Cost-based job scheduling strategy in cloud computing environments

Cloud computing is one of the important approach for business actions in nowadays industry. The different characteristics of cloud such as on-demand capabilities, measured service, virtualization and rapid elasticity make the cloud more interesting in scientific organizations. With increasing number of users and jobs, optimal job scheduling becomes a strenuous process. Most available scheduling techniques in cloud only concentrate on one job type that can be data-intensive or computation-intensive. But, job scheduling based on one job type does not appropriate in the viewpoint of all environments, and sometimes may lead to wasting of resources on the other side. To discuss the problem of simultaneously taking into account both job types, Cost-based job scheduling (CJS) algorithm is proposed in this paper. The CJS algorithm uses data, processing power and network characteristics in job allocation process. Finally, we conducted simulations using CloudSim toolkit and compared CJS with other existing algorithms, like FUGE, Berger, MQS, and HPSO algorithms. CJS method can reduce the response time of submitted jobs, which may consist of data-intensive and computing -intensive jobs.