PEFS: AI-Driven Prediction Based Energy-Aware Fault-Tolerant Scheduling Scheme for Cloud Data Center
文献类型：期刊论文
| 作者 | Marahatta, Avinab2,3; Xin, Qin1; Chi, Ce2,3; Zhang, Fa2; Liu, Zhiyong2 |
| 刊名 | IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING
 |
| 出版日期 | 2021-10-01 |
| 卷号 | 6期号:4页码:655-666 |
| 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 |
| ISSN号 | 2377-3782 |
| DOI | 10.1109/TSUSC.2020.3015559 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000728136400010 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/18048]  |
| 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 Faroe Isl, FR-100 Torshavn, Faroe Islands 2.Chinese Acad Sci, High Performance Comp Res Ctr, Inst Comp Technol, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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