Abstrucf-We propose a new scheme for content dktribution of large files that is based on network coding. With network coding, each node o f 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 block forwarding 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 plece. We demonstrate through simulations of scenarios of practical interest that the expected file download time improves by more than 20-30470 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 leave the system.

Peer-to-peer content distribution networks can suffer from malicious participants that corrupt content. Current systems verify blocks with traditional cryptographic signatures and hashes. However, these techniques do not apply well to more elegant schemes that use network coding techniques for efficient content distribution.Architectures that use network coding are prone to jamming attacks where the introduction of a few corrupted blocks can quickly result in a large number of bad blocks propagating through the system. Identifying such bogus blocks is difficult and requires the use of homomorphic hashing functions, which are computationally expensive. This paper presents a practical security scheme for network coding that reduces the cost of verifying blocks on-the-fly while efficiently preventing the propagation of malicious blocks. In our scheme, users not only cooperate to distribute the content, but (well-behaved) users also cooperate to protect themselves against malicious users by informing affected nodes when a malicious block is found. We analyze and study such cooperative security scheme and introduce elegant techniques to prevent DoS attacks. We show that the loss in the efficiency caused by the attackers is limited to the effort the attackers put to corrupt the communication, which is a natural lower bound in the damage of the system. We also show experimentally that checking as low as 1-5% of the received blocks is enough to guarantee low corruption rates.

No abstract

For half a century, television has been a dominant and pervasive mass media, driving many technological advances. Despite its widespread usage and importance to emerging applications, the ingrained TV viewing habits are not completely understood. This was primarily due to the difficulty of instrumenting monitoring devices at individual homes at a large scale. The recent boom of Internet TV (IPTV) has enabled us to monitor the user behavior and network usage of an entire network. Such analysis can provide a clearer picture of how people watch TV and how the underlying networks and systems can better adapt to future challenges. In this paper, we present the first analysis of IPTV workloads based on network traces from one of the world's largest IPTV systems. Our dataset captures the channel change activities of 250,000 households over a six month period. We characterize the properties of viewing sessions, channel popularity dynamics, geographical locality, and channel switching behaviors. We discuss implications of our findings on networks and systems, including the support needed for fast channel changes. Our data analysis of an operational IPTV system has important implications on not only existing and future IPTV systems, but also the design of the open Internet TV distribution systems such as Joost and BBC's iPlayer that distribute television on the wider Internet.

No abstract

The Raman Laser Spectrometer (RLS) on board the ESA/Roscosmos ExoMars 2020 mission will provide precise identification of the mineral phases and the possibility to detect organics on the Red Planet. The RLS will work on the powdered samples prepared inside the Pasteur analytical suite and collected on the surface and subsurface by a drill system. Raman spectroscopy is a well-known analytical technique based on the inelastic scattering by matter of incident monochromatic light (the Raman effect) that has many applications in laboratory and industry, yet to be used in space applications. Raman spectrometers will be included in two Mars rovers scheduled to be launched in 2020. The Raman instrument for ExoMars 2020 consists of three main units:(1) a transmission spectrograph coupled to a CCD detector; (2) an electronics box, including the excitation laser that controls the instrument functions; and (3) an optical head with an autofocus mechanism illuminating and collecting the scattered light from the spot under investigation. The optical head is connected to the excitation laser and the spectrometer by optical fibers. The instrument also has two targets positioned inside the rover analytical laboratory for onboard Raman spectral calibration. The aim of this article was to present a detailed description of the RLS instrument, including its operation on Mars. To verify RLS operation before launch and to prepare science scenarios for the mission, a simulator of the sample analysis chain has been developed by the team. The results obtained are also discussed. Finally, the potential of the Raman instrument for use in field conditions is addressed. By using a ruggedized prototype, also developed by our team, a wide range of terrestrial analog sites across the world have been studied. These investigations allowed preparing a large collection of real, in situ spectra of samples from different geological processes and periods of Earth evolution. On this basis, we are working to develop models for interpreting analog processes on Mars during the mission.

We consider the problem of broadcasting a live stream of data in an unstructured network. The broadcasting problem has been studied extensively for edge-capacitated networks. We give the first proof that whenever demand λ + ε is feasible for ε > 0, a simple local-control algorithm is stable under demand λ, and as a corollary a famous theorem of Edmonds. We then study the node-capacitated case and show a similar optimality result for the complete graph. We study through simulation the delay that users must wait in order to playback a video stream with a small number of skipped packets, and discuss the suitability of our algorithms for live video streaming. I. INTRODUCTIONWe consider the problem of broadcasting a live stream of data, such as a movie, to all nodes in an unstructured network. When edges have capacities, this problem has been well-studied since the 1970s -Edmonds, Lovasz and Gabow and others have given centralized schemes based on packing spanning trees [1]- [5].The broadcast problem is at the core of every content distribution system; in particular, current live streaming distribution systems such as CoolStreaming [6], PPLive [7], SplitStream [8]. These systems either construct the overlay topology in such a way to easy packet scheduling, and in doing so reduce the network efficiency by not optimally using all the available resources, or use heuristic algorithms for packet distribution with unknown performance properties.We present a completely distributed and suprisingly simple algorithm for broadcasting in arbitrary networks, which does not require coding yet provably achieves the optimal broadcast rate. This is the first known result of this kind. Our analysis is based on fluid models and Lyapunov functions applied to a novel powerset representation of the network. As a corollary we retrieve a famous theorem of Edmonds [1].In the second part of the paper, we introduce a new model for broadcasting a live stream of data in a peer-to-peer network based on the node-capacitated broadcast problem -each node has a specified upload capacity (we assume that download capacity is infinite), modeling the user's connection to the network, and the user must choose how this capacity is allocated among peers. This introduces an allocation problem in addition to the problem of scheduling packet transmissions. We present a completely distributed algorithm for the nodecapacitated broadcast problem, and show that it achieves the optimal rate in certain classes of network.

The difficulty of scaling Online Social Networks (OSNs) has introduced new system design challenges that has often caused costly re-architecting for services like Twitter and Facebook. The complexity of interconnection of users in social networks has introduced new scalability challenges. Conventional vertical scaling by resorting to full replication can be a costly proposition. Horizontal scaling by partitioning and distributing data among multiples servers -e.g. using DHTs -can lead to costly inter-server communication.We design, implement, and evaluate SPAR, a social partitioning and replication middle-ware that transparently leverages the social graph structure to achieve data locality while minimizing replication. SPAR guarantees that for all users in an OSN, their direct neighbor's data is co-located in the same server. The gains from this approach are multi-fold: application developers can assume local semantics, i.e., develop as they would for a single server; scalability is achieved by adding commodity servers with low memory and network I/O requirements; and redundancy is achieved at a fraction of the cost.We detail our system design and an evaluation based on datasets from Twitter, Orkut, and Facebook, with a working implementation. We show that SPAR incurs minimum overhead, and can help a well-known open-source Twitter clone reach Twitter's scale without changing a line of its application logic and achieves higher throughput than Cassandra, Facebook's DHT based key-value store database.