Simulation and performance evaluation of mission critical dispatching systems

Abstract

Dispatching systems, where jobs are routed to servers immediately upon arrival, appear frequently in parallel computing systems of different scales. With a dynamic dispatching policy, the system is generally analytically intractable and performance evaluation is carried out by means of Monte Carlo simulations. A typical performance metric is the mean response time which is often easy to estimate. In contrast, we consider systems where events generating costs are extremely rare. In our reference system, jobs have deadlines for waiting time. When deadlines are loose when compared to the system’s load, novel rare event simulation techniques must be applied. We consider both conditioning and importance sampling to this end. In numerical examples, the proposed techniques decrease the simulation time by several orders of magnitude. We also discover interesting performance relationships among the classical dispatching policies; Random split (RND), Round-robin (RR), Join-the-shortest-queue (JSQ) and Least-work-left (LWL).