A Research of Time Cost Balance Simulated Annealing Ant Colony Algorithm for Task Allocation in Cloud Computing

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

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A Research of Time Cost Balance Simulated Annealing Ant Colony Algorithm for Task Allocation in Cloud Computing

更新时间：2026-09-04

- A Research of Time Cost Balance Simulated Annealing Ant Colony Algorithm for Task Allocation in Cloud Computing
- Vol. 33, Issue 6, Pages: 53-58(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. 基于TCBSA-ACO算法在云计算任务分配中的研究[J]. 微电子学与计算机, 2016, 33(6): 53-58.

NIE Qing-bin, HUO Min-xia, CAO Yao-qin. A Research of Time Cost Balance Simulated Annealing Ant Colony Algorithm for Task Allocation in Cloud Computing[J]. 2016, 33(6): 53-58.
聂清彬, 霍敏霞, 曹耀钦. 基于TCBSA-ACO算法在云计算任务分配中的研究[J]. 微电子学与计算机, 2016, 33(6): 53-58. DOI：

NIE Qing-bin, HUO Min-xia, CAO Yao-qin. A Research of Time Cost Balance Simulated Annealing Ant Colony Algorithm for Task Allocation in Cloud Computing[J]. 2016, 33(6): 53-58. DOI：

摘要

针对云计算中的任务分配问题

提出一种基于建立时间成本负载约束函数的模拟退火蚁群算法(a restraint Function of Time Cost Load based on the Simulated Annealing ant colony Algorithm

TCBSA-ACO)

该算法结合云计算中任务分配的特点

创新地通过建立时间成本约束函数和负载标准差函数分别改进信息素的更新和启发信息

并用模拟退火算法对求出的解进行全局寻优.利用CloudSim工具进行仿真测试

与标准的蚁群算法BACO和最新的改进蚁群算法DSFACO做仿真对比

实验结果表明TCBSA-ACO算法在云任务的执行时间

成本

系统负载均衡率方面均优于这两种算法

提高了系统资源利用率.

Abstract

Aiming at solving the problem of task allocation in cloud computing

this paper proposes a restraint function of Time Cost Balance Simulated Annealing Ant Colony Algorithm (TCBSA-ACO). The TCBSA-ACO

combined with features of task allocation in cloud computing

improves information elements updating and inspiring factors creatively by establishing a time cost restraint function and a load standard deviation function

and achieves global optimization of all the answers by adopting the simulated annealing algorithm. Some simulation experiments are done by the tool CloudSim

and the results are compared with those of the standard ant colony algorithms BACO and the latest ant colony algorithm DSFACO. The comparison shows that this TCBSA-ACO algorithm is more efficient than the other two algorithms both in reducing time and cost of task execution and in keeping load balance

thus improves resource utilization in the system.

关键词

Keywords

references

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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(2): 38-50. DOI: 38

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

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Brototi Mondal, Kouslk Dasgupta, Paramartha Dutta. Load balancing in cloud compupting using stochastic hill climbing-A soft computing approach[J]. Procedia Technology, 2012, 28(4): 783-789. DOI: 783

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

张焕青, 张学平. 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

张春艳, 刘清林, 孟珂. 基于蚁群算法的云计算任务分配[J]. 计算机应用, 2012, 32(5): 1418-1420. DOI: 1418

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