CoMoM: Efficient Class-Oriented Evaluation of Multiclass Performance Models

We introduce the class-oriented method of moments (CoMoM), a new exact algorithm to compute performance indexes in closed multiclass queuing networks. Closed models are important for performance evaluation of multitier applications, but when the number of service classes is large, they become too expensive to solve with exact methods such as mean value analysis (MVA). CoMoM addresses this limitation by a new recursion that scales efficiently with the number of classes. Compared to the MVA algorithm, which recursively computes mean queue lengths, CoMoM also carries on in the recursion information on higher-order moments of queue lengths. We show that this additional information greatly reduces the number of operations needed to solve the model and makes CoMoM the best-available algorithm for networks with several classes. We conclude the paper by generalizing CoMoM to the efficient computation of marginal queue-length probabilities, which finds application in the evaluation of state-dependent attributes such as quality-of-service metrics.