Peer-to-peer (P2P) systems provide a scalable way to stream content to multiple receivers over the Internet. The maximum rate achievable by all receivers is the capacity of a P2P streaming session. We provide a taxonomy of sixteen problem formulations, depending on whether there is a single P2P session or there are multiple concurrent sessions, whether the given topology is a full mesh graph or an arbitrary graph, whether the number of peers a node can have is bounded or not, and whether there are nonreceiver relay nodes or not. In each formulation, computing P2P streaming capacity requires the computation of an optimal set of multicast trees, with an exponential complexity, except in three simplest formulations that have been recently solved with polynomial time algorithms. These solutions, however, do not extend to the other more general formulations. In this paper, we develop a family of constructive, polynomial-time algorithms that can compute P2P streaming capacity and the associated multicast trees, arbitrarily accurately for seven formulations, to a factor of 4-approximation for two formulations, and to a factor of log of the number of receivers for two formulations. The optimization problem is reformulated in each case so as to convert the combinatorial problem into a linear program with an exponential number of variables. The linear program is then solved using a primal-dual approach. The algorithms combine an outer loop of primal-dual update with an inner loop of smallest price tree construction, driven by the update of dual variables in the outer loop. We show that when the construction of smallest price tree can be carried out arbitrarily accurately in polynomial time, so can the computation of P2P streaming capacity. We also develop several efficient algorithms for smallest price tree construction. Using the developed algorithms, we investigate the impact of several factors on P2P streaming capacity using topologies derived from statistics of uplink capacities of Internet hosts.

Two of the fundamental problems in peer-to-peer (P2P) streaming are as follows: what is the maximum streaming rate that can be sustained for all receivers, and what peering algorithms can achieve close to this maximum? These problems of computing and approaching the P2P streaming capacity are often challenging because of the constraints imposed on overlay topology. In this paper, we focus on the limit of P2P streaming rate under node degree bound, i.e., the number of connections a node can maintain is upper bounded. We first show that the streaming capacity problem under node degree bound is NP Complete in general. Then, for the case of node out-degree bound, through the construction of a “Bubble algorithm”, we show that the streaming capacity is at least half of that of a much less restrictive and previously studied case, where we bound the node degree in each streaming tree but not the degree across all trees. Then, for the case of node total-degree bound, we develop a “Cluster-Tree algorithm” that provides probabilistic guarantee of achieving a rate close to the maximum rate achieved under no degree bound constraint, when the node degree bound is logarithmic in network size. The effectiveness of these algorithms in approaching the capacity limit is demonstrated in simulations using uplink bandwidth statistics of Internet hosts. Both analysis and numerical experiments show that peering in a locally dense and globally sparse manner achieves near-optimal streaming rate if the degree bound is at least logarithmic in network size.

We address the following question in this study: Can a network application detect not only the occurrence, but also the location of congestion? Answering this question will not only help the diagnostic of network failure and monitor server's QoS, but also help developers to engineer transport protocols with more desirable congestion avoidance behavior. The paper answers this question through new analytic results on the two underlying technical difficulties: 1) synchronization effects of loss and delay in TCP, and 2) distributed hypothesis testing using only local loss and delay data. We present a practical Congestion Location Detection (CLD) algorithm that effectively allows an end host to distributively detect whether congestion happens in the local access link or in more remote links. We validate the effectiveness of CLD algorithm with extensive experiments.

Peer-assisted streaming is a promising way for service providers to offer high-quality IPTV to consumers at reasonable cost. In peer-assisted streaming, the peers exchange video chunks with one another, and receive additional data from the central server as needed. In this paper, we analyze how to provision resources for the streaming system, in terms of the server capacity, the video quality, and the depth of the distribution trees that deliver the content. We derive the performance bounds for minimum server load, maximum streaming rate, and minimum tree depth under different peer selection constraints. Furthermore, we show that our performance bounds are actually tight, by presenting algorithms for constructing trees that achieve our bounds.

Deterministic delay differential equation models, where the packet traffic is modeled as a fluid, are widely used to study congestion control algorithms in the Internet. In this paper, we point out some pitfalls in such fluid modeling of window flow control algorithms. Specifically, we argue that the modeling assumptions used to capture the variability in the RTT (due to queue length fluctuations) may play a critical role in our ability to design stable algorithms. We study two scenarios to illustrate the dramatic impact of RTT modeling. We first consider TCP-Reno with RED, and show that assuming that the RTT is a constant (when it is actually time-varying) leads to conservative parameter choices, i.e., the system continues to be stable even with variable RTT. On the other hand, for the recently proposed stabilized Vegas, we show the following result: while the network can be stabilized under the constant RTT assumption, there is no choice of parameters that would stabilize the system when the RTT variations are taken into account. Interestingly, such problems do not arise if the congestion-control mechanisms at the end-users are rate-based.

This paper introduces and analyzes a decentralized network congestion control algorithm which has dynamic adaptations at both user ends and link ends, a so-called general primal-dual algorithm. We obtain sufficient conditions for local stability of this algorithm in a general topology network with heterogeneous round-trip delays. Then, as an implementation of this algorithm in the Internet, we introduce an AQM (Active Queue Management) scheme called Exponential-RED (E-RED), which outperforms RED and is inherently stable when combined with TCP-Reno or its variants for high-speed networks.