Reducing the Download Time in Stochastic P2P Content Delivery Networks by Improving Peer Selection

Peer-to-peer (P2P) applications have become a popular method for obtaining digital content. Recent research has shown that the amount of time spent downloading from a poor performing peer effects the total download duration. Current peer selection strategies attempt to limit the amount of time spent downloading from a poor performing peer, but they do not use both advanced knowledge and service capacity after the connection has been made to aid in peer selection. Advanced knowledge has traditionally been obtained from methods that add additional overhead to the P2P network, such as polling peers for service capacity information, using round trip time techniques to calculate the distance between peers, and by using tracker peers. This work investigated the creation of a new download strategy that replaced the random selection of peers with a method that selects server peers based on historic service capacity and ISP in order to further reduce the amount of time needed to complete a download session. Peer-to-peer (P2P) applications have become a popular method for obtaining digital content. Recent research has shown that the amount of time spent downloading from a poor performing peer effects the total download duration. Current peer selection strategies attempt to limit the amount of time spent downloading from a poor performing peer, but they do not use both advanced knowledge and service capacity after the connection has been made to aid in peer selection. Advanced knowledge has traditionally been obtained from methods that add additional overhead to the P2P network, such as polling peers for service capacity information, using round trip time techniques to calculate the distance between peers, and by using tracker peers. This work investigated the creation of a new download strategy that replaced the random selection of peers with a method that selects server peers based on historic service capacity and ISP in order to further reduce the amount of time needed to complete a download session. The results of this new historic based peer selection strategy have shown that there are benefits in using advanced knowledge to select peers and only replacing the worst performing peers. This new approach showed an average download duration improvement of 16.6% in the single client simulation and an average cross ISP traffic reduction of 55.17% when ISPs were participating in cross ISP throttling. In the multiple clients simulation the new approach showed an average download duration improvement of 53.31% and an average cross ISP traffic reduction of 88.83% when ISPs were participating in cross ISP throttling. This new approach also significantly improved the consistency of the download duration between download sessions allowing for the more accurate prediction of download times

Modeling and Evaluation of Multisource Streaming Strategies in P2P VoD Systems

In recent years, multimedia content distribution has largely been moved to the Internet, inducing broadcasters, operators and service providers to upgrade with large expenses their infrastructures. In this context, streaming solutions that rely on user devices such as set-top boxes (STBs) to offload dedicated streaming servers are particularly appropriate. In these systems, contents are usually replicated and scattered over the network established by STBs placed at users' home, and the video-on-demand (VoD) service is provisioned through streaming sessions established among neighboring STBs following a Peer-to-Peer fashion. Up to now the majority of research works have focused on the design and optimization of content replicas mechanisms to minimize server costs. The optimization of replicas mechanisms has been typically performed either considering very crude system performance indicators or analyzing asymptotic behavior. In this work, instead, we propose an analytical model that complements previous works providing fairly accurate predictions of system performance (i.e., blocking probability). Our model turns out to be a highly scalable, flexible, and extensible tool that may be helpful both for designers and developers to efficiently predict the effect of system design choices in large scale STB-VoD system

Reducing BitTorrent Download Time via Handshake-Based Switching

Peer-to-peer networking overcomes the single point of failure and bandwidth limitations inherent to the centralized server model of file-sharing. It is both a popular means of sharing digital content and a major consumer of internet traffic, with BitTorrent being the most-used protocol. As such, significant research has gone into improving peer-to-peer performance in order to reduce both download times and networking costs. One aspect that can affect performance is the client’s selection of peers to download from, as the time spent downloading from even a single poor-performing peer can impact the overall download duration. A recent peer selection strategy explored having a client use historical knowledge acquired through third-party sources, as well as its own first-hand experience with previously visited peers, as a means of selecting likely good-performers, coupled with a peer switching strategy that replaced peers whose post-selection downloads exhibited poor performance contrary to what historical knowledge suggested in order to limit the time spent downloading from said poor-performers Though this tactic demonstrated reduced download times compared to various past works, it still suffered from poor peer selection due to its historical knowledge not necessarily reflecting the current state of the peers. This work introduced and examined an enhancement to this hybrid peer selection and switching strategy by adding current intelligence regarding a peer’s available bandwidth, all the while avoiding the additional network costs associated with performing on-the-fly probing or querying techniques utilized by other peer selection strategies to benchmark prospective peers. With such on-the-fly knowledge about a peer’s current bandwidth availability, this new enhanced strategy quickly replaced poor performers without waiting for downloads to be performed and subsequently benchmarked, resulting in reduced overall peer-to-peer download times. The results of adding this pre-download peer switching enhancement demonstrated improved download performance, particularly in early file transfer runs. However, as more runs occurred and the benefits of the original strategy’s historical knowledge became more pronounced, the time savings gained from this new enhancement diminished

Improving file distribution performance by grouping in peer-to-peer networks

It has been shown that the peer-to-peer paradigm is more efficient than the traditional client-server model for file sharing among a large number of users. Given a group of leechers who wants to download a single file and a group of seeds who possesses the whole file, the minimum time needed for distributing the file to all users can be calculated based on their bandwidth availabilities. A scheduling algorithm has been developed so that every leecher can obtain the file within this minimum time. Unfortunately, this mechanism is not optimal with regard to the average download time among the peers. In this paper, we study how to reduce the average download time without prolonging the time needed for all leechers to obtain the file from a theoretical perspective. Based on the bandwidth capacities, the seeds and leechers are divided into different groups. We identify the necessary conditions for grouping to bring about benefits. We also study the impact on performance when leechers leave the system before the downloading process is complete. To evaluate our mechanism, we conduct extensive simulations and compare the performance with a BitTorrentlike file sharing algorithm. The results show that our grouping protocol successfully reduces the average download time over a wide range of system configurations. © 2009 IEEE.published_or_final_versio

AngelCast: cloud-based peer-assisted live streaming using optimized multi-tree construction

Increasingly, commercial content providers (CPs) offer streaming solutions using peer-to-peer (P2P) architectures, which promises significant scalabil- ity by leveraging clients’ upstream capacity. A major limitation of P2P live streaming is that playout rates are constrained by clients’ upstream capac- ities – typically much lower than downstream capacities – which limit the quality of the delivered stream. To leverage P2P architectures without sacri- ficing quality, CPs must commit additional resources to complement clients’ resources. In this work, we propose a cloud-based service AngelCast that enables CPs to complement P2P streaming. By subscribing to AngelCast, a CP is able to deploy extra resources (angel), on-demand from the cloud, to maintain a desirable stream quality. Angels do not download the whole stream, nor are they in possession of it. Rather, angels only relay the minimal fraction of the stream necessary to achieve the desired quality. We provide a lower bound on the minimum angel capacity needed to maintain a desired client bit-rate, and develop a fluid model construction to achieve it. Realizing the limitations of the fluid model construction, we design a practical multi- tree construction that captures the spirit of the optimal construction, and avoids its limitations. We present a prototype implementation of AngelCast, along with experimental results confirming the feasibility of our service.Supported in part by NSF awards #0720604, #0735974, #0820138, #0952145, #1012798 #1012798 #1430145 #1414119. (0720604 - NSF; 0735974 - NSF; 0820138 - NSF; 0952145 - NSF; 1012798 - NSF; 1430145 - NSF; 1414119 - NSF

Cloud-based Content Distribution on a Budget

To leverage the elastic nature of cloud computing, a solution provider must be able to accurately gauge demand for its offering. For applications that involve swarm-to-cloud interactions, gauging such demand is not straightforward. In this paper, we propose a general framework, analyze a mathematical model, and present a prototype implementation of a canonical swarm-to-cloud application, namely peer-assisted content delivery. Our system – called Cyclops – dynamically adjusts the off-cloud bandwidth consumed by content servers (which represents the bulk of the provider's cost) to feed a set of swarming clients, based on a feedback signal that gauges the real-time health of the swarm. Our extensive evaluation of Cyclops in a variety of settings – including controlled PlanetLab and live Internet experiments involving thousands of users – show significant reduction in content distribution costs (by as much as two orders of magnitude) when compared to non-feedback-based swarming solutions, with minor impact on content delivery times

A Game Theoretic Analysis of Incentives in Content Production and Sharing over Peer-to-Peer Networks

User-generated content can be distributed at a low cost using peer-to-peer (P2P) networks, but the free-rider problem hinders the utilization of P2P networks. In order to achieve an efficient use of P2P networks, we investigate fundamental issues on incentives in content production and sharing using game theory. We build a basic model to analyze non-cooperative outcomes without an incentive scheme and then use different game formulations derived from the basic model to examine five incentive schemes: cooperative, payment, repeated interaction, intervention, and enforced full sharing. The results of this paper show that 1) cooperative peers share all produced content while non-cooperative peers do not share at all without an incentive scheme; 2) a cooperative scheme allows peers to consume more content than non-cooperative outcomes do; 3) a cooperative outcome can be achieved among non-cooperative peers by introducing an incentive scheme based on payment, repeated interaction, or intervention; and 4) enforced full sharing has ambiguous welfare effects on peers. In addition to describing the solutions of different formulations, we discuss enforcement and informational requirements to implement each solution, aiming to offer a guideline for protocol designers when designing incentive schemes for P2P networks.Comment: 31 pages, 3 figures, 1 tabl