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ReAssigner: A Plug-and-Play Virtual Machine Scheduling Intensifier for Heterogeneous Requests

  • Haochuan Cui*
  • , Junjie Sheng
  • , Bo Jin
  • , Yiqiu Hu
  • , Li Su
  • , Lei Zhu
  • , Wenli Zhou
  • , Xiangfeng Wang
  • *此作品的通讯作者
  • East China Normal University
  • Huawei Cloud Computing Technologies

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

With the rapid development of cloud computing, virtual machine scheduling has become one of the most important but challenging issues for the cloud computing community, especially for practical heterogeneous request sequences. By analyzing the impact of request heterogeneity on some popular heuristic schedulers, it can be found that existing scheduling algorithms can not handle the request heterogeneity properly and efficiently. In this paper, a plug-and-play virtual machine scheduling intensifier, called Resource Assigner (ReAssigner), is proposed to enhance the scheduling efficiency of any given scheduler for heterogeneous requests. The key idea of ReAssigner is to pre-assign roles to physical resources and let resources of the same role form a virtual cluster to handle homogeneous requests. ReAssigner can cooperate with arbitrary schedulers by restricting their scheduling space to virtual clusters. With evaluations on the real dataset from Huawei Cloud, the proposed ReAssigner achieves significant scheduling performance improvement compared with some state-of-the-art scheduling methods.

源语言英语
主期刊名Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
编辑Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
出版商Institute of Electrical and Electronics Engineers Inc.
3726-3734
页数9
ISBN(电子版)9781665480451
DOI
出版状态已出版 - 2022
活动2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, 日本
期限: 17 12月 202220 12月 2022

出版系列

姓名Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

会议

会议2022 IEEE International Conference on Big Data, Big Data 2022
国家/地区日本
Osaka
时期17/12/2220/12/22

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