A Product-form Network for Systems with Job Stealing Policies

Abstract

In queueing networks, product-form solutions are of fundamental importance to efficiently compute performance metrics in complex models of computer systems. The product-form property entails that the steady-state probabilities of the joint stochastic process underlying the network can be expressed as the normalized product of functions that only depend on the local state of the components. In many relevant cases, product-forms are the only way to perform exact quantitative analyses of large systems. In this work, we introduce a novel class of product-form queueing networks where servers are always busy. Applications include model of systems where successive refinements on jobs improve the processes quality but are not strictly required to obtain a result. To this aim, we define a job movement policy that admits instantaneous migrations of jobs from non-empty waiting buffers to empty ones. Thus, the resulting routing scheme is state-dependent. This class of networks maximizes the system throughput. This model can be implemented with arbitrary topology, including feedback, and both in an open and closed setting. As far as closed systems are concerned, we give a convolution algorithm and the corresponding mean value analysis to compute expected performance indices for closed models.