Task scheduling using artificial bee foraging optimization for load balancing in cloud data centers

Abstract

AbstractLoad 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.