Research Article
A Deep Reinforcement Learning Based Resource Autonomic Provisioning Approach for Cloud Services
@INPROCEEDINGS{10.1007/978-3-030-67540-0_8, author={Qing Zong and Xiangwei Zheng and Yi Wei and Hongfeng Sun}, title={A Deep Reinforcement Learning Based Resource Autonomic Provisioning Approach for Cloud Services}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2021}, month={1}, keywords={Cloud computing DRL Resource scheduling Autonomic computing}, doi={10.1007/978-3-030-67540-0_8} }- Qing Zong
Xiangwei Zheng
Yi Wei
Hongfeng Sun
Year: 2021
A Deep Reinforcement Learning Based Resource Autonomic Provisioning Approach for Cloud Services
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-030-67540-0_8
Abstract
Resource elastic scheduling is a key feature of cloud services. The elastic makes cloud services have the ability to flexibly increase or decrease resources to satisfy user needs, and dynamically allocate resources for cloud services on demand. The amount of resources to be configured is determined at runtime based on the changes in workload to flexibly respond to the fluctuating demands of cloud services. Appropriate resources need to be configured in advance. In this article, we propose a dynamic resource provisioning framework based on the MAPE loop, and use a two-tier elastic resource configuration for collaborative work. In order to implement the proposed framework, we propose an elastic resource scheduling algorithm based on a combination of the autonomic computing and deep reinforcement learning (DRL) to reduce task rejection rate of the virtual machine (VM) and increase utilization to obtain as much profit as possible. In this paper, Experimental results using actual Google cluster tracking results show that the proposed policy reduces the total cost about 17%–58% and increases the profit by up to not less than 9%, reduces the service level agreement (SLA) violations to less than 0.4% to better guarantee the quality of service (QoS).
