A Comparative Analysis of Cloud Load Balancing Algorithms Using CloudSim Simulations

Cloud computing provides a set of services that allows its users to lease digital infrastructure as software. This abstraction allows users to focus on development without managing low-level aspects and associated challenges. The infrastructure comprises heterogeneous VMs with varying performance parameters such as MIPS and bandwidth. Thus, user cloud jobs must be executed with great care and consideration of load balancing. If scheduling is not done efficiently, the latency that users experience increases, resulting in lower customer satisfaction and decreased revenue. Since scheduling problems are NP-Hard, there is a need for continuous research into improving existing algorithms. Ideally, the scheduling of cloud jobs should be fair and well-balanced, with minimal waiting time and minimal overhead. This study focuses on the comparison of nine such state-of-the-art, batch-based, non-preemptive scheduling schemes. All algorithms have been implemented in Java with the help of the CloudSim simulator and tested across seven performance metrics. We provide an in-depth performance analysis of Random Selection, Round Robin, Minimum Completion Time, Suffrage, Min-Min, Max-Min, Weighted Mean Time-Min, Resource Aware Load Balancing Algorithm, and QoS Guided Weighted Mean Time Min Heuristic across asymptotic time and space complexity, make-span, throughput, average resource utilization rate (ARUR), load balancing level, and batch scheduling time. Comparison of the mechanisms and their performance shall aid future work in comparing and contrasting the concepts deployed in existing schemes. It was observed that Suffrage and QWMTM performed the best across most of the performance metrics.