Optimization flow control with estimation error

We analyze the effects of price estimation error in a dual-gradient optimization flow control scheme, and characterize the performance of the algorithm in this case. By treating estimation error as inexactness of the gradient, we utilize sufficient conditions for convergence subject to bounded error to characterize the long-term dynamics of the link utilization in terms of a region, which the trajectory enters in finite time. We explicitly find bounds for this region under a particular quantization error model, and provide simulation results to verify the predicted behavior of the system. Finally, we analyze the effects of the stepsize on the convergence of the algorithm, and provide analytical and numerical results, which suggest a particular choice for this parameter

Asynchronous Distributed Averaging on Communication Networks

Distributed algorithms for averaging have attracted interest in the control and sensing literature. However, previous works have not addressed some practical concerns that will arise in actual implementations on packet-switched communication networks such as the Internet. In this paper, we present several implementable algorithms that are robust to asynchronism and dynamic topology changes. The algorithms are completely distributed and do not require any global coordination. In addition, they can be proven to converge under very general asynchronous timing assumptions. Our results are verified by both simulation and experiments on Planetlab, a real-world TCP/IP network. We also present some extensions that are likely to be useful in applications

Distributed Averaging and Efficient File Sharing on Peer-to-Peer Networks

The work presented in this thesis is mainly divided in two parts. In the first part we study the problem of distributed averaging, which has attracted a lot of interest in the research community in recent years. Our work focuses on the issues of implementing distributed averaging algorithms on peer-to-peer networks such as the Internet. We present algorithms that eliminate the need for global coordination or synchronization, as many other algorithms require, and show mathematical analysis of their convergence. Discrete-event simulations that verify the theoretical results are presented. We show that the algorithms proposed converge rapidly in practical scenarios. Real-world experiments are also presented to further corroborate these results. We present experiments conducted on the PlanetLab research network. Finally, we present several promising applications of distributed averaging that can be implemented in a wide range of areas of interest. The second part of this thesis, also related to peer-to-peer networking, is about modelling and understanding peer-to-peer file sharing. The BitTorrent protocol has become one of the most popular peer-to-peer file sharing systems in recent years. Theoretical understanding of the global behavior of BitTorrent and similar peer-to-peer file sharing systems is however not very complete yet. We study a model that requires very simple assumptions yet exhibits a lot structure. We show that it is possible to consider a wide range of performance criteria within the framework, and that the model captures many of the important issues of peer-to-peer file sharing. We believe the results provide fundamental insights to practical peer-to-peer file sharing systems. We show that many optimization criteria can be studied within our framework. Many new directions of research are also opened up.</p

Optimization Flow Control with Estimation Error

We analyze the effects of price estimation error in a dual-gradient optimization flow control scheme, and characterize the performance of the algorithm in this case. By treating estimation error as inexactness of the gradient, we utilize sufficient conditions for convergence subject to bounded error to characterize the long-term dynamics of the link utilization in terms of a region which the trajectory enters in finite time. We explicitly find bounds for this region under a particular quantization error model, and provide simulation results to verify the predicted behavior of the system. Finally, we analyze the effects of the stepsize on the convergence of the algorithm, and provide analytical and numerical results which suggest a particular choice for this parameter

Optimal strategies for efficient peer-to-Peer file sharing

We study a model for peer-to-peer file sharing. The goal is to distribute a file from a server to multiple peers. We assume the upload capacity of each peer is the only bottleneck. We examine the finish times of peers under different transmission strategies. Pareto optimality, min-max finish time, and optimal average finish time of the model are studied. We believe the results provide fundamental insights into practical peer-to-peer systems such as BitTorrent

Modelling peer-to-peer file dissemination

We investigate a model for peer-to-peer file sharing. The goal is to distribute a file from a server to multiple peears. Each peer has limited upload capacity and can upload previously downloaded pieces to other peears. We examine the finish times of peers under different transmission strategies. Pareto optimality, min-max finish time, and optimal average finish time of the model are studied. We believe the model provides insite into practical peer-to-peer systems such as Bittorrent

Distributed averaging on asynchronous communication networks

Abstract — Distributed algorithms for averaging have attracted interest in the control and sensing literature. However, previous works have not addressed some practical concerns that will arise in actual implementations on packet-switched communication networks such as the Internet. In this paper, we present several implementable algorithms that are robust to asynchronism and dynamic topology changes. The algorithms do not require global coordination and can be proven to converge under very general asynchronous timing assumptions. Our results are verified by both simulation and experiments on a real-world TCP/IP network. I