Machine-learning-based cache partition method in cloud environment

doi:10.1007/s12083-021-01235-x

	Machine-learning-based cache partition method in cloud environment
	Qiu, Jiefan 1; Hua, Zonghan 1; Liu, Lei 2; Cao, Mingsheng 3; Chen, Dajiang 3,4
	2021-09-06
发表期刊	PEER-TO-PEER NETWORKING AND APPLICATIONS
ISSN	1936-6442
页码	14
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.
关键词	Cloud Cache Partition Last Level Cache Machine Learning
DOI	10.1007/s12083-021-01235-x
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Information Systems ; Telecommunications
WOS记录号	WOS:000692983300001
出版者	SPRINGER
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17162
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.Zhejiang Univ Technol, Coll Comp Sci & Technol, Hangzhou, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing, Peoples R China 3.Univ Elect Sci & Technol China, Network & Data Secur Key Lab Sichuan Prov, Chengdu 611731, 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.