Abstract
In a wireless network, a basestation transmits data to mobiles at time-varying, mobile-dependent rates due to the ever changing nature of the communication channels. In this paper we consider a wireless system in which the channel conditions and data arrival processes are governed by an adversary. We first consider a single server and a set of users. At each time step t the server can only transmit data to one user. If user i is chosen the transmission rate is r/sub i/(t). We say that the system is (/spl omega/, /spl epsiv/)-admissible if in any window of /spl omega/ time steps the adversary can schedule the users so that the total data arriving to each user is at most 1 - /spl epsiv/ times the total service it receives. Our objective is to design on-line scheduling algorithms to ensure stability in an admissible system. We first show, somewhat surprisingly, that the admissibility condition alone does not guarantee the existence of a stable online algorithm, even in a subcritical system (i.e. /spl epsiv/ > 0). For example, if the nonzero rates in an infinite rate set can be arbitrarily small, then a subcritical system can be unstable for any deterministic online algorithm. On a positive note, we present a tracking algorithm that attempts to mimic the behavior of the adversary. This algorithm ensures stability for all (/spl omega/, /spl epsiv/)-admissible systems that are not excluded by our instability results. As a special case, if the rate set is finite, then the tracking algorithm is stable even for a critical system (i.e. /spl epsiv/ = 0). Moreover, the queue sizes are independent of e. For subcritical systems, we also show that a simpler max weight algorithm is stable as long as the user rates are bounded away from zero. The offline version of our problem resembles the problem of scheduling unrelated machines and can be modeled by an integer program. We present a rounding algorithm for its linear relaxation and prove that the rounding technique cannot be substantially improved. We conclude by discussing the extension of our model to the network setting.
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