Improving Resource Sharing In Computer Networks With Stochastic Scheduling

Improving Resource Sharing in Computer Networks with Stochastic Scheduling

In the current thesis we propose several new contributions to improve the performance of computer networks. The obtained results concern the resource sharing problems in the Internet routers, Web servers and operating systems. We study several stochastic scheduling algorithms which decrease the mean waiting time in the system with efficient resource sharing and provide the possibility to introduce the Quality of Service and flow differentiation to the networks. We show the effectiveness of the proposed algorithms and study the possibility of their implementation in the router queues. The most important obtained results are the following. For the Two Level Processor Sharing scheduling discipline with the hyper-exponential job size distribution with two phases we find an approximation for the optimal value of the threshold that minimizes the expected sojourn time. With the simulation results (NS-2) we show that TLPS improves significantly the system performance when the found approximation of the optimal threshold is used. We study the Discriminatory Processor Sharing policy and show the monotonicity of the expected sojourn time in the system depending on the weight vector under certain conditions on the system. We apply the Gittins optimality result to characterize the optimal scheduling discipline in a multi-class single server queue. The found policy minimizes the mean sojourn time in the system between all non-anticipating scheduling policies. In several cases of practical interest we describe the policy structure and provide the simulation results (NS-2). For the the congestion control problem in the networks we propose a new flow-aware algorithm to improve the fair resource sharing of the bottleneck capacity.

Annotated Bibliography of the Literature on Resource Sharing Computer Networks

Modern Trends in Controlled Stochastic Processes:

This book presents state-of-the-art solution methods and applications of stochastic optimal control. It is a collection of extended papers discussed at the traditional Liverpool workshop on controlled stochastic processes with participants from both the east and the west. New problems are formulated, and progresses of ongoing research are reported. Topics covered in this book include theoretical results and numerical methods for Markov and semi-Markov decision processes, optimal stopping of Markov processes, stochastic games, problems with partial information, optimal filtering, robust control, Q-learning, and self-organizing algorithms. Real-life case studies and applications, e.g., queueing systems, forest management, control of water resources, marketing science, and healthcare, are presented. Scientific researchers and postgraduate students interested in stochastic optimal control,- as well as practitioners will find this book appealing and a valuable reference. ​