited by 1
“Combining deep learning with RL, recent advances in deep reinforcement learning (DRL) has lead popularity in various application areas like natural language processing [1], resource management [2], and robotics [3].”
BackgroundLiu, Z., Wang, L., & Quan, G. (2020) · Proceedings of the International Conference on Parallel Processing · doi:10.1145/3404397.3404475 · Cited by 2
“[14] presents “DeepRM” that utilizes reinforcement learning for obtaining a resource manager to coordinate workload execution in a cluster for improving execution efficiency.”
BackgroundThe Ecological Model and Global optimization Algorithm for Service Internet
Jia, Z., Huang, S., & Fan, Y. (2020) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case48305.2020.9216876 · Cited by 2
“Mao et al. [27] provided a resource management solution based on deep reinforcement learning, which transforms the task packing problem of multi-resource requirements into a learning problem.”
BackgroundDynamic allocation of limited memory resources in reinforcement learning
Patel, N., Acerbi, L., & Pouget, A. (2020) · Archive ouverte UNIGE (University of Geneva) · doi:10.48550/arxiv.2011.06387 · Cited by 2
“Finally, it is worth noting that our work fundamentally differs from previous work in RL applied to external resource allocation [20, 21] in that we are using RL to optimize the computational resources of the agent itself.”
MethodAutomated Functional Test Generation Practice for a Large-Scale Android Application
Köroglu, Y., Şen, A., & Akin, A. (2020) · Turkish National Software Engineering Symposium (UYMS) · doi:10.1109/uyms50627.2020.9247060 · Cited by 2
“Destekli ö ˘ grenme kaynak yönetiminden [4] trafik ı¸sı ˘ gı kontrolüne [5], satranç [6] ve atari [7] gibi karma¸sık oyunlardan kimyaya [8] kadar çok geni¸s bir alanda insan becerisinin ötesine geçmi¸stir.”
Wang, C., Yoshikane, N., Balasis, F., & Tsuritani, T. (2020) · European Conference on Optical Communication · doi:10.1109/ecoc48923.2020.9333250 · Cited by 2
“According to the earlier analysis, and following the trick from Mao et al. [7] , the action can be given as 𝑎 % = 𝑜 , where 𝑜 is from {1,2, … , 𝑁 $ − 𝑁} denotes the scheduling order of the 𝑛 -th request.”
BackgroundMethodLearning-Aided Heuristics Design for Storage System
Tang, Y., Han, L., Li, X., Chen, L., Yuan, M., & Zeng, J. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3457554 · Cited by 2
“[5] apply deep reinforcement learning (DRL) on resource management problems.”
BackgroundPOIRON-GUIDONI, N., & Bisgambiglia, P.-A. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534024 · Cited by 2
“The management of common pool resources (CPR) systems, seems appropriate for its use, especially using multi-agent deep learning methods [18], [21], [31].”
BackgroundA Learning-Based Scheduler for High Volume Processing in Data Warehouse Using Graph Neural Networks
Bengre, V. L., HoseinyFarahabady, M. R., Pivezhandi, M., Zomaya, A. Y., & Jannesari, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-96772-7_17 · Cited by 2
“On the other hand, the general data processing approaches focus on separation of the general jobs into multiple stages and tasks regardless of their intrinsic behavior [11, 14, 15].”
BackgroundKalyaev, I., & Kalyaev, A. (2022) · Automation and Remote Control · doi:10.1134/s0005117922080069 · Cited by 2
Task Scheduling Based on Adaptive Priority Experience Replay on Cloud Platforms
Li, C.-X., Gao, W., Shi, L., Shang, Z., & Zhang, S. (2023) · Electronics · doi:10.3390/electronics12061358 · Cited by 2
Analysis of Dynamic Scheduling for Edge Cloud Computing
Vanipenta, C. S. R., Bhukya, S. R., Koppula, T. R., Nelavelli, C. S., Vignesh, T., & Bulla, S. (2023) · International Conference on Computer Communication and Informatics (ICCCI) · doi:10.1109/iccci56745.2023.10128455 · Cited by 2
“This process is repeated until all processes have been executed [11].”
BackgroundMASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
Peng, Q., & Wang, S. (2023) · Electronics · doi:10.3390/electronics12194056 · Cited by 2
DeepSDN: Connecting the Dots Towards Self-driving Networks
Atwal, K. S., & Bassiouni, M. A. (2018) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/pccc.2018.8711025 · Cited by 2
Video Game Design and Performance Analysis Based on Reinforcement Learning
Lei, L., Han, M., Sun, Y., & Yang, H. (2019) · International Joint Conference on Information, Media and Engineering (IJCIME) · doi:10.1109/ijcime49369.2019.00053 · Cited by 2
“In addition, RL has gradually improved the interaction between Chabot and users by replacing decision tree algorithms [7].”
BackgroundMARS: Multi-Scalable Actor-Critic Reinforcement Learning Scheduler
Baheri, B. (2020) · OhioLink ETD Center (Ohio Library and Information Network) · Cited by 2
Flow Scheduling in a Heterogeneous NFV Environment using Reinforcement Learning
Lin, C., Luo, Y., Wang, L., & Chen, L.-D. (2021) · IEEE International Conference on Networking, Architecture and Storage (NAS) · doi:10.1109/nas51552.2021.9605395 · Cited by 2
OnlineDC: Leveraging Temporal Driving Behavior to Facilitate Driver Classification
Abu-gellban, H., Zhuang, Y., Nguyen, L., Jin, F., & Zhang, Z. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671591 · Cited by 2
Metadata
- Authors
- Mao, H., Alizadeh, M., Menache, I., & Kandula, S.
- Affiliations
- Massachusetts Institute of Technology (US)
- Microsoft (United States) (US)
- Venue
- Proceedings of the 15th ACM Workshop on Hot Topics in Networks
- Publisher
- ACM
- Published
- 2016-11-09
- Pages
- 50-56
- 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
- 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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