The goal of this paper is to minimize service disruption, a unique phenomenon in peer-to-peer (P2P) streaming system and the major contributing factor to degrade user experience. In order to improve its streaming quality and stability, a P2P system should select peers with abundant bandwidth resource and prospect longevity as the parents of other peers. Towards this end, we propose PRW (peer resilient weight), a metric to evaluate a peer's healthiness regarding these two aspects. The theoretical background of PRW originates from the optimization framework based on the generalized flow theory. To show PRW's applicability to a wide variety of P2P streaming solutions, we propose several parent selection algorithms guided by PRW under the single-tree, multi-tree, and mesh structures. Using MSN and PPLive traces, we find our algorithms to be able to achieve lower service disruption and lower peer rejection, compared to several existing heuristics and algorithms.

Peer-to-peer (P2P) traffic is arguably the biggest contributor to today's Internet traffic. So far, very limited effort has been paid to theoretically analyze the effect of bringing locality-awareness into P2P applications. In this paper, we analyze three locality-awareness policies for BitTorrent-like system: tracker locality(neighbor selection), choker locality(choking and unchoking) and picker locality(piece picking). Based on an H-sphere model which captures the essential property of Internet topology, we analyze how much network load saving can be expected for these locality policies, as well as their impact to the downloading efficiency of the system. Through analytical study and simulation, we have the following findings. First, when the system enters the steady state, the locality policies proposed in this paper can achieve the same downloading efficiency as the standard BitTorrent system. Second, these locality policies can significantly alleviate the traffic load on the Internet. Finally, we find that there exists no all-around winner. Instead, each policy performs the best under different system configuration parameters, such as number of unchoked peers.

Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best solution, existing work proposes to formulate the mesh network routing problem as an optimization problem. In this problem formulation, traffic demand is usually implicitly assumed as static and known a priori. Contradictorily, recent studies of wireless network traces show that the traffic demand, even being aggregated at access points, is highly dynamic and hard to estimate. Thus, in order to apply the optimization-based routing solution into practice, one must take into account the dynamic and unpredictable nature of wireless traffic demand. This paper presents an integrated framework for network routing in multi-radio multi-channel wireless mesh networks under dynamic traffic demand. This framework consists of two important components: traffic estimation and routing optimization. By analyzing the traces collected at wireless access points, the traffic estimation component predicts future traffic demand based on its historical value using time-series analysis, and represents the prediction result in two forms - mean value and statistical distribution. The optimal mesh network routing strategies then take these two forms of traffic demand estimations as inputs. In particular, two routing algorithms are proposed based on linear programming which consider the mean value and the statistical distribution of the predicted traffic demands, respectively. The trace-driven simulation study demonstrates that our integrated traffic estimation and routing optimization framework can effectively incorporate traffic dynamics in mesh network routing, where both algorithms outperform the shortest path algorithm in about 80% of the test cases.

This paper considers two important issues for video sensor networks: (1) timely delivery of captured video stream and (2) energy-efficient network design. Based on network calculus, it presents a unified analytical framework that is able to quantitatively weigh the tradeoff between these two factors. In particular, it derives the service curve, buffer and delay bound under single-hop and multi-hop scenarios for video sensor networks under power management. To the best of our knowledge, this is the first work that extends the network calculus theory to the domain of wireless network with the consideration of power management and wireless interference. Our analysis has been validated through experiments conducted on a video sensor network testbed.