The Research of Scheduling Algorithm Faced to the Matching Degree of Users Task and Cloud Resource

NIE Qing-bin; HUO Min-xia; CAO Yao-qin

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The Research of Scheduling Algorithm Faced to the Matching Degree of Users Task and Cloud Resource

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- The Research of Scheduling Algorithm Faced to the Matching Degree of Users Task and Cloud Resource
- Vol. 33, Issue 7, Pages: 93-97(2016)
- 作者机构：
  
  1. 重庆邮电大学机构移通学院, 重庆合川,401520
  2. 四川大学锦江学院,四川,眉山,620860
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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聂清彬, 霍敏霞, 曹耀钦. 面向用户任务与云资源匹配度的调度算法研究[J]. 微电子学与计算机, 2016, 33(7): 93-97.

NIE Qing-bin, HUO Min-xia, CAO Yao-qin. The Research of Scheduling Algorithm Faced to the Matching Degree of Users Task and Cloud Resource[J]. 2016, 33(7): 93-97.
聂清彬, 霍敏霞, 曹耀钦. 面向用户任务与云资源匹配度的调度算法研究[J]. 微电子学与计算机, 2016, 33(7): 93-97. DOI：

NIE Qing-bin, HUO Min-xia, CAO Yao-qin. The Research of Scheduling Algorithm Faced to the Matching Degree of Users Task and Cloud Resource[J]. 2016, 33(7): 93-97. DOI：

摘要

为了提高任务在云环境中的执行效率

提出一种基于任务资源匹配度函数和的成本函数的改进蚁群算法(an advanced Matching function of Task Resource and Cost in Ant Colony Optimization

MTRCACO)

在综合参考各种最新蚁群算法的基础上

创新地通过任务与资源匹配度函数来改进信息素中的启发信息

并通过成本函数降低云计算中心的负载不均衡度

使虚拟机通过多次算法迭代以后能够处于一种负载均衡的状态

利用CloudSim工具进行仿真测试

实验结果表明MTRCACO算法在任务的执行成本以及系统负载均衡方面均优于IPSO算法和BACO算法

提高了资源的利用率.

Abstract

To improve the execution efficiency of task in cloud environment

the Matching function of Task Resource and Cost Advanced Ant Colony Optimization(MTRCACO)is proposed.The MTRCACO improves inspiration information from information elements by making a comprehensive reference of the latest ant colony algorithms and adopting the matching function of task and resource.It also decreases the load unbalancing degree in cloud computing center by cost function

and simultaneously keeps the load balance of virtual machines by executing the tasks for many times.Some experiments are carried out on the CloudSim platform.The comparison shows this MTRCACO algorithm is more efficient than the algorithms IPSO and BACO in reducing task execution cost and in keeping system load balance

thus optimizes resource utilization.

关键词

Keywords

references

Brototi Mondal, KOUSIK Dasgupta, Paramartha Dutta. Load Balancing in Cloud Compupting using Stochastic Hill Climbing-A Soft computing Approach[C]//International Conference on Computer Communication. Control and Information Tednology. [s. l. ]: IEEE, 2012: 783-789.

Ramesh D, Krishnan A. Optimal parameter identification in ant colony optimization for load balancing in grid com-putting[J]. European Journal of Scientific Research, 2012, 75(3): 370-376. DOI: 370

魏赟, 陈元元. 基于改进蚁群算法的云计算任务调度模型[J]. 计算机工程, 2015, 41(1): 12-16. DOI: 12

Zheng Z N, Wang R. An approach for cloud resource scheduling based on parallel genetic algorithm[C]//In Proc Of 3rd International Conference on Computer Research and Development. Shanghai: IEEE, 2011: 444-447.

Zhan S B, Huo H Y. Improved PSO-based task scheduling algorithm in cloud computing[J]. Information % Computational Science, 2013, 13(9): 3821-3829. DOI: 3821

Cristian Mateos, Elina Pacini, Carlos Garcia garion. An ACO-inspired algorithm for minimizing weighted flow time in cloud-based parameter sweep experiments[J]. Advances in Engineering Software, 2013, 56(11): 38-50. DOI: 38

蔡琪, 单东红, 赵伟艇. 改进粒子群算法的云计算环境资源优化调度[J]. 辽宁工程技术大学学报(自然科学版), 2016, 35(1): 93-96. DOI: 93

张焕青, 张学平, 王洲清, 等. 基于负载均衡蚁群优化算法的云计算任务调度[J]. 微电子学与计算机, 2015, 32(5): 31-40. DOI: 31

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