Task scheduling algorithm based on PSO in cloud environment

被引:7
作者
Xu, Anqi [1 ]
Yang, Yang [1 ]
Mi, Zhenqiang [1 ]
Xiong, Zenggang [2 ]
机构
[1] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing, Peoples R China
[2] Hubei Engn Univ, Sch Comp & Informat Sci, Xiaogan, Hubei, Peoples R China
来源
IEEE 12TH INT CONF UBIQUITOUS INTELLIGENCE & COMP/IEEE 12TH INT CONF ADV & TRUSTED COMP/IEEE 15TH INT CONF SCALABLE COMP & COMMUN/IEEE INT CONF CLOUD & BIG DATA COMP/IEEE INT CONF INTERNET PEOPLE AND ASSOCIATED SYMPOSIA/WORKSHOPS | 2015年
基金
美国国家科学基金会;
关键词
cloud computing; task scheduling; Particle Swarm Optimization(PSO); Berger model; PARTICLE SWARM OPTIMIZATION;
D O I
10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.196
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In recent years, cloud computing has developed rapidly under the vigorous promotion of industry and academia. With the expansion of cloud computing, users' special needs for cloud resources have gradually improved. As a business model, cloud computing must pay more attention to user demands for services and provide users with high-quality services. As one of the key technologies in cloud computing, task scheduling is mainly responsible for assigning user tasks to the appropriate resources. However, the existing scheduling algorithms do not take full account of users' different needs. In this paper, we consider multidimensional QoS requirements, and introduce Berger model to judge the fairness of the resource allocation results. We also improve the Particle Swarm Optimization(PSO) algorithm by adjusting its parameters dynamically and making the position coding discrete. Then, we propose a task scheduling algorithm based on QoS-DPSO. The simulation results show that this algorithm can effectively carry out user tasks and reflect more fairness
引用
收藏
页码:1055 / 1061
页数:7
相关论文
共 16 条
[1]  
[Anonymous], INT J COMPUTATIONAL
[2]  
Arfeen M. A., 2011, Proceedings of the 2011 IEEE 35th IEEE Annual Computer Software and Applications Conference Workshops (COMPSACW 2011). Volume II: Workshops, P261, DOI 10.1109/COMPSACW.2011.52
[3]   Defining a standard for particle swarm optimization [J].
Bratton, Daniel ;
Kennedy, James .
2007 IEEE SWARM INTELLIGENCE SYMPOSIUM, 2007, :120-+
[4]  
Buyya R., 2008, P 10 IEEE INT C HIGH
[5]  
Buyya R., 2002, THESIS
[6]   CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms [J].
Calheiros, Rodrigo N. ;
Ranjan, Rajiv ;
Beloglazov, Anton ;
De Rose, Cesar A. F. ;
Buyya, Rajkumar .
SOFTWARE-PRACTICE & EXPERIENCE, 2011, 41 (01) :23-50
[7]  
Ferguson D., 1988, 8th International Conference on Distributed Computing Systems (Cat. No.88CH2541-1), P491, DOI 10.1109/DCS.1988.12552
[8]  
Ratnaweera A, 2004, IEEE T EVOLUT COMPUT, V8, P240, DOI [10.1109/TEVC.2004.826071, 10.1109/tevc.2004.826071]
[9]  
REGEV O, 1998, P 1 INT C INF COMP E
[10]   Particle swarm optimization for task assignment problem [J].
Salman, A ;
Ahmad, I ;
Al-Madani, S .
MICROPROCESSORS AND MICROSYSTEMS, 2002, 26 (08) :363-371