基于改进量子遗传算法的云计算资源调度

针对云计算环境下资源的高效调度问题，当前研究较少关注云服务提供商的服务成本，为此，以云服务提供商降低最小服务成本为目的，提出了改进量子遗传算法的云资源调度算法。由于采用二进制量子位表示的染色体无法描述资源调度矩阵，该算法将量子位的二进制编码转换为实数编码，并使用旋转策略和变异算子保证算法的收敛性。通过仿真实验平台将此算法与遗传算法和粒子群算法进行比较分析，在种群迭代次数为100的情况下，分别取种群数为1和10，实验结果表明该算法能取得更小的最小服务成本。

Cloud computing resource scheduling based on improved quantum genetic algorithm

Focusing on the problem of high efficiency resource scheduling in cloud computing environment, since current research has been less concerned about the cost of the services of the cloud service provider, an improved Quantum Genetic Algorithm (QGA) was proposed to reduce the minimum service cost of cloud service provider. This algorithm converted quantum-bits encoded by binary number to real-coded quantum-bits as chromosome represented by binary-coded quantum-bits cannot describe the resource scheduling matrix, and used rotation strategy and mutation operator to guarantee the convergence of the algorithm. Comparative experiments were conducted among the improved QGA, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) through simulation platform, the populations number is 1 and 100 with 100 iteration times. The experimental results show that the improved QGA can obtain smaller minimum service cost.