VMAgent: Scheduling Simulator for Reinforcement Learning

• URL: http://arxiv.org/abs/2112.04785v1
• Date: Thu, 9 Dec 2021 09:18:38 GMT
• Title: VMAgent: Scheduling Simulator for Reinforcement Learning
• Authors: Junjie Sheng and Shengliang Cai and Haochuan Cui and Wenhao Li and Yun Hua and Bo Jin and Wenli Zhou and Yiqiu Hu and Lei Zhu and Qian Peng and Hongyuan Zha and Xiangfeng Wang
• Abstract summary: A novel simulator called VMAgent is introduced to help RL researchers better explore new methods. VMAgent is inspired by practical virtual machine (VM) scheduling tasks. From the VM scheduling perspective, VMAgent also helps to explore better learning-based scheduling solutions.
• Score: 44.026076801936874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling. VMAgent is inspired by practical virtual machine (VM) scheduling tasks and provides an efficient simulation platform that can reflect the real situations of cloud computing. Three scenarios (fading, recovering, and expansion) are concluded from practical cloud computing and corresponds to many reinforcement learning challenges (high dimensional state and action spaces, high non-stationarity, and life-long demand). VMAgent provides flexible configurations for RL researchers to design their customized scheduling environments considering different problem features. From the VM scheduling perspective, VMAgent also helps to explore better learning-based scheduling solutions.

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