Task scheduling scheme based on improved particle swarm optimization with biological symbiosis mechanism in cloud computing

Linjie WANG

doi:10.11959/j.issn.1000-0801.2016241

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Task scheduling scheme based on improved particle swarm optimization with biological symbiosis mechanism in cloud computing

Research and Development | 更新时间：2024-06-05

- Task scheduling scheme based on improved particle swarm optimization with biological symbiosis mechanism in cloud computing
- Telecommunications Science Vol. 32, Issue 9, Pages: 113-119(2016)
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- DOI：10.11959/j.issn.1000-0801.2016241
  CLC： TP311
- Published Online：2016-09，
  
  Published：15 September 2016
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Linjie WANG. Task scheduling scheme based on improved particle swarm optimization with biological symbiosis mechanism in cloud computing[J]. Telecommunications science, 2016, 32(9): 113-119.
DOI：

Linjie WANG. Task scheduling scheme based on improved particle swarm optimization with biological symbiosis mechanism in cloud computing[J]. Telecommunications science, 2016, 32(9): 113-119. DOI： 10.11959/j.issn.1000-0801.2016241.

摘要

针对传统的基于智能算法的云计算任务调度方案获取最优解用时较多的问题，受生物界共生现象的启发，提出一种基于生物共生机制(SM)改进粒子群优化(PSO)的任务调度方案。首先，将PSO中的粒子分成2个种群，各自执行寻优。然后，每执行k次PSO迭代后，将两个种群中的个体进行互利共生和寄生操作。通过互利共生操作使搜索过程穿过最佳解区域，从而增强搜索能力；通过寄生操作排除较差解并引入较优解来防止过早收敛。最终获得任务调度的最优解。仿真结果表明，提出的优化算法可快速收敛，相比其他几种较新的调度方案，提出的方案能够获得最小的任务完成时间和响应时间。

Abstract

For the issues that the existing task scheduling scheme based on intelligent algorithms can’t obtain the optimal solution in cloud computing and inspired by nature symbiosis

a new task scheduling scheme based on improved particle swarm optimization(PSO)with biological symbiosis mechanism(SM)was proposed.Firstly

the particles in PSO were divided into two populations

and the optimization process were performed alone.Then

after each execution of the k iteration of PSO

the individual in the two populations performed the mutualism and parasitism operation.The search process was optimized by mutualism operation to through the optimal solution region

which could enhance the search ability.The parasitism operation was used to avoid premature convergence by eliminating the poor and introducing the optimal solution.Finally

the optimal solution of the task scheduling was obtained.Simulation results show that the optimal scheduling scheme can obtain the minimum task completion time and response time.

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references

张鹏 , 王桂玲 , 徐学辉 . 云计算环境下适于工作流的数据布局方法 [J ] . 计算机研究与发展 , 2013 , 50 ( 3 ): 636 - 647 .

ZHANG P , WANG G L , XU X H . A data placement approach for workflow in cloud [J ] . Journal of Computer Research and Development , 2013 , 50 ( 3 ): 636 - 647 .

刘丹琦 , 于炯 , 英昌甜 . 云计算环境下多有向无环图工作流的节能调度算法 [J ] . 计算机应用 , 2013 , 33 ( 9 ): 2410 - 2415 .

LIU D Q , YU J , YING C T . Energy efficient scheduling for multiple directed acyclic graph in cloud computing [J ] . Journal of Computer Applications , 2013 , 33 ( 9 ): 2410 - 2415 .

刘少伟 , 孔令梅 , 任开军 , 等 . 云环境下优化科学工作流执行性能的两阶段数据放置与任务调度策略 [J ] . 计算机学报 , 2011 , 34 ( 11 ): 2121 - 2130 .

LIU S W , SUN L M , REN K J , et al . A two-step data placement and task scheduling strategy for optimizing scientific workflow performance on cloud computing platform [J ] . Chinese Journal of Computers , 2011 , 34 ( 11 ): 2121 - 2130 .

LI W , WU J , ZHANG Q , et al . Trust-driven and QoS demand clustering analysis based cloud workflow scheduling strategies [J ] . Cluster Computing , 2014 , 17 ( 3 ): 1 - 18 .

武善玉 , 张平 , 覮李方 . 云制造系统中基于粒子群优化的多任务调度 [J ] . 华南理工大学学报:自然科学版 , 2015 , 43 ( 1 ): 105 - 110 .

WU S Y , ZHANG P , YING L F . Multi-task scheduling based on particle swarm optimization in cloud manufacturing systems [J ] . Journal of South China University of Technology(Natural Science Edition) , 2015 , 43 ( 1 ): 105 - 110 .

THANUSHKODI K . Hybrid intelligent algorithm[improved particle swarm optimization(PSO)with ant colony optimization (ACO)]for multiprocessor job scheduling [J ] . Scientific Research＆Essays , 2012 , 7 ( 20 ): 1935 - 1953 .

KIM Y G , LEE M J . Scheduling multi-channel and multi-timeslot in time constrained wireless sensor networks via simulated annealing and particle swarm optimization [J ] . IEEE Communications Magazine , 2014 , 52 ( 1 ): 122 - 129 .

刘冬华 , 甘若迅 , 樊锁海 , 等 . 基于捕食策略的粒子群算法求解投资组合问题 [J ] . 计算机工程与应用 , 2013 , 49 ( 6 ): 253 - 256 .

LIU D H , GAN R X , FAN S H , et al . Particle swarm optimization based on predatory search for portfolio investment [J ] . Computer Engineering and Applications , 2013 , 49 ( 6 ): 253 - 256 .

XIA H . An improved particle swarm optimization based on biological chemotaxis [J ] . Advanced Materials Research , 2014 , 15 ( 10 ): 737 - 740 .

石星宏 , 张仲荣 , 王博 , 等 . 基于生物寄生行为的双种群粒子群算法在VRP中的应用 [J ] . 兰州交通大学学报 , 2012 , 31 ( 3 ): 65 - 68 .

SHI X H , ZHANG Z R , WANG B , et al . Particle swarm optimization based on parasitic behavior for the VRP in the application [J ] . Journal of Lanzhou Jiaotong University , 2012 , 31 ( 3 ): 65 - 68 .

JIA Q , SEO Y . An improved particle swarm optimization for the resource-constrained project scheduling problem [J ] . International Journal of Advanced Manufacturing Technology , 2013 , 67 ( 12 ): 2627 - 2638 .

KOULINAS G , KOTSIKAS L , ANAGNOSTOPOULOS K . A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem [J ] . Information Sciences , 2014 , 277 ( 2 ): 680 - 693 .

温涛 , 盛国军 , 郭权 , 等 . 基于改进粒子群算法的 Web服务组合 [J ] . 计算机学报 , 2013 , 36 ( 5 ): 1031 - 1046 .

WEN T , SHENG G J , GUO Q , et al . Web services composition based on modified particle swarm optimization [J ] . Chinese Journal of Computers , 2013 , 36 ( 5 ): 1031 - 1046 .

BUX M , LESER U . DynamicCloudSim:simulating heterogeneity in computational clouds [J ] . Future Generation Computer Systems , 2014 , 46 ( 4 ): 85 - 99 .

史小露 , 孙辉 , 李俊 , 等 . 具有快速收敛和自适应逃逸功能的粒子群优化算法 [J ] . 计算机应用 , 2013 , 33 ( 5 ): 1308 - 1312 .

SHI X L , SUN H , LI J , et al . Particle swarm optimization algorithm with fast convergence and adaptive escape [J ] . Journal of Computer Applications , 2013 , 33 ( 5 ): 1308 - 1312 .

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