Elasticity Based Scheduling Heuristic Algorithm 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 suggest an elastic scheduler that is able to alter its focus based on the current requirements demanded by the cloud service provider and the user of those services. The Elasticity Based Scheduling Heuristic (EBSH) suggested is measured against the bio-inspired optimization algorithms such as Ant Colony Optimization (ACO) and Honey Bee Optimization (HBO). Also, a networking algorithm is used in this study, namely Random Biased Sampling (RBS). The presented EBSH shows superior performance because of its ability to adapt to changes.