Performance Analysis of Scheduling Algorithms for Virtual Machines and Tasks in Cloud Computing
Scheduling virtual machines (VMs) and tasks in the appropriate location is a fundamental challenge integral to the consolidation process in cloud data centers. Therefore, many optimization methods have been developed as optimal solutions to assign VMs to host or tasks to VM. The most popular algorithms are SJF (Short Job First), FCFS (First Come First Served), MinMin, and MaxMin. This work introduces a comparative study by evaluating those algorithms with PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) using the CloudSim toolkit. The performance metrics used are degree of imbalance and makespan. The simulation results showed that ACO outperforms the evaluated scheduling algorithms in terms of the degree of imbalance and VM makespan.
