Network coordinates provide a mechanism for selecting and placing servers efficiently in a large distributed system. This approach works well as long as the coordinates continue to accurately reflect network topology. We conducted a long-term study of a subset of a million-plus node coordinate system and found that it exhibited some of the problems for which network coordinates are frequently criticized, for example, inaccuracy and fragility in the presence of violations of the triangle inequality. Fortunately, we show that several simple techniques remedy many of these problems. Using the Azureus BitTorrent network as our testbed, we show that live, large-scale network coordinate systems behave differently than their tame PlanetLab and simulation-based counterparts. We find higher relative errors, more triangle inequality violations, and higher churn. We present and evaluate a number of techniques that, when applied to Azureus, efficiently produce accurate and stable network coordinates. 1

This article is an editorial note submitted to CCR. It has NOT been peer reviewed Authors take full responsibility for this article’s technical content. Comments can be posted through CCR Online In this paper we propose a radical solution to data hosting and delivery for the Internet of the future. The current data delivery architecture is “network centric”, with content stored in data centers connected directly to Internet backbones. This approach has multiple drawbacks among which complexity of deploying data centers, power consumption, and lack of scalability are the most critical ones. We propose a totally innovative and orthogonal approach to traditional data centers, through what we call “nano ” data centers, which are essentially boxes deployed at the edge of the network (e.g., in home gateways, set-top-boxes, etc.) that cooperate in a peer-to-peer manner. Unlike traditional peer-to-peer clients, however, our nano data centers operate under a common management authority, e.g., the ISP who installs and maintains the set-top-boxes, and can thus cooperate more effectively and achieve a higher aggregate performance. Nano data centers are, therefore, better suited for providing guaranteed quality to new emerging applications such as online gaming, interactive IPTV and VoD, and user generated content.

Abstract — In a typical overlay network for routing or content sharing, each node must select a fixed number of immediate overlay neighbors for routing traffic or content queries. A selfish node entering such a network would select neighbors so as to minimize the weighted sum of expected access costs to all its destinations. Previous work on selfish neighbor selection has built intuition with simple models where edges are undirected, access costs are modeled by hop-counts, and nodes have potentially unbounded degrees. However, in practice, important constraints not captured by these models lead to richer games with substantively and fundamentally different outcomes. Our work models neighbor selection as a game involving directed links, constraints on the number of allowed neighbors, and costs reflecting both network latency and node preference. We express a node’s “best response ” wiring strategy as a k-median problem on asymmetric distance, and use this formulation to obtain pure Nash equilibria. We experimentally examine the properties of such stable wirings on synthetic topologies, as well as on real topologies and maps constructed from PlanetLab and AS-level Internet measurements. Our results indicate that selfish nodes can reap substantial performance benefits when connecting to overlay networks constructed by naive nodes. On the other hand, in overlays that are dominated by selfish nodes, the resulting stable wirings are optimized to such great extent that even uninformed newcomers can extract near-optimal performance through naive wiring strategies. I.

Abstract—Due to peer instability and time-varying peer upload bandwidth availability in live peer-to-peer (P2P) streaming channels, it is preferable to provision adequate levels of stable upload capacities at dedicated streaming servers, in order to guarantee the streaming quality in all channels. Most commercial P2P streaming systems have resorted to the practice of overprovisioning upload capacities on streaming servers. In this paper, we have performed a detailed analysis on 400 GB and 7 months of run-time traces from UUSee, a commercial P2P streaming system, and observed that available capacities on streaming servers are not able to keep up with the increasing demand imposed by hundreds of channels. We propose a novel online server capacity provisioning algorithm that proactively adjusts the server capacities available to each of the concurrent channels, such that the supply of server bandwidth in each channel dynamically adapts to the forecasted demand, taking into account the number of peers, the streaming quality, and the priorities of channels. The algorithm is able to learn over time, and has full ISP awareness to maximally constrain P2P traffic within ISP boundaries. To evaluate the effectiveness of our solution, our experimental studies are based on an implementation of the algorithm with actual channels of P2P streaming traffic, with real-world traces replayed within a server cluster. I.

Grid applications often need to distribute large amounts of data efficiently from one cluster to multiple others (multicast). Existing methods usually arrange nodes in optimized tree structures, based on external network monitoring data. This dependence on monitoring data, however, severely impacts both ease of deployment and adaptivity to dynamically changing network conditions. In this paper, we present Multicast Optimizing Bandwidth (MOB), a high-throughput multicast approach, inspired by the BitTorrent protocol [4]. With MOB, data transfers are initiated by the receivers that try to steal data from peer clusters. Instead of using potentially outdated monitoring data, MOB automatically adapts to the currently achievable bandwidth ratios. Our experimental evaluation compares MOB to both the BitTorrent protocol and to our previous approach, Balanced Multicasting [11], the latter optimizing multicast trees based on external monitoring data. We show that MOB outperforms the BitTorrent protocol. MOB is competitive with Balanced Multicasting as long as the network bandwidth remains stable. With dynamically changing bandwith, MOB outperforms Balanced Multicasting by wide margins.

Motivated by emerging cooperative P2P applications we study new uplink allocation algorithms for substituting the ratebased choke/unchoke algorithm of BitTorrent which was developed for non-cooperative environments. Our goal is to shorten the download times by improving the uplink utilization of nodes. We develop a new family of uplink allocation algorithms which we call BitMax, to stress the fact that they allocate to each unchoked node the maximum rate it can sustain, instead of an 1/(k + 1) equal share as done in the existing BitTorrent. BitMax computes in each interval the number of nodes to be unchoked, and the corresponding allocations, and thus does not require any empirically preset parameters like k. We demonstrate experimentally that Bit-Max can reduce significantly the download times in a typical reference scenario involving mostly ADSL nodes. We also consider scenarios involving network bottlenecks caused by filtering of P2P traffic at ISP peering points and show that BitMax retains its gains also in these cases.

This paper studies the conditions under which peer-to-peer (P2P) technology may be beneficial in providing IPTV services over typical network architectures. It has two major contributions. First, we contrast two network models used to study the performance of such a system: a commonly used logical “Internet as a cloud ” model and a “physical ” model that reflects the characteristics of the underlying network. Specifically, we show that the cloud model overlooks important architectural aspects of the network and may drastically overstate the benefits of P2P technology by a factor of 3 or more. Second, we provide a cost-benefit analysis of P2P video content delivery focusing on the profit trade-offs for different pricing/incentive models rather than purely on capacity maximization. In particular, we find that under high volume of video demand, a P2P built-in incentive model performs better than any other model for both high-definition and standard-definition media, while the usage-based model generally generates more profits when the request rate is low. The flat-reward model generally falls in-between the usagebasedmodelandthebuilt-inmodelintermsofprofitability.

Abstract- IP-enabled set-top boxes are becoming key devices in home entertainment networks. In addition to providing TV signals, STBs have been providing pay-per view service for a long time. But this service suffers from bandwidth requirements at the source server and has scaling problems. We propose a new design for providing a peer-assisted VoD service where peers co-operate in delivering the content to other peers. This design uses a Bittorrent like protocol for peer-to-peer topology management and DHT for data retrieval with low startup time, provision for VCR operation and admission control to guarantee QoS for subscribers. It utilizes the large storage of STBs for better viewing experience with reduced jitter and the underlying network architecture to do a location aware content fetching and reduce the expensive cross AS traffic over the Internet. 1.

Abstract — The content distribution techniques have recently started embracing peer-to-peer system as an alternative to the client-server architecture, such as BitTorrent system. BitTorrent system offers a scale mechanism for distributing a large volume of data to a set of peers over the Internet, but it is not designed for minimizing the time taken for all peers to receive the file. As a result, the peers of BitTorrent system may suffer a long download time, specifically the narrow-band peers. In this paper, in order to reduce the download time of BitTorrent, we propose a weighty piece selection strategy instead of the local rarest first strategy in BitTorrent. The proposed strategy is based on the greedy concept that a peer assigns each missing piece a weight according to total number of neighbor’s downloaded pieces. The peer selects the missing piece with the highest priority for next download. This strategy can speed up the cooperation between heterogeneous peers while making the BitTorrent more efficient in terms of the average download time and the total elapsed time. The simulation results show that weighty piece selection strategy can improve more than 15 % average download time and reduce in average 60 % total elapsed time than the BitTorrent system. I.

Peer-to-peer file sharing applications have become enormously popular over the past few years, coming to represent a large fraction of wide-area Internet traffic. A side effect of this explosive growth has been an emerging tussle between users, who want fast downloads, and ISPs, whose flat-rate pricing business model is threatened by the extreme volume of P2P traffic. Because ISP costs scale with usage while their prices do not, many ISPs have attempted to throttle or shut down P2P systems. Recently, several researchers have proposed that this tussle is unnecessary, that small changes in client and/or protocol behavior can lead to a “win-win " solution of better performance for end-users with less wide-area traffic for ISPs. Using a very large scale trace measurement of BitTorrent usage, we find evidence that such a win-win outcome is unlikely for at least one very popular P2P protocol. 1.