QoS-aware parallel job scheduling framework for simulation execution as a service

Abstract

Cloud computing is attracting an increased number of researches in delivering modeling and simulation abilities as a service. Among which, simulation execution as a service (EaaS) is a hot spot. It aims at releasing users from complex running configurations and meanwhile guaranteeing the QoS requirements. Under the motivation, focusing on EaaS for parallel and distributed simulation (PADS) application, the paper proposes a QoS-aware job scheduling framework in two-tier virtualization-based private cloud data center. In PADS EaaS, an adaptive job size adjustment component is designed to realize intelligent and adaptive job size setting for PADS instead of assigning by users. Furthermore, an adaptive deadline-aware job size adjustment algorithm, named ADaSA, is designed in the adjustment component to realize efficient job scheduling with high job responsiveness. ADaSA algorithm firstly computes a minimum processor requested that leads to maximum runtime stretch. It makes sure that more jobs can be scheduled at the same time while satisfying current job's deadline requirements. On other hand, ADaSA tries to pick up all possible idle CPU time in background virtual machines and reserved ones for other jobs. Through that way, more chances are generated to response more jobs in waiting queue. Finally, we conduct extensive experiments with trace-driven simulation. The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time (up to 90%) and bounded slow down (up to 95%), and at the same time guarantees approximately equivalent deadline-missed rate. ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate (up to 60%).