Cost-Based Multi-QoS Job Scheduling using Divisible Load Theory in Cloud Computing

The advent of cloud computing as a new model of service provisioning in distributed systems, encourages researchers to investigate its benefits and drawbacks in executing scientific applications such as workflows. In this research, we attempt to investigate the use of a Divisible Load Theory (DLT) to design efficient strategies to minimize the overall processing time for scheduling jobs in compute cloud environments. We consider homogeneous processors in our analysis and we derive a closed-form solution for the load fractions to be assigned to each processors. Our analysis also attempts to schedule the jobs such a way that cloud provider can gain maximum benefit for his service and Quality of Service (QoS) requirement user's job. Finally, we quantify the performance of the strategies via rigorous simulation studies.