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Energy-Efficient Scheduling Algorithm for Real-TimeTasks in Cloud Data Center(PDF)

南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2016年03期
Page:
81-
Research Field:
计算机工程
Publishing date:

Info

Title:
Energy-Efficient Scheduling Algorithm for Real-TimeTasks in Cloud Data Center
Author(s):
Bao WenyanZhang YinjuanLi ChenLi Yun
College of Information Engineering,Yangzhou University,Yangzhou 225127,China
Keywords:
cloud data centerreal-time tasksslack time of tasksenergy-efficient scheduling
PACS:
TP393
DOI:
10.3969/j.issn.1672-1292.2016.03.013
Abstract:
In this paper,we study the problem of energy-efficient scheduling for real-time tasks. A task scheduling algorithm based on the slack time of tasks is designed. Three components of the algorithm are the distribution of real-time tasks,dynamically expanding virtual machine resource and integration of virtual machine resource. By computing the slack time of tasks,tasks can be completed within the deadline to ensure the timeliness of the task. With a multi-threshold-based virtual machine integration strategy,the system load is balanced and energy consumption of the system to complete the task set is reduced. Experiments show that,comparing with two other scheduling algorithms,the algorithm in this paper ensures that tasks can be completed on time,and energy consumption of the system can be efficiently reduced.

References:

[1] 巫晨云. 数据中心能效影响因素及评估模型浅析[J]. 电信工程技术与标准化,2014(1):46-49.
WU C Y. A brief analysis of the factors and evaluation models of data center energy efficiency[J]. Telecom engineering technics and standardization,2014(1):46-49. (in Chinese)
[2] 赵彬,王淖,王高才. 云计算环境下的节能任务调度策略的随机Petri网分析[J]. 计算机科学,2015,42(8):112-117.
ZHAO B,WANG N,WANG G C. Analysis on energy-saving task scheduling strategy based on stochastic Petri net for cloud computing.[J]. Computer science,2015,42(8):112-117. (in Chinese)
[3] 谭一鸣,曾国荪,王伟. 随机任务在云计算平台中能耗的优化管理方法[J]. 软件学报,2012,23(2):266-278.
TAN Y M,ZENG G S,WANG W. Policy of energy optimal management for cloud computing platform with stochastic tasks[J]. Journal of software,2012,23(2):266-278. (in Chinese)
[4] 何丽,饶俊,赵富强. 一种基于能耗优化的云计算系统任务调度方法[J]. 计算机工程与应用,2013,49(20):19-22.
HE L,RAO J,ZHAO F Q. Task scheduling method based on energy optimization in cloud computing system[J]. Computer engineering and applications,2013,49(20):19-22. (in Chinese)
[5] FAN X,WEBER W D,BARROSO L A. Power provisioning for a warehouse-sized computer[J]. ACM SIGARCH computer architecture news,2007,35(2):13-23.
[6] LI J,MING Z,QIU M,et al. Resource allocation robustness in multi-core embedded systems with inaccurate information[J]. Journal of systems architecture,2011,57(9):840-849.
[7] MUTHUCUMARU M,SHOUKAT A,HOWARD H S,et al. Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems[C]// Proceeding of the 8th Heterogeneous Computing Workshop(HCW’98),1999,4(3):1-15.
[8] KUO W H,YANG D L. Minimizing the total completion time in a single-machine scheduling problem with a time-dependent learning effect[J]. European journal of operational research,2006,174(2):1 184-1 190.

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Last Update: 2016-09-30