云计算中任务调度优化策略的研究
Research on Task Scheduling Optimization Strategy in Cloud Computing
查看参考文献16篇
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文摘
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针对基于蚁群算法的任务调度负载不均衡与收敛速度较慢的问题,提出一种改进的任务调度优化算法。通过赋予权重的方法对蚁群算法的信息素更新规则进行优化,加快求解速度,利用动态更新挥发系数优化算法的综合性能,并在局部信息素的更新过程中,引入虚拟机负载权重系数,从而保证虚拟机的负载均衡。实验结果表明,改进算法的任务调度策略在保证任务得到合理分配的同时,还可以提高收敛速度并缩短总执行时间。 |
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其他语种文摘
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Aiming at the problem that task scheduling based on ant colony algorithm has unbalanced load and slow convergence speed,an improved task scheduling optimization algorithm is proposed. The pheromone update rules of the ant colony algorithm are optimized by weighting methods to accelerate the solution speed,and the comprehensive performance of the dynamic update volatilization coefficient optimization algorithm is utilized,and the load weight coefficient of the virtual machine is introduced during the update process of the local pheromone to ensure the load balancing of virtual machines. Experimental results show that the task scheduling strategy of the improved algorithm ensures that the task is reasonably allocated,and at the same time,the convergence speed of the algorithm is improved and the total execution time is shortened. |
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来源
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计算机工程
,2018,44(8):14-18 【扩展库】
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DOI
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10.19678/j.issn.1000-3428.0049169
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关键词
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蚁群算法
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信息素
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虚拟机
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权重系数
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收敛速度
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地址
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长沙理工大学计算机与通信工程学院, 长沙, 410114
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-3428 |
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学科
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自动化技术、计算机技术 |
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文献收藏号
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CSCD:6306145
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参考文献 共
16
共1页
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