Abstract
Quality of Service (QoS) support in private clouds is a challenging process because of the limitations of available resources and the high rate of received jobs, which leads to an NP hard scheduling problem. In private clouds, resource owners are usually interested in maximizing their resource utilization and completion rates while minimizing the turnaround time of their jobs, which complicates the scheduling problem even more. Haizea is an eminent cloud scheduler that offers high performance in terms of job turnaround time and completion rate. However, Haizea, and cloud schedulers in general, suffer from low resource utlization. Additionally, cloud schedulers usually consider only end users' demands, while providers' demands are entirely neglected. This is because an infinite pool of resources is assumed, which is difficult to achieve and simply not true in private clouds. Conversely, Condor, the eminent High Throughput Computing (HTP) scheuler, is known for addressing these shortcomings by formulating owner's and user's requirements as a logical expression evaluated based on the context which result is high resource utilization. Unfortunatly, this comes with the price of long execution time. As each of Haizea and Condor has its own advantages and limitations, in this paper, we propose a hybrid Haizea and Condor approach (HHCS) which utilizes techniques from both schedulers in a way that maximizes their advantages and overcomes their limitations. The proposed approach has been tested thoroughly in a simulated private cloud environment under various numbers of nodes and jobs. Experimental results illustrated an enhanced performance in terms of resources utilization without compromising the job turnaround time or the job completion rate. (C) 2014 Elsevier B.V.