A Genetic Algorithm-Based Virtual Machine Allocation Policy for Load Balancing Using Actual Asymmetric Workload Traces

Abstract

Load balancing is a very important concept in cloud computing. In this work, studies are conducted on workload traces at Los Alamos National Lab (LANL). The jobs in this trace are asymmetric in nature as most of them have small time limit. Two hybrid algorithms, a Genetic Algorithm combined with First Come First Serve (GA_FCFS) and Genetic Algorithm combined with Round Robin (GA_RR), are proposed here. The results obtained are compared with the existing First Come First Serve (FCFS), Round Robin (RR) and Genetic Algorithm (GA). Makespan and Resource Utilization are used for the comparison of results. In terms of Makespan, it is observed that GA_RR outperforms the other methods for all the batch sizes. Although the performance of GA_FCFS is much better than that of the other three well-established algorithms FCFS, RR and GA, it is still worse than that of the GA_RR algorithm for all the cases. GA_RR performs best in terms of Resource Utilization also and GA_FCFS is a close competitor. Overall, GA_RR outperforms all the other algorithms.

Keywords

• task scheduling
• genetic algorithms
• virtual machines
• cloud computing
• load balancing
• optimization