Fengcun Li
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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Index Terms
- DeepJS: Job Scheduling Based on Deep Reinforcement Learning in Cloud Data Center
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