摘要
资源调度问题是云计算研究的一个重要方向。针对传统量子粒子群算法的不足,提出了一种改进量子粒子算法,并将其应用于云计算资源调度策略。首先,建立了云计算资源调度问题的模型,并将资源调度任务完成的时间作为适应度函数。随后采用自适应机制,通过改变粒子位置更新的惯性权值,提高了算法的全局搜索能力,加快了收敛速度。最后通过实验仿真对该算法进行了测试。实验表明,该算法能更好更快地找到云计算资源调度方案,使资源分配更加合理高效。
Resource scheduling problem is an important direction of cloud computing research.Aiming at the shortcomings of the traditional quantum particle swarm optimization algorithm,this paper proposes a quantum particle swarm algorithm based on improved.Cloud computing resource scheduling model is established firstly,and then the fitness function of the model is defined.Soon afterwards,by using the adaptive mechanism,the inertia of particle position update weights to improve the global search ability of the algorithm is changed,and the convergence speed is accelerated.Finally the simulation of the algorithm is tested by experiments.And the experimental results show that this method can better and faster to find cloud computing resource scheduling scheme,make resource allocation more reasonable and efficient.
作者
赵昱
惠晓滨
高杨军
郭庆
ZHAO Yu;HUI Xiao-bin;GAO Yang-jun;GUO Qing(Material Management and Safety Engineering College,Air Force Engineering University,Xi’an 710051,China)
出处
《火力与指挥控制》
CSCD
北大核心
2017年第4期14-17,共4页
Fire Control & Command Control
基金
国家自然科学青年基金资助(71501184)
关键词
云计算
资源调度
量子粒子群算法
惯性权值
cloud computing
resource scheduling
quantumparticle swarmoptimization
inertia weight
