PEFS: AI-Driven Prediction Based Energy-Aware Fault-Tolerant Scheduling Scheme for Cloud Data Center

doi:10.1109/TSUSC.2020.3015559

	PEFS: AI-Driven Prediction Based Energy-Aware Fault-Tolerant Scheduling Scheme for Cloud Data Center
	Marahatta, Avinab 1,2; Xin, Qin 3; Chi, Ce 1,2; Zhang, Fa 2; Liu, Zhiyong 2
	2021-10-01
发表期刊	IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING
ISSN	2377-3782
卷号	6 期号:4 页码:655-666
摘要	Cloud data centers (CDCs) have become increasingly popular and widespread in recent years with the growing popularity of cloud computing and high-performance computing. Due to the multi-step computation of data streams and heterogeneous task dependencies, task failure frequently occurs, resulting in poor user experience and additional energy consumption. To reduce task execution failure as well as energy consumption, we propose a novel AI-driven energy-aware proactive fault-tolerant scheduling scheme for CDCs in this paper. First, a prediction model based on the machine learning approach is trained to classify the arriving tasks into "failure-prone tasks" and "non-failure-prone tasks" according to the predicted failure rate. Then, two efficient scheduling mechanisms are proposed to allocate two types of tasks to the most appropriate hosts in a CDC. The vector reconstruction method is developed to construct super tasks from failure-prone tasks and separately schedule these super tasks and non-failure-prone tasks to the most suitable physical host. All the tasks are scheduled in an earliest-deadline-first manner. Our evaluation results show that the proposed scheme can intelligently predict task failure and achieves better fault tolerance and reduces total energy consumption better than the existing schemes.
关键词	Energy efficiency Deep learning Fault tolerant systems Energy consumption Scheduling Cloud computing Predictive models Neural networks Cloud computing cloud data center scheduling fault-tolerance energy-efficiency task failure prediction deep neural network
DOI	10.1109/TSUSC.2020.3015559
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61520106005] ; National Natural Science Foundation of China[61761136014] ; National Key Research and Development Program of China[2017YFB1010001] ; CAS-TWAS President's Fellowship
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Information Systems ; Telecommunications
WOS记录号	WOS:000728136400010
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：20[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/2XEOYT63/18049
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Marahatta, Avinab
作者单位	1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, High Performance Comp Res Ctr, Inst Comp Technol, Beijing 100190, Peoples R China 3.Univ Faroe Isl, FR-100 Torshavn, Faroe Islands
推荐引用方式 GB/T 7714	Marahatta, Avinab,Xin, Qin,Chi, Ce,et al. PEFS: AI-Driven Prediction Based Energy-Aware Fault-Tolerant Scheduling Scheme for Cloud Data Center[J]. IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING,2021,6(4):655-666.
APA	Marahatta, Avinab,Xin, Qin,Chi, Ce,Zhang, Fa,&Liu, Zhiyong.(2021).PEFS: AI-Driven Prediction Based Energy-Aware Fault-Tolerant Scheduling Scheme for Cloud Data Center.IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING,6(4),655-666.
MLA	Marahatta, Avinab,et al."PEFS: AI-Driven Prediction Based Energy-Aware Fault-Tolerant Scheduling Scheme for Cloud Data Center".IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 6.4(2021):655-666.