Proceedings Article10.1145/3335484.3335513
DeepJS: Job Scheduling Based on Deep Reinforcement Learning in Cloud Data Center
Fengcun Li,Bo Hu +1 more
- 10 May 2019
- pp 48-53
82
TL;DR: The DeepJS, a job scheduling algorithm based on deep reinforcement learning under the framework of the bin packing problem, is presented and the results prove that DeepJS outperforms the heuristic-based job scheduling algorithms.
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Abstract: Job scheduling is a key building block of a cloud data center. Hand-crafted heuristics cannot automatically adapt to the change of the environment and optimize for specific workloads. We present the DeepJS, a job scheduling algorithm based on deep reinforcement learning under the framework of the bin packing problem. DeepJS can automatically obtain a fitness calculation method which will minimize the makespan (maximize the throughput) of a set of jobs directly from experience. Through a trace-driven simulation, we demonstrate the convergence and generalization of DeepJS and the essence of DeepJS learning. The results prove that DeepJS outperforms the heuristic-based job scheduling algorithms.
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Citations
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References
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
Dominant resource fairness: fair allocation of multiple resource types
Ali Ghodsi,Matei Zaharia,Benjamin Hindman,Andy Konwinski,Scott Shenker,Ion Stoica +5 more
- 30 Mar 2011
TL;DR: Dominant Resource Fairness (DRF), a generalization of max-min fairness to multiple resource types, is proposed, and it is shown that it leads to better throughput and fairness than the slot-based fair sharing schemes in current cluster schedulers.
1.3K
Resource Management with Deep Reinforcement Learning
Hongzi Mao,Mohammad Alizadeh,Ishai Menache,Srikanth Kandula +3 more
- 09 Nov 2016
TL;DR: This work presents DeepRM, an example solution that translates the problem of packing tasks with multiple resource demands into a learning problem, and shows that it performs comparably to state-of-the-art heuristics, adapts to different conditions, converges quickly, and learns strategies that are sensible in hindsight.
Multi-resource packing for cluster schedulers
TL;DR: In this paper, the authors present the likelihood of bottlenecks in modern data parallel clusters with highly diverse resource requirements, along CPU, memory, disk and network, along with the likelihood that any of these resources may become bottleneck.
Multi-resource packing for cluster schedulers
Robert Grandl,Ganesh Ananthanarayanan,Srikanth Kandula,Sriram Rao,Aditya Akella +4 more
- 17 Aug 2014
TL;DR: This work presents Tetris, a cluster scheduler that packs, i.e., matches multi-resource task requirements with resource availabilities of machines so as to increase cluster efficiency (makespan).