Performance Analysis of Hybrid Polling Schemes with Multiple Classes of Self-Similar and Long-Range Dependent Traffic Input

Queuing delay have significant impact on the performance of network applications. To meet distinct delay requirements of multi-classend-user traffic,various queuing and scheduling schemes have been proposed. These schemes are analogous to a polling mechanism in which multiple traffic queues are concurrently handled by a single scheduler. However, researchers were unable to analyze this synergy between the conventional queuing-cum-scheduling and polling models. Moreover, research on analyzing polling models assumed traditional Poisson traffic distribution which is unable to capture self-similar and long-range dependent (LRD) characteristics and hence yield misleading results. Furthermore, published work related to self-similar traffic modeling is mainly based on conventional queuing-cum-scheduling which are simple approximations. The objective of this work is to analyze different combinations of conventional queuing and polling models. In this paper, we exploit the synergy between traditional queuing-cum-scheduling and polling models. We analyze different combinations of queuing and polling mechanisms with realistic traffic distributions i.e., self-similar and LRD. An analytical framework for G/M/1 queuing system is developed which contemplates multiple classes of self-similar and LRD traffic as input. We formulate the Markov chain for G/M/1 queuing system and extract closed-form expressions of queuing delay for corresponding traffic classes. We analyze a combination of limited service polling model with non-pre-emptive priority queuing. Different combinations of polling models (i.e., exhaustive, gated and limited service) are also analyzed. We validate the performance of the proposed analytical framework through simulations. Simulation results suggest that synergy of polling and schedulingdangle promising results.