Analyzing and Minimizing the Impact of Opportunity Cost in QoS-aware Job Scheduling

Quality of service (QoS) mechanisms allowing users to request for turn-around time guarantees for their jobs have recently generated much interest. In our previous work we had designed a framework, QoPS, to allow for such QoS. This framework provides an admission control mechanism that only accepts jobs whose requested deadlines can be met and, once accepted, guarantees these deadlines. However, the framework is completely blind to the revenue these jobs can fetch for the supercomputer center. By accepting a job, the supercomputer center might relinquish its capability to accept some future arriving (and potentially more expensive) jobs. In other words, while each job pays an explicit price to the system for running it, the system may also be viewed as paying an implicit opportunity cost by accepting the job. Thus, accepting a job is profitable only when the job´s price is higher than its opportunity cost. In this paper we analyze the impact such opportunity cost can have on the overall revenue of the supercomputer center and attempt to minimize it through predictive techniques. Specifically, we propose two extensions to QoPS, value-aware QoPS (VQoPS) and dynamic value-aware QoPS (DVQoPS), to provide such capabilities. We present detailed analysis of these schemes and demonstrate using simulation that they not only achieve several factors improvement in system revenue, but also good service differentiation as a much desired side-effect.