Abstract
Cloud computing is providing resources to customers based on application demand under service level agreement (SLA) rules. Service providers are concentrating on providing a requirement based resource to fulfill the quality of service (QoS) requirements. But, it has become a challenge to cope with service-oriented resources due to uncertainty and dynamic demand for cloud services. Task scheduling is an alternative to distributing resource by estimating the unpredictable workload. Therefore, an efficient resource scheduling technique needs to distribute appropriate virtual machines (VMs). Swarm intelligence, involving a metaheuristic approach, is suitable to handle such uncertainty problems meticulously. In this research paper, we present an efficient resource scheduling technique using ant colony optimization (ACO) algorithm, with an objective to minimize execution cost and time. The comparative analysis of results has been demonstrated that the proposed scheduling algorithm performed better as compared to existing algorithms. Thus, the proposed resource scheduling algorithm can be used to improve the efficacy of cloud resources.
Published Version
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