an analytical model for generalized processor sharing scheduling with heterogeneous network traffic
Implementation of differentiated Quality-of-Service (QoS) in next-generation computer networks has received increasing research interests from both academia and industry. The Generalized Processor Sharing (GPS) scheduling strategy has been widely studied as a promising way to provide differentiated QoS due to its service protection feature. Most of the previous studies reported in the literature, however, have focused on the analysis of GPS under either Short Range Dependent (SRD) or Long Range Dependent (LRD) traffic only, neither of which is able to capture the heterogeneous properties of realistic traffic in multi-service networks solely. To fill this gap, this paper develops a new analytical performance model for GPS systems subject to both LRD self-similar traffic and SRD Poisson traffic. More specifically, using an approach based on Large Deviation Principles, this study contributes to performance modelling and evaluation of GPS scheduling by deriving the analytical upper and lower bounds of the aggregate and individual queue length distributions of heterogeneous traffic flows. The comparisons between analytical bounds and extensive simulation results validate the accuracy and merits of the analytical model which can be adopted as a practical and cost-effective evaluation tool for investigating the performance behaviour of GPS systems under heterogeneous network traffic with various parameter settings.
generalized processor sharing short range dependence long range dependence self-similar traffic large deviation principle