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The ability of a P2P network to scale its throughput up in proportion to the arrival rate of peers has recently been shown to be crucially dependent on the chunk sharing policy employed. Some policies can result in low frequencies of a particular chunk, known as the missing chunk syndrome, which can dramatically reduce throughput and lead to instability of the system. For instance, commonly used policies that nominally “boost” the sharing of infrequent chunks such as the well-known rarest-first algorithm have been shown to be unstable. We take a complementary viewpoint, and instead consider a policy that simply prevents the sharing of the most frequent chunk(s), that we call mode-suppression. We also consider a more general version that suppresses the mode only if the mode frequency is larger than the lowest frequency by a fixed threshold. We prove the stability of mode-suppression using Lyapunov techniques, and use a Kingman bound argument to show that the total download time does not increase with peer arrival rate. We then design versions of mode-suppression that sample a small number of peers at each time, and construct noisy mode estimates by aggregating these samples over time. We show numerically that mode suppression stabilizes and outperforms all other recently proposed chunk sharing algorithms, and via integration into BitTorrent implementation operating over the ns-3 that it ensures stable, low sojourn time operation in a real-world setting. [ABSTRACT FROM AUTHOR]

With the rapid growth in clean distributed energy resources involving micro-generation and flexible loads, users can actively manage their own energy and have the capability to enter in a market of energy services as prosumers while reducing their carbon footprint. The coordination between these distributed energy resources is essential in order to ensure fair trading and equality in resource sharing among a community of prosumers. Peer-to-Peer (P2P) networks can provide the underlying mechanisms for supporting such coordination and offer incentives to prosumers to participate in the energy market. In particular, the federation of energy clusters with P2P networks has the potential to unlock access to energy resources and lead to the development of new energy services in a fast-growing sharing energy economy. In this paper, we present the formation and federation of smart energy clusters using P2P networks with a view to decentralise energy markets and enable access and use of clean energy resources. We implement a P2P framework to support the federation of energy clusters and study the interaction of consumers and producers in a market of energy resources and services. We demonstrate how energy exchanges and energy costs in a federation are influenced by the energy demand, the size of energy clusters and energy types. We conduct our modelling and analysis based on a real fish industry case study in Milford Haven, South Wales, as part of the EU H2020 INTERREG piSCES project.

Since the introduction of Bitcoin in 2009, it has gained a significant popularity around the world. Bitcoin is a peer-to-peer electronic payment system where payment transactions are stored in a data structure named the block-chain. Based on a fully decentralized network, the blockchain is maintained by a community of participants. In Bitcoin system, mining is the fundamental concept. In this paper, we design a new Bitcoin mining scheme based on the multi-leader multi-follower Stackelberg game model. To effectively implement the peer-to-peer relationship of Bitcoin system agents, we assume that mining pool operators are leaders and mining participating users are followers in our Stackelberg game. By using the dynamics of feedback-based repeated process, leaders, and followers can be interacting with one another and make their decisions in a way to reach an efficient system solution. Without the influence of any central authorities and organizations, the proposed method is practically applied to a distributed Bitcoin system. Through system level simulations, we show that our game approach outperforms the existing Bitcoin schemes in providing a better fair-efficient system performance.

Real-world applications, such as peer-to-peer (P2P) networks, e-commerce and social networks, usually employ reputation systems to provide guidance in selecting trustworthy node for high system reliability and security. A reputation system computes and publishes reputation score for each node based on a collection of opinions from others about the node. However, collusion behaviors impair the effectiveness of reputation systems in trustworthy node selection. Though many reputation calculation methods have been proposed to mitigate collusion's influence, little effort has been devoted to specifically tackling collusion. Based on the important collusion behavior characteristics in reputation evaluation and influence on reputation values, we propose a basic collusion detection method to specifically detect suspicious collusion behaviors in pairs. We further optimize the method by reducing the computing overhead. We also propose two pre-processing methods to firstly identify partial reputation raters of a node that are more likely to be colluders before applying the collusion detection method on them, thus reducing the collusion detection overhead. Extensive experimental results show that our proposed methods can significantly enhance the capability of existing reputation systems to detect collusion with low overhead. Also, the pre-processing methods are effective in reducing the collusion detection overhead without affecting the collusion detection accuracy. [ABSTRACT FROM AUTHOR]

Beyond offering the widely used keyword search function, many peer-to-peer systems nowadays support the subscription function. For example, Vuze allows users to create subscription filters based on the keyword search. Given the subscription, episodic or related content will be delivered to the users whenever new episodes are available. Unfortunately, these applications suffer from the downsides, for example, high network traffic in the nodes maintaining popular terms. In this paper, we propose the MTAF mechanism to overcome the issues. The key of MTAF is to carefully select a subset of terms without incurring false negatives and to forward the content item toward the home nodes of such selected terms for low content forwarding cost. Experimental results based on real datasets indicate that the proposed solutions are efficient compared to existing approaches. In particular, the similarity-based replication of filters is shown to mitigate the effect of hot spots that arise due to the fact that some document terms are substantially more popular than the others. [ABSTRACT FROM AUTHOR]

Video sharing has been an increasingly popular application in online social networks (OSNs). However, its sustainable development is severely hindered by the intrinsic limit of the client/server architecture deployed in current OSN video systems, which is not only costly in terms of server bandwidth and storage but also not scalable with the soaring amount of users and video content. The peer-assisted Video-on-Demand (VoD) technique, in which participating peers assist the server in delivering video content, has been proposed recently. Unfortunately, videos can only be disseminated through friends in OSNs. Therefore, current VoD works that explore clustering nodes with similar interests or close location for high performance are suboptimal, if not entirely inapplicable, in OSNs. Based on our long-term real-world measurement of over 1,000,000 users and 2,500 videos on Facebook, we propose SocialTube, a novel peer-assisted video sharing system that explores social relationship, interest similarity, and physical location between peers in OSNs. Specifically, SocialTube incorporates four algorithms: a social network (SN)-based P2P overlay construction algorithm, an SN-based chunk prefetching algorithm, chunk delivery, and scheduling algorithm, and a buffer management algorithm. Experimental results from a prototype on PlanetLab and an event-driven simulator show that SocialTube can improve the quality of user experience and system scalability over current P2P VoD techniques. [ABSTRACT FROM PUBLISHER]

Overlay network topology together with peer/data organization and search algorithm are the crucial components of unstructured peer-to-peer (P2P) networks as they directly affect the efficiency of search on such networks. Scale-free (power-law) overlay network topologies are among structures that offer high performance for these networks. A key problem for these topologies is the existence of hubs, nodes with high connectivity. Yet, the peers in a typical unstructured P2P network may not be willing or able to cope with such high connectivity and its associated load. Therefore, some hard cutoffs are often imposed on the number of edges that each peer can have, restricting feasible overlays to limited or truncated scale-free networks. In this paper, we analyze the growth of such limited scale-free networks and propose two different algorithms for constructing perfect scale-free overlay network topologies at each instance of such growth. Our algorithms allow the user to define the desired scale-free exponent ( \gamma). They also induce low communication overhead when network grows from one size to another. Using extensive simulations, we demonstrate that these algorithms indeed generate perfect scale free networks (at each step of network growth) that provide better search efficiency in various search algorithms than the networks generated by the existing solutions. [ABSTRACT FROM PUBLISHER]

In peer-to-peer networks (P2Ps), many autonomous peers without preexisting trust relationships share resources with each other. Due to their open environment, the P2Ps usually employ reputation systems to provide guidance in selecting trustworthy resource providers for high reliability and security; however, node collusion impairs the effectiveness of reputation systems in trustworthy node selection. Although some reputation systems have certain mechanisms to counter collusion, the effectiveness of the mechanisms is not sufficiently high. In this paper, we leverage social networks to enhance the capability of reputation systems in combating collusion. We first analyzed real trace of the reputation system in the Overstock online auction platform, which incorporates a social network. The analysis reveals the impact of the social network on user purchasing and reputation rating patterns. We, thus, identified suspicious collusion behavior patterns and propose a social network-based mechanism, SocialTrust, to counter collusion. SocialTrust adaptively adjusts the weight of ratings based on the social distance and interest relationship between peers. Experiment results show that SocialTrust can significantly strengthen the capability of current reputation systems in combating collusion. [ABSTRACT FROM AUTHOR]

We are motivated by the problem of designing a simple distributed algorithm for peer-to-peer streaming applications that can achieve high throughput and low delay, while allowing the neighbor set maintained by each peer to be small. While previous works have mostly used tree structures, our algorithm constructs multiple random directed Hamiltonian cycles and disseminates content over the superposed graph of the cycles. We show that it is possible to achieve the maximum streaming capacity even when each peer only transmits to and receives from \Theta (1) neighbors. Further, we show that the proposed algorithm achieves the streaming delay of \Theta (\log N) when the streaming rate is less than (1-1/K) of the maximum capacity for any fixed constant K\geq 2, where N denotes the number of peers in the network. The key theoretical contribution is to characterize the distance between peers in a graph formed by the superposition of directed random Hamiltonian cycles, in which edges from one of the cycles may be dropped at random. We use Doob martingales and graph expansion ideas to characterize this distance as a function of N, with high probability. [ABSTRACT FROM AUTHOR]

In this paper, we conduct an in-depth study on the feasibility of using network coding to ameliorate the content availability of BitTorrent swarms. We first perform mathematical analysis on the potential improvement in the content availability and bandwidth utilization induced by two existing network coding schemes. It is found that these two coding schemes either incur a very high coding complexity and disk operation overhead or cannot effectively leverage the potential of improving the content availability. In this regard, we propose a simple sparse network coding scheme in which both the drawbacks mentioned before are precluded. To accommodate the proposed coding scheme into BitTorrent, a new block scheduling algorithm is also developed based on the original rarest-first block scheduling policy of BitTorrent. Through extensive simulations and performance evaluations, we show that the proposed coding scheme is very effective in terms of improving the content availability of BitTorrent swarms when compared with some existing methods. [ABSTRACT FROM PUBLISHER]

This paper proposes a unique discovery signal as an enabler of peer-to-peer (P2P) communication which overlays a cellular network and shares its resources. Applying P2P communication to cellular network has two key issues: 1. Conventional ad hoc P2P connections may be unstable since stringent resource and interference coordination is usually difficult to achieve for ad hoc P2P communications; 2. The large overhead required by P2P communication may offset its gain. We solve these two issues by using a special discovery signal to aid cellular network-supervised resource sharing and interference management between cellular and P2P connections. The discovery signal, which facilitates efficient neighbor discovery in a cellular system, consists of un-modulated tones transmitted on a sequence of OFDM symbols. This discovery signal not only possesses the properties of high power efficiency, high interference tolerance, and freedom from near-far effects, but also has minimal overhead. A practical discovery-signal-based P2P in an OFDMA cellular system is also proposed. Numerical results are presented which show the potential of improving local service and edge device performance in a cellular network. [ABSTRACT FROM PUBLISHER]

The influence of node mobility on the convergence time of averaging gossip algorithms in networks is studied. It is shown that a small number of fully mobile nodes can yield a significant decrease in convergence time. A method is developed for deriving lower bounds on the convergence time by merging nodes according to their mobility pattern. This method is used to show that if the agents have 1-D mobility in the same direction, the convergence time is improved by at most a constant. Upper bounds on the convergence time are obtained using techniques from the theory of Markov chains and show that simple models of mobility can dramatically accelerate gossip as long as the mobility paths overlap significantly. Simulations verify that different mobility patterns can have significantly different effects on the convergence of distributed algorithms. [ABSTRACT FROM AUTHOR]

In mesh-based peer-to-peer (P2P) streaming systems, each video sequence is divided into segments, which are then streamed from multiple senders to a receiver. The receiver needs to coordinate the senders by specifying a transmission schedule for each of them. We consider the problem of scheduling segment transmission in P2P streaming systems, where different segments have different weights in terms of quality improvements to the received video. Our goal is to compute the transmission schedule for each receiver in order to maximize the perceived video quality. We first show that this scheduling problem is NP-Complete. We then present an integer linear programming (ILP) formulation for it, so that it can be solved with any ILP solver. This optimal solution, however, is computationally expensive and is not suitable for real-time P2P streaming systems. Thus, we propose two approximation algorithms to solve this segment scheduling problem. These algorithms provide theoretical guarantees on the worst-case performance. The first algorithm considers the weight of each video segment. The second algorithm is simpler and it assumes that segments carry equal weights. We analyze the performance and complexity of the two algorithms. In addition, we rigorously evaluate the proposed algorithms with simulations and experiments using a prototype implementation. Our simulation and experimental results show that the proposed algorithms outperform other algorithms that are commonly used in deployed P2P streaming systems and that have been recently proposed in the literature. [ABSTRACT FROM PUBLISHER]

Peer-to-peer (P2P) networks are vulnerable to peers who cheat, propagate malicious code, leech on the network, or simply do not cooperate. The traditional security techniques developed for the centralized distributed systems like client-server networks are insufficient for P2P networks by the virtue of their centralized nature. The absence of a central authority in a P2P network poses unique challenges for reputation management in the network. These challenges include identity management of the peers, secure reputation data management, Sybil attacks, and above all, availability of reputation data. In this paper, we present a cryptographic protocol for ensuring secure and timely availability of the reputation data of a peer to other peers at extremely low costs. The past behavior of the peer is encapsulated in its digital reputation, and is subsequently used to predict its future actions. As a result, a peer's reputation motivates it to cooperate and desist from malicious activities. The cryptographic protocol is coupled with self-certification and cryptographic mechanisms for identity management and countering Sybil attack. We illustrate the security and the efficiency of the system analytically and by means of simulations in a completely decentralized Gnutella-like P2P network. [ABSTRACT FROM AUTHOR]

This paper presents a queuing model to evaluate the latency associated with file transfers or replications in peer-to-peer (P2P) computer systems. The main contribution of this paper is a modeling framework for the peers that accounts for the file size distribution, the search time, load distribution at peers, and number of concurrent downloads allowed by a peer. We propose a queuing model that models the nodes or peers in such systems as M/G/1/K processor sharing queues. The model is extended to account for peers which alternate between online and offline states. The proposed queuing model for the peers is combined with a single class open queuing network for the routers interconnecting the peers to obtain the overall file transfer latency. We also show that in scenarios with multipart downloads from different peers, a rate proportional allocation strategy minimizes the download times. [ABSTRACT FROM AUTHOR]

Collusive piracy is the main source of intellectual property violations within the boundary of a P2P network. Paid clients (colluders) may illegally share copyrighted content files with unpaid clients (pirates). Such online piracy has hindered the use of open P2P networks for commercial content delivery. We propose a proactive content poisoning scheme to stop colluders and pirates from alleged copyright infringements in P2P file sharing. The basic idea is to detect pirates timely with identity-based signatures and time-stamped tokens. The scheme stops collusive piracy without hurting legitimate P2P clients by targeting poisoning on detected violators, exclusively. We developed a new peer authorization protocol (PAP) to distinguish pirates from legitimate clients. Detected pirates will receive poisoned chunks in their repeated attempts, Pirates are thus severely penalized with no chance to download successfully in tolerable time. Based on simulation results we find 99.9 percent prevention rate in Gnutella KaZaA and Freenet. We achieved 85-98 percent prevention rate on eMule, eDonkey, Morpheus, etc. The scheme is shown less effective in protecting some poison-resilient networks like BitTorrent and Azureus. Our work opens up the low-cost P2P technology for copyrighted content delivery. The advantage lies mainly in minimum delivery cost, higher content availability, and copyright compliance in exploring P2P network resources. [ABSTRACT FROM AUTHOR]

Previous analytical studies of unstructured P2P resilience have assumed exponential user lifetimes and only considered age-independent neighbor replacement. In this paper, we overcome these limitations by introducing a general node-isolation model for heavy-tailed user lifetimes and arbitrary neighbor-selection algorithms. Using this model, we analyze two age-biased neighbor-selection strategies and show that they significantly improve the residual lifetimes of chosen users, which dramatically reduces the probability of user isolation and graph partitioning compared with uniform selection of neighbors. In fact, the second strategy based on random walks on age-proportional graphs demonstrates that, for lifetimes with infinite variance, the system monotonically increases its resilience as its age and size grow. Specifically, we show that the probability of isolation converges to zero as these two metrics tend to infinity. We finish the paper with simulations in finite-size graphs that demonstrate the effect of this result in practice. [ABSTRACT FROM AUTHOR]

Credential chains are needed in trusted peer-to-peer(P2P) applications, where trust delegation must be established between each pair of peers at specific role level. Role-based trust is refined from the coarse-grained trust model used in most P2P reputation systems. This paper offers a novel heuristic-weighting approach to selecting the most likely path to construct a role-based trust chain. We apply history-sensitive heuristics to measure the path complexity and to assess the chaining efficiency. Our method discovers successive edges of a trust chain, adaptively, to match with the demands in any given P2P application. New heuristic chaining algorithms are developed for backward, forward, and bidirectional discovery of trust chains. Our heuristic chain discovery scheme shortens the search time, reduces the memory requirement, and enhances the chaining accuracy in scalable P2P networks. Consider a trust graph over N credentials and M distinct role nodes. Our heuristic trust-chain discovery algorithms require O(N²logN) search time and O(M) memory space, if the secondary heuristics are generated offline in advance. These are improved from O(N³) search time and O(NM) space required in nonheuristic discovery algorithms developed by Li et al. [12]. Our analytical results are verified by extensive simulation experiments over typical classes of role-based trust graphs. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) networks have been emerging as one of the most promising approaches to improve the scalability of video-on-demand (VoD) service over Internet. Although a number of architectures and streaming protocols have been proposed in past years, there is few work to study the practical performance of P2P-based VoD service especially considering the real user behavior which actually has significant impact on the system scalability. Therefore, in this paper, we first characterize the user behavior by analyzing a large amount of real traces from a popular VoD system supported by the biggest television station in China, cctv.com. Then, we examine the practical scalability of P2P-based VoD service through extensive trace-driven simulation under a general system framework. The results show that P2P networks scale well in providing VoD service under real user behavior by saving a considerable percentage of server bandwidth. Moreover, it is observed that adopting hard cache at client side achieves much better system scalability than that with soft cache. We also identify the impact of various aspects of user behavior upon system scalability through detailed simulation. We believe that our study will shine insightful light on the understanding of practical scalability of P2P-based VoD service and be helpful to future system design and optimization. [ABSTRACT FROM AUTHOR]

Server selection plays an essential role in content replication networks, such as peer-to-peer (P2P) and content delivery networks (CDNs). In this paper, we perform an analytical investigation of the strengths and weaknesses of existing server selection policies, based initially on an M/G/1 Processor Sharing (PS) queueing-theoretic model. We develop a theoretical benchmark to evaluate the performance of two general server selection policies, referred to as EQ_DELAY and EQ_LOAD, which characterize a wide range of existing server selection algorithms. We find that EQ_LOAD achieves an average delay always higher than or equal to that of EQ_DELAY. A key theoretical result of this paper is that in an N-server system, the worst case ratio between the average delay of EQ_DELAY or EQ_LOAD and the minimal average delay (obtained from the benchmark) is precisely N. We constructively show how this worst case scenario can arise in highly heterogeneous systems. This result, when interpreted in the context of selfish routing, means that the price of anarchy in unbounded delay networks depends on the topology, and can potentially be very large. Our analytical findings are extended in asymptotic regimes to the G/G/1 First-Come First-Serve and multi-class M/G/1-PS models and supported by simulations run for various arrival and service processes, scheduling disciplines, and workload exhibiting temporal locality. These results indicate that our analysis is applicable to realistic scenarios. [ABSTRACT FROM AUTHOR]

As more and more cars are equipped with Global Positioning System (GPS) and Wi-Fi transmitters, it becomes easier to design systems that will allow cars to autonomously interact with each other, e.g., regarding traffic on the roads. Indeed, car manufacturers are already equipping their cars with such devices. Although, currently, these systems are a proprietary, we envision a natural evolution where agent applications will be developed for vehicular systems, e.g., to improve car routing in dense urban areas. Nonetheless, this new technology and agent applications may lead to the emergence of self-interested car owners, who will care more about their own welfare than the social welfare of their peers. These car owners will try to manipulate their agents such that they transmit false data to their peers. Using a simulation environment, which models a real transportation network in a large city, we demonstrate the benefits that are achieved by self- interested agents if no countermeasures are implemented. We then proceed to describe the mechanisms for minimizing the effect of the malicious agents on other agents in the network. [ABSTRACT FROM AUTHOR]

We compare two possible mechanisms to manage a peer-to-peer storage system, where participants can store data online on the disks of peers in order to increase data availability and accessibility. Due to the lack of incentives for peers to contribute to the service, we suggest that either each peer's use of the service be limited to her contribution level (symmetric schemes), or that storage Space be bought from and sold to peers by a system operator that seeks to maximize profit. Using a noncooperative game model to take into account user selfishness, we study those mechanisms with respect to the social welfare performance measure, and give necessary and sufficient conditions for one scheme to socially outperform the other. [ABSTRACT FROM AUTHOR]

In this paper, we present an analytical study of dynamic membership (aka churn) in structured peer-to-peer networks. We use a fluid model approach to describe steady-state or transient phenomena and apply it to the Chord system. For any rate of churn and stabilization rates and any system size, we accurately account for the functional form of the probability of network disconnection as well as the fraction of failed or incorrect successor and finger pointers. We show how we can use these quantities to predict both the performance and consistency of lookups under churn. All theoretical predictions match simulation results. The analysis includes both features that are generic to structured overlays deploying a ring as well as Chord-specific details and opens the door to a systematic comparative analysis of, at least, ring-based structured overlay systems under churn. [ABSTRACT FROM AUTHOR]

Network structure construction and global state maintenance are expensive in large-scale dynamic peer-to-peer (P2P) networks. With inherent topology independence and low state maintenance overhead, random walk is an excellent tool in such network environments. However, the current uses are limited to unguided or heuristic random walks with no guarantee on their converged node visitation probability distribution. Such a convergence guarantee is essential for strong analytical properties and high performance of many P2P applications. In this paper, we investigate an approach for random walks to converge to application- desired node visitation probability distributions while only requiring information about the direct neighbors of each peer. Our approach is guided by the Metropolis-Hastings algorithm for Monte Carlo Markov Chain sampling. Our contributions are threefold. First, we analyze the convergence time of the random walk node visitation probability distribution on common P2P network topologies. Second, we analyze the fault tolerance of our random walks in dynamic networks with potential walker losses. Third, we present the effectiveness of random walks in assisting three realistic network applications: random membership subset management, search, and load balancing. Both search and load balancing desire random walks with biased node visitation distributions to achieve application- specific goals. Our analysis, simulations, and Internet experiment demonstrate the advantage of our random walks compared with alternative topology-independent index-free approaches. [ABSTRACT FROM AUTHOR]

Media distribution through application-layer overlay networks has received considerable attention recently, owing to its flexibility and readily deployable nature. On-demand streaming with asynchronous requests and, in general, with VCR-like interactions nevertheless remains a challenging task in overlay networks. In this paper, we introduce the Dynamic Skip List (DSL), a novel randomized and distributed structure that inherently accommodates dynamic and asynchronous clients. We establish the theoretical foundations of the DSL and demonstrate a practical DSL-based streaming overlay. In this overlay, the costs for typical operations, including join, leave, fast-forward, rewind, and random seek, are all sublinear to the client population. The model also seamlessly integrates a smart data scheduling algorithm using linear network coding, yielding fast and robust downloading from multiple suppliers. Our simulation results show that the DSL-based overlay is highly scalable. It delivers reasonably smooth playback with diverse client interactivities while keeping the computation and bandwidth overheads low. [ABSTRACT FROM AUTHOR]

We propose Push-to-Peer, a peer-to-peer system to cooperatively stream video. The main departure from previous work is that content is proactively pushed to peers, and persistently stored before the actual peer-to-peer transfers. The initial content placement increases content availability and improves the use of peer uplink bandwidth. Our specific contributions are: (i) content placement and associated pull policies that allow the optimal use of uplink bandwidth; (ii) performance analysis of such policies in controlled environments such as DSL networks under ISP control; (iii) a distributed load balancing strategy for selection of serving peers. [ABSTRACT FROM AUTHOR]

In information theory, it has been shown that network coding can effectively improve the throughput of multicast communication sessions in directed acyclic graphs. More practically, random network coding is also instrumental towards improving the downloading performance in BitTorrent-like peer-to-peer content distribution sessions. Live peer-to-peer streaming, however, poses unique challenges to the use of network coding, due to its strict timing and bandwidth constraints. In this paper, we revisit the complete spectrum in the design space of live peer-to-peer streaming protocols, with a sole objective of taking full advantage of random network coding. We present R², our new streaming algorithm designed from scratch to incorporate random network coding with a randomized push algorithm. R² is designed to improve the performance of live streaming in terms of initial buffering delays, resilience to peer dynamics, as well as reduced bandwidth costs on dedicated streaming servers, all of which are beyond the basic requirement of stable streaming playback. On an experimental testbed consisting of dozens of dual-CPU cluster servers, we thoroughly evaluate R² with an actual implementation, real network traffic, and emulated peer upload capacities, in comparisons with a typical live streaming protocol (both without and with network coding), representing the current state-of-the-art in real-world streaming applications. [ABSTRACT FROM AUTHOR]

In recent literature, network coding has emerged as a promising information theoretic approach to improve the performance of both peer-to-peer (P2P) and wireless networks. It has been widely accepted and acknowledged that network coding can theoretically improve network throughput of multicast sessions in directed acyclic graphs, achieving their cut-set capacity bounds. Recent studies have also supported the claim that network coding is beneficial for large-scale P2P content distribution, as it solves the problem of locating the last missing blocks to complete the download. We seek to perform a reality check of using network coding for P2P live multimedia streaming. We start with the following critical question: How helpful is network coding in P2P streaming? To address this question, we first implement the decoding process using Gauss-Jordan elimination, such that it can be performed while coded blocks are progressively received. We then implement a realistic testbed, called Lava, with actual network traffic to meticulously evaluate the benefits and tradeoffs involved in using network coding in P2P streaming. We present the architectural design challenges in implementing network coding for the purpose of streaming, along with a pull-based P2P live streaming protocol in our comparison studies. Our experimental results show that network coding makes it possible to perform streaming with a finer granularity, which reduces the redundancy of bandwidth usage, improves resilience to network dynamics, and is most instrumental when the bandwidth supply barely meets the streaming demand. [ABSTRACT FROM PUBLISHER]