Dynamic Data Replication Based on Tasks scheduling for Cloud Computing Environment

Abstract

Cloud computing provides IT resources (e.g., CPU, memory, network, storage, etc.) based on virtualization concepts and a pay-as-you-go principle. It comprises an accumulation of inter-related plus virtualized calculating resources which are managed by one or more amalgamated calculating resources. With the development of a computerized scientific workflow, the amount of data is increasing exponentially. Workflow scheduling and data replication have been considered the major challenges in cloud computing. Nevertheless, many researchers focus on scheduling or data replication separately. In this article, a combination of workflow scheduling based on the clustering of data and dynamic data replication strategies, has been introduced together and evaluates several performance metrics using a Cloudsim simulator. The aim of this proposed algorithm is to minimize the completion time and transfer time. The performance of this proposed algorithm has been evaluated using the CloudSim toolkit.