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BitTorrent (BT) in the last years has been one of the most effective mechanisms for P2P content distribution. Although BT was created for distribution of time insensitive content, in this work we try to identify what are the minimal changes needed in the BT's mechanisms in order to support streaming. The importance of this capability is that the peer will now have the ability to start enjoying the video before the complete download of the video file. This ability is particularly important in highly polluted environments, since the peer can evaluate the quality of the video content early and thus preserve its valuable resources.In a nutshell, our approach gives higher download priority to pieces that are close to be reproduced by the player. This comes in contrast to the original BT protocol, where pieces are downloaded in an out-of-order manner based solely on their rareness. In particular, our approach tries to strike the balance between downloading pieces in: (a) playing order, enabling smooth playback, and (b) the rarest first order, enabling the use of parallel downloading of pieces. In this work, we introduce three different Piece Selection mechanisms and we evaluate them through simulations based on how well they deliver streaming services to the peers.

Monitoring network traffic and detecting unwanted applications has become a challenging problem, since many applications obfuscate their traffic using unregistered port numbers or payload encryption. Apart from some notable exceptions, most traffic monitoring tools use two types of approaches: (a) keeping traffic statistics such as packet sizes and interarrivals, flow counts, byte volumes, etc., or (b) analyzing packet content. In this paper, we propose the use of Traffic Dispersion Graphs (TDGs) as a way to monitor, analyze, and visualize network traffic. TDGs model the social behavior of hosts ("who talks to whom"), where the edges can be defined to represent different interactions (e.g. the exchange of a certain number or type of packets). With the introduction of TDGs, we are able to harness a wealth of tools and graph modeling techniques from a diverse set of disciplines.

We exploit recent advances in analysis of graph topology to better understand software evolution, and to construct predictors that facilitate software development and maintenance. Managing an evolving, collaborative software system is a complex and expensive process, which still cannot ensure software reliability. Emerging techniques in graph mining have revolutionized the modeling of many complex systems and processes. We show how we can use a graph-based characterization of a software system to capture its evolution and facilitate development, by helping us estimate bug severity, prioritize refactoring efforts, and predict defect-prone releases. Our work consists of three main thrusts. First, we construct graphs that capture software structure at two different levels: (a) the product, i.e., source code and module level, and (b) the process, i.e., developer collaboration level. We identify a set of graph metrics that capture interesting properties of these graphs. Second, we study the evolution of eleven open source programs, including Firefox, Eclipse, MySQL, over the lifespan of the programs, typically a decade or more. Third, we show how our graph metrics can be used to construct predictors for bug severity, high-maintenance software parts, and failureprone releases. Our work strongly suggests that using graph topology analysis concepts can open many actionable avenues in software engineering research and practice.

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