Opportunistic routing is a recent technique that achieves high throughput in the face of lossy wireless links. The current opportunistic routing protocol, ExOR, ties the MAC with routing, imposing a strict schedule on routers ’ access to the medium. Although the scheduler delivers opportunistic gains, it misses some of the inherent features of the 802.11 MAC. For example, it prevents spatial reuse and thus may underutilize the wireless medium. It also eliminates the layering abstraction, making the protocol less amenable to extensions to alternate traffic types such as multicast. This paper presents MORE, a MAC-independent opportunistic routing protocol. MORE randomly mixes packets before forwarding them. This randomness ensures that routers that hear the same transmission do not forward the same packets. Thus, MORE needs no special scheduler to coordinate routers and can run directly on top of 802.11. Experimental results from a 20-node wireless testbed show that MORE’s median unicast throughput is 22 % higher than ExOR, and the gains rise to 45 % over ExOR when there is a chance of spatial reuse. For multicast, MORE’s gains increase with the number of destinations, and are 35-200 % greater than ExOR.

Abstract — In recent years, BitTorrent has emerged as a very scalable peer-to-peer file distribution mechanism. While early measurement and analytical studies have verified BitTorrent’s performance, they have also raised questions about various metrics (upload utilization, fairness, etc.), particularly in settings other than those measured. In this paper, we present a simulationbased study of BitTorrent. Our goal is to deconstruct the system and evaluate the impact of its core mechanisms, both individually and in combination, on overall system performance under a variety of workloads. Our evaluation focuses on several important metrics, including peer link utilization, file download time, and fairness amongst peers in terms of volume of content served. Our results confirm that BitTorrent performs near-optimally in terms of uplink bandwidth utilization, and download time except under certain extreme conditions. We also show that low bandwidth peers can download more than they upload to the network when high bandwidth peers are present. We find that the rate-based tit-for-tat policy is not effective in preventing unfairness. We show how simple changes to the tracker and a stricter, block-based tit-for-tat policy, greatly improves fairness. I.

The performance of peer-to-peer file replication comes from its piece and peer selection strategies. Two such strategies have been introduced by the BitTorrent protocol: the rarest first and choke algorithms. Whereas it is commonly admitted that BitTorrent performs well, recent studies have proposed the replacement of the rarest first and choke algorithms in order to improve efficiency and fairness. In this paper, we use results from real experiments to advocate that the replacement of the rarest first and choke algorithms cannot be justified in the context of peer-to-peer file replication in the Internet. We instrumented a BitTorrent client and ran experiments on real torrents with different characteristics. Our experimental evaluation is peer oriented, instead of tracker oriented, which allows us to get detailed information on all exchanged messages and protocol events. We go beyond the mere observation of the good efficiency of both algorithms. We show that the rarest first algorithm guarantees close to ideal diversity of the pieces among peers. In particular, on our experiments, replacing the rarest first algorithm with source or network coding solutions cannot be justified. We also show that the choke algorithm in its latest version fosters reciprocation and is robust to free riders. In particular, the choke algorithm is fair and its replacement with a bit level tit-for-tat solution is not appropriate. Finally, we identify new areas of improvements for efficient peer-to-peer file replication protocols.

We present a capacity-approaching coding scheme for unicast or multicast over lossy packet networks. In the scheme, all nodes perform coding, but do not wait for a full block of packets to be received before sending out coded packets. Rather, whenever they have a transmission opportunity, they form coded packets with random linear combinations of previously received packets. All coding and decoding operations in the scheme have polynomial complexity. Our analysis of the scheme shows that it is not only capacity-approaching, but that the propagation of packets carrying “innovative ” information follows that of a queueing network where every node acts as a stable M/M/1 queue. We consider networks with both lossy point-to-point and broadcast links, allowing us to model both wireline and wireless packet networks. 1

Recently, peer-to-peer (P2P) networks have emerged as an attractive solution to enable large-scale content distribution without requiring major infrastructure investments. While such P2P solutions appear highly beneficial for content providers and end-users, there seems to be a growing concern among Internet Service Providers (ISPs) that now need to support the distribution cost. In this work, we explore the potential impact of future P2P file delivery mechanisms as seen from three different perspectives: i) the content provider, ii) the ISPs, and iii) individual content consumers. Using a diverse set of measurements including BitTorrent tracker logs and payload packet traces collected at the edge of a 20,000 user access network, we quantify the impact of peer-assisted file delivery on end-user experience and resource consumption. We further compare it with the performance expected from traditional distribution mechanisms based on large server farms and Content Distribution Networks (CDNs).

Traditionally, interference is considered harmful. Wireless networks strive to avoid scheduling multiple transmissions at the same time in order to prevent interference. This paper adopts the opposite approach; it encourages strategically picked senders to interfere. Instead of forwarding packets, routers forward the interfering signals. The destination leverages network-level information to cancel the interference and recover the signal destined to it. The result is analog network coding because it mixes signals not bits. So, what if wireless routers forward signals instead of packets? Theoretically, such an approach doubles the capacity of the canonical relay network. Surprisingly, it is also practical. We implement our design using software radios and show that it achieves significantly higher throughput than both traditional wireless routing and prior work on wireless network coding. 1.

Abstract—Network coding substantially increases network throughput. But since it involves mixing of information inside the network, a single corrupted packet generated by a malicious node can end up contaminating all the information reaching a destination, preventing decoding. This paper introduces distributed polynomial-time rate-optimal network codes that work in the presence of Byzantine nodes. We present algorithms that target adversaries with different attacking capabilities. When the adversary can eavesdrop on all links and jam zO links, our first algorithm achieves a rate of C 0 2zO, where C is the network capacity. In contrast, when the adversary has limited eavesdropping capabilities, we provide algorithms that achieve the higher rate of C 0 zO. Our algorithms attain the optimal rate given the strength of the adversary. They are information-theoretically secure. They operate in a distributed manner, assume no knowledge of the topology, and can be designed and implemented in polynomial time. Furthermore, only the source and destination need to be modified; nonmalicious nodes inside the network are oblivious to the presence of adversaries and implement a classical distributed network code. Finally, our algorithms work over wired and wireless networks. Index Terms—Byzantine adversaries, distributed network error-correcting codes, eavesdroppers, information-theoretically optimal, list decoding, polynomial-time algorithms. I.

Abstract — We consider the problem of storing a large file or multiple large files in a distributed manner over a network. In the framework we consider, there are multiple storage locations, each of which only have very limited storage space for each file. Each storage location chooses a part (or a coded version of the parts) of the file without the knowledge of what is stored in the other locations. We want a file-downloader to connect to as few storage locations as possible and retrieve the entire file. We compare the performance of three strategies: uncoded storage, traditional erasure coding based storage, random linear coding based storage motivated by network coding. We demonstrate that, in principle, a traditional erasure coding based storage (eg: Reed-Solomon Codes) strategy can almost do as well as one can ask for with appropriate choice of parameters. However, the cost is a large amount of additional storage space required at the centralized server before distribution among multiple locations. The random linear coding based strategy performs as well without suffering from any such disadvantage. Further, with a probability close to one, the minimum number of storage location a downloader needs to connect to (for reconstructing the entire file), can be very close to the case where there is complete coordination between the storage locations and the downloader. We also argue that an uncoded strategy performs poorly. I.

Distributed storage systems provide reliable access to data through redundancy spread over individually unreliable nodes. Application scenarios include data centers, peer-to-peer storage systems, and storage in wireless networks. Storing data using an erasure code, in fragments spread across nodes, requires less redundancy than simple replication for the same level of reliability. However, since fragments must be periodically replaced as nodes fail, a key question is how to generate encoded fragments in a distributed way while transferring as little data as possible across the network. For an erasure coded system, a common practice to repair from a node failure is for a new node to download subsets of data stored at a number of surviving nodes, reconstruct a lost coded block using the downloaded data, and store it at the new node. We show that this procedure is sub-optimal. We introduce the notion of regenerating codes, which allow a new node to download functions of the stored data from the surviving nodes. We show that regenerating codes can significantly reduce the repair bandwidth. Further, we show that there is a fundamental tradeoff between storage and repair bandwidth which we theoretically characterize using flow arguments on an appropriately constructed graph. By invoking constructive results in network coding, we introduce regenerating codes that can achieve any point in this optimal tradeoff. I.

Abstract — There have been tremendous efforts and many technical innovations in supporting real-time video streaming in the past two decades, but cost-effective large-scale video broadcast has remained an elusive goal. IP multicast represented the earlier attempt to tackle this problem, but failed largely due to concerns regarding scalability, deployment, and support for higher level functionality. Recently, peer-to-peer based broadcast has emerged as a promising technique, which has been shown to be cost effective and easy to deploy. This new paradigm brings a number of unique advantages such as scalability, resilience and also effectiveness in coping with dynamics and heterogeneity. While peer-to-peer applications such as file download and voice over IP have gained tremendous popularity, video broadcast is still in its early stages and its full potential remains to be seen. This article reviews the state-of-the-art of peer-to-peer Internet video broadcast technologies. We describe the basic taxonomy of peer-to-peer broadcast and summarize the major issues associated with the design of broadcast overlays. We closely examine two approaches, namely, tree-based and data-driven, and discuss their fundamental trade-off and potential for large-scale deployment. Finally, we outline the key challenges and open problems, and highlight possible avenues for future directions. I.