Proceedings of the 2006 Winter Simulation Conference L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoto, eds. ABSTRACT Despite growing popularity of small-scale clusters built out of off-the-shelf components, there has been little research on how these small-scale clusters behave under different scheduling policies. Batch scheduling policies with back- filling provide excellent space-sharing strategy for parallel jobs. However, as the performances of uniprocessor and symmetric multiprocessor have improved with time- sharing scheduling strategies, it is intuitive that the per- formance of a cluster of PCs with distributed memory may also improve with time-sharing strategies, or a combination of time-sharing and space-sharing strategies. Apart from the batch scheduling policies, this research explores the possibilities of using synchronized time-sharing scheduling algorithms for clusters. This paper describes simulation of the Gang scheduling policies on top of an existing batch scheme. The simulation results indicate that time-sharing scheduler for clusters could exhibit superior performance over a batch policy. 1 INTRODUCTION With the increase in processing power and decrease in the prices of today’s commercial-off-the-shelf (COTS) PCs, and the increase in the bandwidth of easily available and affordable Ethernet, “home grown” clusters like Beowulf cluster have been popularly built for High-Performance- Computing and Parallel Computing environment. Such parallel processing finds a wide range of usage among re- searchers and practitioners. However, often the parallel systems suffer from under utilization due to inappropriate choice of scheduling policy. A scheduling policy is used to settle the conflicts in resource acquisition when a job re- quires more nodes than that are currently available. In our previous studies (Rajaei and Dadfar 2005, 2006), various backfilling techniques, namely conservative and aggressive backfilling (Srinivasan et al. 2002), multi- ple-queue (Lawson and Smirni 2002) and lookahead (Shmueli and Feitelson 2003), have been investigated. Multiple queue backfilling suffers from over-fragmentation of available nodes in a small-scale cluster (Rajaei and Dad- far 2006), whereas other techniques seem to be more prom- ising. The main drawback of backfilling is higher response time for some jobs which are requesting more resources particularly in case of aggressive backfilling, and some jobs may suffer from overestimation of required processing time. Gang scheduling (Zang et al. 2003) overcomes the problem of response time, while its disadvantage is the global synchronization overhead needed to coordinate a set of processes. Even though it incurs a heavy context switch overhead, it may still serve as a viable scheduling alterna- tive in small-scale clusters. Gang scheduling also offers various tunable parameters, which can be dynamically changed according to size and nature of workload. The number of time slots, which are the interval allocated to a job during which it runs without preemption, and their du- ration can be fine-tuned dynamically. Gang scheduling also supports dynamic prioritization of jobs. This research simulates Gang scheduling based upon various policies and tries to find its adaptability to small- scale cluster. Arrival of the jobs to the system as well as the payloads and other interesting attributes are randomly generated. The generated jobs are sent to the simulated scheduler who in turn activates the desired nodes based on its current policy. The goal of this research is to provide a framework that can be used beyond the current simulation of schedul- ing policies. We investigate whether a timesharing policy is suitable also for cluster computing. If the answer is posi- tive, then we could replace the batch scheduling with an appropriate scheme satisfying the selection criteria. Other parameters such as migration policies and cost perform- ance analysis constitute important research elements. The rest of this paper is organized as follows. Section 2 describes related work. Section 3 provides details of the scheduling polices under investigation. The simulation SIMULATION OF JOB SCHEDULING FOR SMALL SCALE CLUSTERS Hassan Rajaei Mohammad Dadfar Pankaj Joshi Dept. of Computer Science Bowling Green State University Bowling Green, OH 43403, U.S.A. 1195 1-4244-0501-7/06/$20.00 ©2006 IEEE