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计算机科学与技术汇刊

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ISSN Print:2327-090X

ISSN Online:2327-0918

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Paper Infomation

QoS-Constrained, Reliable and Energy-Efficient Task Deployment in Cloud Computing

Full Text(PDF, 1442KB)

Author: Zhenghui Zhang, Yuqi Fan

Abstract: Reliability, QoS and energy consumption are three important concerns of cloud service providers. Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concerns. However, these three factors have intrinsic trade-off relationships. The existing studies show that load concentration can reduce the number of servers and hence save energy. In this paper, we deal with the problem of reliable task deployment in data centers, with the goal of minimizing the number of servers used in cloud data centers under the constraint that the job execution deadline can be met upon single server failure. We propose a QoS-Constrained, Reliable and Energy-efficient task replica deployment (QSRE) algorithm for the problem by combining task replication and re-execution. For each task in a job that cannot finish executing by re-execution within deadline, we initiate two replicas for the task: main task and task replica. Each main task runs on an individual server. The associated task replica is deployed on a backup server and completes part of the whole task load before the main task failure. Different from the main tasks, multiple task replicas can be allocated to the same backup server to reduce the energy consumption of cloud data centers by minimizing the number of servers required for running the task replicas. Specifically, QSRE assigns the task replicas with the longest and the shortest execution time to the backup servers in turn, such that the task replicas can meet the QoS-specified job execution deadline under the main task failure. We conduct experiments through simulations. The experimental results show that QSRE can effectively reduce the number of servers used, while ensuring the reliability and QoS of job execution.

Keywords: Cloud Computing, Task Deployment, Reliability, Quality of Service, Energy Consumption

References:

[1] Engelmann C, Geist A. Super-scalable algorithms for computing on 100,000 processors[C]//International Conference on Computational Science. Springer, 2005: 313-321.

[2] Cui X, Mills B N, Znati T, Melhem R G. Shadows on the cloud: An energy-aware, profit maximizing resilience framework for cloud computing[C]//CLOSER, 2014: 15-26.

[3] Cost of data center outages 2020[EB/OL]. https://pitcch.org/uei6upi6/1c0972-cost-of-data-center-outages-2020.

[4] Garraghan P, Moreno I S, Townend P, Xu J. An analysis of failure-related energy waste in a large-scale cloud environment[J]. IEEE Transactions on Emerging topics in Computing, 2014, 2(2): 166-180.

[5] Luo L, Wu W, Zhang F. Energy modeling based on cloud data center[J]. Journal of Software, 2014, 25(7): 1371-1387.

[6] Fan Y, Wang C, Wu W, Znati T, Du D. Slow replica and shared protection: Energy-efficient and reliable task assignment in cloud data centers[J]. IEEE Transactions on Reliability, 2021, 70(3): 931-943.

[7] Chiang M L, Huang Y F, Hsieh H C, Tsai W C. Highly reliable and efficient three-layer cloud dispatching architecture in the heterogeneous cloud computing environment[J]. Applied Sciences, 2018, 8(8): 1385.

[8] Tan Y, Zeng G, Wang W. Policy of energy optimal management for cloud computing platform with stochastic tasks[J]. Journal of software, 2021, 23(2): 266-278.

[9] Liu X, Zhan Z, Deng J D, Li Y, Gu T, Zhang J. An energy efficient ant colony system for virtual machine placement in cloud computing[J]. IEEE Transactions on Evolutionary Computation, 2018, 22(1): 113-128.

[10] Chase J S, Anderson D C, Thakar P N, Vahdat A M, Doyle R P. Managing energy and server resources in hosting centers[J]. ACM SIGOPS operating systems review, 2001, 35(5): 103-116.

[11] Softchris. Reliability metrics - Training[EB/OL]. https://learn.microsoft.com/en-us/training/modules/cmu-data-center-design/7-reliability-metrics.

[12] Guo Z, Li J, Ramesh R. Optimal management of virtual infrastructures under flexible cloud service agreements[J]. Information Systems Research, 2019, 30(4): 1424-1446.

[13] Sahoo R K, Squillante M S, Sivasubramaniam A, Zhang Y. Failure data analysis of a large-scale heterogeneous server environment[C]//International Conference on Dependable Systems and Networks, IEEE, 2004: 772-781.

[14] Eisler M, Corbett P, Kazar M, Nydick D S, Wagner J C. Data ONTAP GX: A scalable storage cluster[C]//FAST, 2007: 23-23.

[15] Masuda S, He F, Kawabata A, Oki E. Distributed server allocation model with preventive start-time optimization against single failure[C]//2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR), IEEE, 2020: 1-6.

[16] Pedram M. Energy-efficient datacenters[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2012, 31(10): pp. 1465-1484.

[17] Limam S, Mokadem R, Belalem G. Data replication strategy with satisfaction of availability, performance and tenant budget requirements[J]. Cluster Computing, 2019, 22(4): 1199-1210.

[18] Zhang L, Li K, Xu Y, Mei J, Zhang F, Li K. Maximizing reliability with energy conservation for parallel task scheduling in a heterogeneous cluster[J]. Inform Sciences, 2015, 319: 113-131.

[19] Taal A, Wang J, de Laat C, Zhao Z. Profiling the scheduling decisions for handling critical paths in deadline-constrained cloud workflows[J]. Future Generation Computer Systems, 2019, 100: 237-249.

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