A hybrid analytical/simulation optimization of Generalized Processor Sharing

With Generalized Processor Sharing (GPS), packets of different applications are backlogged in different queues and the different queues are served according to predetermined weights. It is well-established that GPS is a viable approach to provide different QoS for different applications. However, since the analysis of systems with GPS is a notoriously hard problem, it is not easy to find the weights that optimize GPS for some given objective function. The latter is important from a practical point of view. In this paper, we assume the objective function to be some weighted combination of (non-linear) increasing functions of the mean delays. We use results from strict priority scheduling (which can be regarded as a special case of GPS) to establish some exact theoretical bounds on when GPS is more optimal than strict priority. Some important case studies are included, thereby resorting to Monte-Carlo estimation to find the optimal weights for GPS systems.