Individual equilibrium dynamic routing in a multiple server retrial queue

TLDR: The existence of a unique symmetric Nash equilibrium policy is established and it is shown that its structure is characterized by a set of threshold-type strategies; the relevance of this concept in the context of a dynamic learning scenario is discussed.

Abstract: Customers arrive sequentially to a service system where the arrival times form a Poisson process of rate l. The system offers a choice between a private channel and a public set of channels. The transmission rate at each of the public channels is faster than that of the private one; however, if all of the public channels are occupied, then a customer who commits itself to using one of them attempts to connect after exponential periods of time with mean m−1. Once connection to a public channel has been made, service is completed after an exponential period of time, with mean n−1. Each customer chooses one of the two service options, basing its decision on the number of busy channels and reapplying customers, with the aim of minimizing its own expected sojourn time. The best action for an individual customer depends on the actions taken by subsequent arriving customers. We establish the existence of a unique symmetric Nash equilibrium policy and show that its structure is characterized by a set of threshold-type strategies; we discuss the relevance of this concept in the context of a dynamic learning scenario.