Selfish Routing on Dynamic Flows

Selfish routing on dynamic flows over time is used to model scenarios that vary with time in which individual agents act in their best interest. In this paper we provide a survey of a particular dynamic model, the deterministic queuing model, and discuss how the model can be adjusted and applied to different real-life scenarios. We then examine how these adjustments affect the computability, optimality, and existence of selfish routings.Comment: Oberlin College Computer Science Honors Thesis. Supervisor: Alexa Sharp, Oberlin Colleg

An Empirical Model of Packet Processing Delay of the Open vSwitch

Network virtualization offers flexibility by decoupling virtual network from the underlying physical network. Software-Defined Network (SDN) could utilize the virtual network. For example, in Software-Defined Networks, the entire network can be run on commodity hardware and operating systems that use virtual elements. However, this could present new challenges of data plane performance. In this paper, we present an empirical model of the packet processing delay of a widely used OpenFlow virtual switch, the Open vSwitch. In the empirical model, we analyze the effect of varying Random Access Memory (RAM) and network parameters on the performance of the Open vSwitch. Our empirical model captures the non-network processing delays, which could be used in enhancing the network modeling and simulation

Energy-aware cooperative wireless networks with multiple cognitive users

In this paper, we study and analyze cooperative cognitive radio networks with arbitrary number of secondary users (SUs). Each SU is considered a prospective relay for the primary user (PU) besides having its own data transmission demand. We consider a multi-packet transmission framework that allows multiple SUs to transmit simultaneously because of dirty-paper coding. We propose power allocation and scheduling policies that optimize the throughput for both PU and SU with minimum energy expenditure. The performance of the system is evaluated in terms of throughput and delay under different opportunistic relay selection policies. Toward this objective, we present a mathematical framework for deriving stability conditions for all queues in the system. Consequently, the throughput of both primary and secondary links is quantified. Furthermore, a moment generating function approach is employed to derive a closed-form expression for the average delay encountered by the PU packets. Results reveal that we achieve better performance in terms of throughput and delay at lower energy cost as compared with equal power allocation schemes proposed earlier in the literature. Extensive simulations are conducted to validate our theoretical findings

Echo State Queueing Network: a new reservoir computing learning tool

In the last decade, a new computational paradigm was introduced in the field of Machine Learning, under the name of Reservoir Computing (RC). RC models are neural networks which a recurrent part (the reservoir) that does not participate in the learning process, and the rest of the system where no recurrence (no neural circuit) occurs. This approach has grown rapidly due to its success in solving learning tasks and other computational applications. Some success was also observed with another recently proposed neural network designed using Queueing Theory, the Random Neural Network (RandNN). Both approaches have good properties and identified drawbacks. In this paper, we propose a new RC model called Echo State Queueing Network (ESQN), where we use ideas coming from RandNNs for the design of the reservoir. ESQNs consist in ESNs where the reservoir has a new dynamics inspired by recurrent RandNNs. The paper positions ESQNs in the global Machine Learning area, and provides examples of their use and performances. We show on largely used benchmarks that ESQNs are very accurate tools, and we illustrate how they compare with standard ESNs.Comment: Proceedings of the 10th IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, USA, 201