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Task scheduling using artificial bee foraging optimization for load balancing in cloud data centers
Computer Applications in Engineering Education ( IF 2.9 ) Pub Date : 2020-04-16 , DOI: 10.1002/cae.22236
Geetha Muthsamy 1 , Suganthe Ravi Chandran 1
Affiliation  

Load balancing in cloud data centers is a process of distributing the incoming service requests or the incoming tasks to the available virtual machines (VMs). This can be achieved by proper scheduling mechanisms through which the tasks will be allocated to suitable VMs. Scheduling in distributed systems, such as cloud data centers, is considered to be an NP‐complete problem. An efficient method of scheduling will result in balancing the load on the VMs, thereby achieving effective resource utilization. Hence, there is a need for a new scheduling framework to perform load balancing amid considering multiple quality of service (QoS) metrics, such as makespan, response time, execution time, and task priority. Therefore, considering the above metrics, task scheduling using artificial bee foraging (TSABF) optimization is proposed to obtain an optimal schedule of tasks to VMs. The resulting optimal schedule consists of a set of VMs to which the tasks are scheduled in a preemptive manner. Task preemption is done to reduce the response and the execution time of the tasks pertaining to different priorities. The experimental results are compared to the existing honey bee behavior‐inspired load balancing (HBB‐LB) algorithm. The results show that TSABF acts as an alternative scheduling strategy to perform load balancing and also improves the QoS metrics when compared to HBB‐LB.

中文翻译:

使用人工蜜蜂觅食优化云数据中心负载均衡的任务调度

云数据中心中的负载均衡是将传入的服务请求或传入的任务分发到可用的虚拟机 (VM) 的过程。这可以通过适当的调度机制来实现,通过该机制将任务分配给合适的 VM。分布式系统(如云数据中心)中的调度被认为是一个 NP 完全问题。一种有效的调度方法将导致平衡 VM 上的负载,从而实现有效的资源利用。因此,需要一种新的调度框架来在考虑多个服务质量 (QoS) 指标(例如,完工时间、响应时间、执行时间和任务优先级)的情况下执行负载平衡。因此,考虑到上述指标,提出了使用人工蜜蜂觅食(TSABF)优化的任务调度,以获得对 VM 的最佳任务调度。最终的最佳调度由一组 VM 组成,任务以抢占方式调度到这些 VM。完成任务抢占是为了减少属于不同优先级的任务的响应和执行时间。将实验结果与现有的蜜蜂行为启发负载平衡(HBB-LB)算法进行比较。结果表明,与 HBB-LB 相比,TSABF 作为一种替代调度策略来执行负载平衡,并且还提高了 QoS 指标。完成任务抢占是为了减少属于不同优先级的任务的响应和执行时间。将实验结果与现有的蜜蜂行为启发负载平衡(HBB-LB)算法进行比较。结果表明,与 HBB-LB 相比,TSABF 作为一种替代调度策略来执行负载平衡,并且还提高了 QoS 指标。完成任务抢占是为了减少属于不同优先级的任务的响应和执行时间。将实验结果与现有的蜜蜂行为启发负载平衡(HBB-LB)算法进行比较。结果表明,与 HBB-LB 相比,TSABF 作为一种替代调度策略来执行负载平衡,并且还提高了 QoS 指标。
更新日期:2020-04-16
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