Optimal workflow scheduling in cloud computing based on hybrid bacterial evolutionary and bees mating optimization algorithm

Abstract

Distributed computing is the most recent developing pattern in disseminated processing that conveys equipment framework and programming applications as administrations. The clients can devour these administrations dependent on a SLA which characterizes their required QoS parameters. By using the cloud computing technique it is possible to reduce the investment on various resources like computer hardware and software. The application or processes that are hosted and executed using clouds consist of set of tasks and it is considered that this task will form the workflow. Therefore scheduling the task is considered as a major issue as resource usage has to be maximized without affecting the services that are facilitated by the cloud. In order to execute different virtual machine application of the tasks are assigned and it is termed as enterprise arranging. In the scheduling process the inter-dependent tasks are mapped and managed in the distributed resources. For additional improvement, this paper proposes a hybrid optimization algorithm for workflow scheduling (HOWS) in cloud environment. In the proposed algorithm the first contribution is the bees mating optimization (BMO) algorithm used to share physical infrastructure to enable multiple service providers to optimize scheduling. The second contribution in the proposed algorithm is the bacterial evolutionary algorithm used to flexible access of the resources in order to optimize the network resources. By combining the hybrid optimization algorithm provides the better improvement in terms of task scheduling and optimal resource allocation. The result and performance analysis shows that the proposed technique performs very efficient in terms of energy efficiency and scalability without compromising security. The performance is obtained using cloudSim tool