OASM: An overload‐aware workload scheduling method for cloud computing based on biogeographical optimization

With the daily increase in the number of cloud users and the volume of submitted workloads, load balancing (LB) over clouds followed by a reduction in users' response time is emerging as a vital issue. To successfully address the LB problem, we have optimized workload distribution among virtual machines (VMs). This approach consists of two parts: Firstly, a meta‐heuristic method based on biogeographical optimization for workload dispatching among VMs is introduced; secondly, we propose an innovative heuristic algorithm inspired by the “Banker algorithm” that runs in core scheduler to control and avoid VM overloads. The combination of these two (meta‐)heuristic algorithms constitutes an LB approach through which we have been able to reduce the value of the makespan to a reasonable time frame. Moreover, an information base repository (IBR) is introduced to maintain the online processing status of physical machines (PMs) and VMs. In our approach, data stored in IBR are retrieved when needed. This approach is compared with well‐known (non‐)evolutionary approaches, such as round‐robin, max‐min, MGGS, and TBSLB‐PSO. Experimental results reveal that our proposed approach outperforms its counterparts in a heterogeneous environment when the resources are smaller than the workloads. Moreover, the utilization of physical resources gradually increases. Therefore, optimal workload scheduling, as well as the lack of overload occurrence, results in a reduction in makespan.