基于混合优化算法的云计算资源调度

由于云计算的动态性、异构性和不可预测性等特点,使得资源调度策略面临很大的挑战。目前解决资源调度的方法主要是一些启发式算法,如模拟退火算法、人工神经网络算法、粒子群算法、蚁群算法和遗传算法等,由于优缺点分明,不能单独实现云计算任务的最优分配。因此,提出了使用混合优化算法解决云计算资源分配问题。在算法前期,借助粒子群全局广泛搜索能力,快速寻找到较优解;在算法后期,借助蚁群算法的正反馈性和高效性,寻找最优解。实验表明该算法有较短的任务执行时间和实现各个物理主机间的负载均衡。

Research on Cloud Computing Resource Scheduling Based on Hybrid Optimization Algorithm

It makes resource scheduling policy a big challenge because of the dynamic nature of cloud computing, heterogeneous and unpredictable characteristics. The present solution are heuristic algorithms,such as simulated annealing, artificial neural network algorithm, particle swarm optimization, ant colony optimization, genetic algorithm and so on; It cannot be achieved optimal allocation of cloud computing tasks separately due to all these methods have its advantages and disadvantages. So this study try to fix the problem of resource scheduling of cloud computing using Hybrid optimization algorithm. In the early stage of algorithm, using a wide range global search capability of Particle Swarm Optimization to find the optimum solution quickly; In the late stage, with positive and efficiency of feedback Ant Colony Algorithm, the optimal solution is found. Experimental results show that task execution time of the algorithm is shorter and make load balancing for each physical host.