云计算环境下基于蜜蜂觅食行为的任务负载均衡算法

Load balancing algorithm of task scheduling in cloud computing environment based on honey bee behavior

针对云计算环境下的任务调度程序通常需要较多响应时间和通信成本的问题,提出了一种基于蜜蜂行为的负载均衡(HBB-LB)算法。首先,利用虚拟机(VM)进行负载平衡来最大化吞吐量;然后,对机器上任务的优先级进行平衡;最后,将平衡重点放在减少VM等待序列中任务的等待时间上,从而提高处理过程的整体吞吐量和优先级。利用Cloud Sim工具模拟云计算环境进行仿真实验,结果表明,相比粒子群优化(PSO)、蚁群算法(ACO)、动态负载均衡(DLB)、先入先出(FIFO)和加权轮询(WRR)算法,HBB-LB算法的平均响应时间分别节省了5%、13%、17%、67%、37%,最大完成时间分别节省了20%、23%、18%、55%、46%,可以更好地平衡非抢占式独立任务,适用于异构云计算系统。

For the problem that task scheduling program in cloud computing environments usually takes high response time and communication costs, a Honey Bee Behavior inspired Load Balancing（ HBB-LB） algorithm was proposed. Firstly,the load was balanced across Virtual Machines（ VMs） for maximizing the throughput. Then the priorities of tasks on the machines were balanced. Finally, HBB-LB algorithm was used to improve the overall throughput of processing, and priority based balancing focused on reducing the wait time of tasks on a queue of the VM. The experiments were carried out in cloud computing environments simulated by Cloud Sim. The experiment results showed that HBB-LB algorithm respectively reduced average response time by 5%, 13%, 17%, 67% and 37% compared with Particle Swarm Optimization（ PSO）, Ant Colony Optimization（ ACO）, Dynamic Load Balancing（ DLB）, First In First Out（ FIFO） and Weighted Round Robin（ WRR）algorithms, and reduced maximum completion time by 20%, 23%, 18%, 55% and 46%. The result indicates that HBB-LB algorithm is suitable for cloud computing system and helpful to balancing non-preemptive independent tasks.