Of the many P2P file-sharing prototypes in existence, BitTorrent is one of the few that has managed to attract millions of users. BitTorrent relies on other (global) components for file search, employs a moderator system to ensure the integrity of file data, and uses a bartering technique for downloading in order to prevent users from freeriding. In this paper we present a measurement study of BitTorrent in which we focus on four issues, viz. availability, integrity, flashcrowd handling, and download performance. The purpose of this paper is to aid in the understanding of a real P2P system that apparently has the right mechanisms to attract a large user community, to provide measurement data that may be useful in modeling P2P systems, and to identify design issues in such systems.

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Streaming multimedia content in real-time over a wireless link is a challenging task because of the rapid fluctuations in link conditions that can occur due to movement, interference, and so on. The popular IEEE 802.11 standard includes low-level tuning parameters like the transmission rate. Standard device drivers for today's wireless products are based on gathering statistics, and consequently, adapt rather slowly to changes in conditions. To meet the strict latency requirements of streaming applications, we designed and implemented an advanced control algorithm that uses signal-strength (SNR) information to achieve fast responses. Since SNR readings are quite noisy we do not use that information to directly control the rate setting, but rather as a safeguard limiting the range of feasible settings to choose from. We report on real-time experiments involving two laptops equipped with IEEE 802.11a wireless interface cards. The results show that using SNR information greatly enhances responsiveness in comparison to statistics-based rate controllers.

Based on Intel's Many Integrated Core (MIC) architecture, Intel Xeon Phi is one of the few truly many-core CPUs -featuring around 60 fairly powerful cores, two levels of caches, and graphic memory, all interconnected by a very fast ring. Given its promised ease-of-use and high performance, we took Xeon Phi out for a test drive. In this paper, we present this experience at two different levels: (1) the microbenchmark level, where we stress "each nut and bolt" of Phi in the lab, and (2) the application level, where we study Phi's performance response in a real-life environment. At the microbenchmarking level, we show the high performance of five components of the architecture, focusing on their maximum achieved performance and the prerequisites to achieve it. Next, we choose a medical imaging application (Leukocyte Tracking) as a case study. We observed that it is rather easy to get functional code and start benchmarking, but the first performance numbers can be far from satisfying. Our experience indicates that a simple data structure and massive parallelism are critical for Xeon Phi to perform well. When compiler-driven parallelization and/or vectorization fails, programming Xeon Phi for performance can become very challenging.