Associate scheduling of mixed jobs in cloud computing

Abstract

In a cloud environment, the jobs are scheduled based on different constraints so as to complete the job within its deadline. However, the classical scheduling algorithms have focussed on processing the compute-intensive and data-intensive job independently. So, simultaneous processing of compute-intensive and data-intensive jobs is a challenging task in a cloud environment. Hence, this paper proposes a new technique called Associate Scheduling of Mixed Jobs (ASMJ) that will concurrently process compute-intensive and data-intensive jobs in a two-tier VM architecture using the sliding window technique to improve processor utilization and network bandwidth. The experimental results show that the proposed ASMJ improves the processor utilization, QoS, user satisfaction and network bandwidth compared with the existing techniques.