Performance Analysis of Bio-Inspired Scheduling Algorithms for Cloud Environments

Cloud computing environments mainly focus on the delivery of resources, platforms, and applications as services to users over the Internet. Cloud promises users access to as many resources as they need, making use of an elastic provisioning of resources. The cloud technology has gained popularity in recent years as the new paradigm in the IT industry. The number of users of Cloud services has been increasing steadily, so the need for efficient task scheduling is crucial for maintaining performance. In this particular case, a scheduler is responsible for assigning tasks to virtual machines efficiently; it is expected to adapt to changes along with defined demand. In this paper, we present a comparative performance study on bio-inspired scheduling algorithms: Ant Colony Optimization (ACO) and Honey Bee Optimization (HBO). A networking scheduling algorithm, Random Biased Sampling, is also evaluated. Those algorithms show the ability of self-managing and adapting to changes in the environment. The experimental results have shown that ACO performs better when computation power is set as the objective, and HBO shows better scheduling when the objective mainly relies on costs.