Using Simulation, Historical and Hybrid Estimation Systems for Enhacing Job Scheduling on NOWs

The computation capacity of the workstations in an open laboratory is enough to execute not only the local workload but some distributed computation. Unfortunately, the local workload introduces much uncertainty into the predictability of the system, which hinders the applicability of the job scheduling strategies. In this work, we introduce an estimation engine into our job scheduling system, termed CISNE. This prediction capacity allows us guarantee some limits to the turnaround time of parallel jobs. With this aim, three different estimation methods have been proposed and implemented in the CISNE system: a simulation tool, a historical system and an integration of both (hybrid). In this framework, we have compared our proposals to representative estimation methods in the literature. Likewise, we have analyzed these estimation methods in relation to different scheduling policies. These results reveal that the hybrid method achieves the best performance due to the fact that it combines the flexibility of a simulator to represent such a dynamic system as a non-dedicated cluster together with the accuracy given by the historical information