Cost-Effective Algorithm for Workflow Scheduling in Cloud Computing Under Deadline Constraint

Abstract

Cloud computing is a popular model that allows users to store, access, process, and retrieve data remotely. It provides a high-performance computing with large scale of resources. However, this model requires an efficient scheduling strategy for resources management. Recently, several algorithms are presented to solve the resource scheduling problem. Nevertheless, still the problem exists with complex applications such as workflows, which need an efficient algorithm to be scheduled on the available resources. This paper presents a novel hybrid algorithm, called CR-AC, combining both the chemical reaction optimization (CRO) and ant colony optimization (ACO) algorithms to solve the workflow-scheduling problem. The proposed CR-AC algorithm is implemented in the CloudSim toolkit and evaluated by using real applications and Amazon EC2 pricing model. Moreover, the results are compared with the most recent algorithms: modified particle swarm optimization (PSO) and cost-effective genetic algorithm (CEGA). The experimental results indicate that the CR-AC algorithm achieves better results than the traditional CRO, the ACO, the modified PSO and CEGA algorithms, in terms of total cost, time complexity, and schedule length. IntroductionCloud computing is considered as one of the most famous computing models. It gained a lot of attention in both academia and industry fields as it provides many benefits for users and organizations. It enables users to store and process their complex applications on a large datacenter. It also allows users to access their data remotely from everywhere. Further, it enables organizations and companies to obtain high computing capacity without bearing the elevated price of building huge datacenters inside. It also enables organizations and companies to outsource infrastructures using cloud environment and thus transfer their data and applications to

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