In tree-based multicast systems, a relatively small number of interior nodes carry the load of forwarding multicast messages. This works well when the interior nodes are highly available, d d cated infrastructure routers but it poses a problem for application-level multicast in peer-to-peer systems. SplitStreamadV esses this problem by striping the content across a forest of interior-nodno# sjoint multicast trees that d stributes the forward ng load among all participating peers. For example, it is possible to construct efficient SplitStream forests in which each peer contributes only as much forwarding bandH d th as it receives. Furthermore, with appropriate content encod ngs, SplitStream is highly robust to failures because a nod e fai ure causes the oss of a single stripe on average. We present thed#' gnand implementation of SplitStream and show experimental results obtained on an Internet testbed and via large-scale network simulation. The results show that SplitStreamd istributes the forward ing load among all peers and can accommod'9 peers with different band0 d capacities while imposing low overhead for forest constructionand maintenance.

In recent years, overlay networks have become an effective alternative to IP multicast for efficient point to multipoint communication across the Internet. Typically, nodes self-organize with the goal of forming an efficient overlay tree, one that meets performance targets without placing undue burden on the underlying network. In this paper, we target high-bandwidth data distribution from a single source to a large number of receivers. Applications include large-file transfers and real-time multimedia streaming. For these applications, we argue that an overlay mesh, rather than a tree, can deliver fundamentally higher bandwidth and reliability relative to typical tree structures. This paper presents Bullet, a scalable and distributed algorithm that enables nodes spread across the Internet to self-organize into a high bandwidth overlay mesh. We construct Bullet around the insight that data should be distributed in a disjoint manner to strategic points in the network. Individual Bullet receivers are then responsible for locating and retrieving the data from multiple points in parallel. Key contributions of this work include: i) an algorithm that sends data to di#erent points in the overlay such that any data object is equally likely to appear at any node, ii) a scalable and decentralized algorithm that allows nodes to locate and recover missing data items, and iii) a complete implementation and evaluation of Bullet running across the Internet and in a large-scale emulation environment reveals up to a factor two bandwidth improvements under a variety of circumstances. In addition, we find that, relative to tree-based solutions, Bullet reduces the need to perform expensive bandwidth probing.

We propose a new scheme for content distribution of large files that is based on network coding. With network coding, each node of the distribution network is able to generate and transmit encoded blocks of information. The randomization introduced by the coding process eases the scheduling of block propagation, and, thus, makes the distribution more efficient. This is particularly important in large unstructured overlay networks, where the nodes need to make decisions based on local information only. We compare network coding to other schemes that transmit unencoded information (i.e. blocks of the original file) and, also, to schemes in which only the source is allowed to generate and transmit encoded packets. We study the performance of network coding in heterogeneous networks with dynamic node arrival and departure patterns, clustered topologies, and when incentive mechanisms to discourage free-riding are in place. We demonstrate through simulations of scenarios of practical interest that the expected file download time improves by more than 20-30 % with network coding compared to coding at the server only and, by more than 2-3 times compared to sending unencoded information. Moreover, we show that network coding improves the robustness of the system and is able to smoothly handle extreme situations where the server and nodes departure the system.

We introduce LT codes, the first rateless erasure codes that are very efficient as the data length grows.

We present Trickle, an algorithm for propagating and maintaining code updates in wireless sensor networks. Borrowing techniques from the epidemic/gossip, scalable multicast, and wireless broadcast literature, Trickle uses a "polite gossip" policy, where motes periodically broadcast a code summary to local neighbors but stay quiet if they have recently heard a summary identical to theirs. When a mote hears an older summary than its own, it broadcasts an update. Instead of flooding a network with packets, the algorithm controls the send rate so each mote hears a small trickle of packets, just enough to stay up to date. We show that with this simple mechanism, Trickle can scale to thousand-fold changes in network density, propagate new code in the order of seconds, and impose a maintenance cost on the order of a few sends an hour.

In tree-based multicast systems, a relatively small number of interior nodes carry the load of forwarding multicast messages. This works well when the interior nodes are dedicated infrastructure routers. But it poses a problem in cooperative application-level multicast, where participants expect to contribute resources proportional to the benefit they derive from using the system. Moreover, many participants may not have the network capacity and availability required of an interior node in high-bandwidth multicast applications. SplitStream is a high-bandwidth content distribution system based on application-level multicast. It distributes the forwarding load among all the participants, and is able to accommodate participating nodes with different bandwidth capacities. We sketch the design of SplitStream and present some preliminary performance results.

We consider the problem of distributing "five" streaming media content to a potentially large and highly dynamic population of hosts. Peer-to-peer content distribution is attractive in this setting because the bandwidth available to serve content scales with demand. A key challenge, however, is making content distribution robust to peer transience. Our approach to providing robustness is to introduce redundancy, both in network paths and in data. We use multiple, diverse distribution trees to provide redundancy in network paths and multiple description coding (MDC) to provide redundancy in data. We present

Abstract—The proliferation of applications that must reliably distribute large, rich content to a vast number of autonomous receivers motivates the design of new multicast and broadcast protocols. We describe an ideal, fully scalable protocol for these applications that we call a digital fountain. A digital fountain allows any number of heterogeneous receivers to acquire content with optimal efficiency at times of their choosing. Moreover, no feedback channels are needed to ensure reliable delivery, even in the face of high loss rates. We develop a protocol that closely approximates a digital fountain using two new classes of erasure codes that for large block sizes are orders of magnitude faster than standard erasure codes. We provide performance measurements that demonstrate the feasibility of our approach and discuss the design, implementation, and performance of an experimental system. Index Terms—Content delivery, erasure codes, forward error correction, reliable multicast, scalability. I.

We present Slurpie: a peer-to-peer protocol for bulk data transfer. Slurpie is specifically designed to reduce client download times for large, popular files, and to reduce load on servers that serve these files. Slurpie employs a novel adaptive downloading strategy to increase client performance, and employs a randomized backoff strategy to precisely control load on the server. We describe a full implementation of the Slurpie protocol, and present results from both controlled localarea and wide-area testbeds. Our results show that Slurpie clients improve performance as the size of the network increases, and the server is completely insulated from large flash crowds entering the Slurpie network.

Distributed hash tables have recently become a useful building block for a variety of distributed applications. However, current schemes based upon consistent hashing require both considerable implementation complexity and substantial storage overhead to achieve desired load balancing goals. We argue in this paper that these goals can be achieved more simply and more cost-effectively. First, we suggest the direct application of the "power of two choices" paradigm, whereby an item is stored at the less loaded of two (or more) random alterna- tives. We then consider how associating a small constant number of hash values with a key can naturally be extended to support other load balancing methods, including load-stealing or load-shedding schemes, as well as providing natural fault-tolerance mechanisms.