基于烟花算法的云计算多目标任务调度

为了更好地满足云计算中用户的服务质量(Quality of Service, QoS)需求，合理利用云数据中心的资源，以任务的执行时间和虚拟机的负载均衡作为优化的目标对象，提出了一种基于烟花算法(Fireworks Algorithm, FWA)的多目标优化调度模型。烟花算法是一种启发式算法，利用爆炸算子、高斯变异和选择策略能较快地寻找到全局最优解。通过在Cloudsim上与粒子群优化算法(PSO)和遗传算法(GA)进行有效性和执行时间上的对比，结果表明烟花算法在不同实验次数下可持续得到最优适应度值，而且在种群规模不断扩大时，烟花算法的执行时间没有陡然增加，明显优于PSO算法和GA算法。

Multi-objective Task Scheduling based on Fireworks Algorithm in Cloud Computing

In order to better meet the requirements of users'' QoS (Quality of Service) in cloud computing, and utilize the resources of the cloud datacenter reasonably, this paper proposed a multi-objective optimization scheduling model based on fireworks algorithm (FWA), which considered the executive time of task and load balancing of virtual machine as optimization target. FWA is a heuristic algorithm. It can find global optima quickly using explosion operator, Gaussian mutation and selection strategy. By making a comparison with particle swarm optimization algorithm (PSO) and genetic algorithm (GA) on the effectiveness and executive time of the algorithm on Cloudsim platform, it shows that FWA could obtain the best fitness value in different test times sustainably. With a growing number of population size, there is no sharp increase of executive time of FWA, which is superior to PSO and GA.