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Machine-learning-based cache partition method in cloud environment

文献类型：期刊论文

作者Qiu, Jiefan2; Hua, Zonghan2; Liu, Lei3; Cao, Mingsheng1; Chen, Dajiang1,4
刊名PEER-TO-PEER NETWORKING AND APPLICATIONS
出版日期2021-09-06
页码14
关键词Cloud Cache Partition Last Level Cache Machine Learning
ISSN号1936-6442
DOI10.1007/s12083-021-01235-x
英文摘要In the modern cloud environment, considering the cost of hardware and software resources, applications are often co-located on a platform and share such resources. However, co-located execution and resource sharing bring memory access conflict, especially in the Last Level Cache (LLC). In this paper, a lightweight method is proposed for partition LLC named by Classification-and-Allocation (C&A). Specifically, Support Vector Machine (SVM) is used in the proposed method to classify applications into the triple classes based on the performance change characteristic (PCC), and the Bayesian Optimizer (BO) is leveraged to schedule LLC to guarantee applications with the same PCC sharing the same part of LLC. Since the near-optimal partition can be found efficiently by leveraging BO-based scheduling with a few sampling steps, C&A can handle unseen and versatile workloads with low overhead. We evaluate the proposed method in several workloads. Experimental results show that C&A can outperform the state-of-art method KPart (El-Sayed et al in Proceedings of 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA) 104-117, 2018) by 7.45% and 22.50% respectively in overall system throughput and fairness, and reduces 20.60% allocation overhead.
资助项目National Key Research and Development Project[2018YFB1402800] ; Zhejiang Provincial Natural Science Foundation of China[LY20F020026] ; project The Verification Platform of Multi-tier Coverage Communication Network for oceans[LZC0020] ; NSFC[61872059]
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:000692983300001
出版者SPRINGER
源URL[http://119.78.100.204/handle/2XEOYT63/17161]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Chen, Dajiang
作者单位1.Univ Elect Sci & Technol China, Network & Data Secur Key Lab Sichuan Prov, Chengdu 611731, Peoples R China
2.Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing, Peoples R China
4.Peng Cheng Lab, Shenzhen 518055, Peoples R China
推荐引用方式
GB/T 7714		 Qiu, Jiefan,Hua, Zonghan,Liu, Lei,et al. Machine-learning-based cache partition method in cloud environment[J]. PEER-TO-PEER NETWORKING AND APPLICATIONS,2021:14.
APA Qiu, Jiefan,Hua, Zonghan,Liu, Lei,Cao, Mingsheng,&Chen, Dajiang.(2021).Machine-learning-based cache partition method in cloud environment.PEER-TO-PEER NETWORKING AND APPLICATIONS,14.
MLA Qiu, Jiefan,et al."Machine-learning-based cache partition method in cloud environment".PEER-TO-PEER NETWORKING AND APPLICATIONS (2021):14.

入库方式： OAI收割

来源：计算技术研究所

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