Phenomenal improvements in the computational performance of multiprocessors have not been matched by comparable gains in I/O system performance. This imbalance has resulted in I/O becoming a signi cant bottleneck for many scienti c applications. One key to overcoming this bottleneck is improving the performance of parallel le systems. The design of a high-performance parallel le system requires a comprehensive understanding of the expected workload. Unfortunately, u n til recently, no general workload studies of parallel le systems have been conducted. The goal of the CHARISMA project was to remedy this problem by c haracterizing the behavior of several production workloads, on di erent machines, at the level of individual reads and writes. The rst set of results from the CHARISMA project describe the workloads observed on an Intel iPSC/860 and a Thinking Machines CM-5. This paper is intended to compare and contrast these two w orkloads for an understanding of their essential similarities and di erences, isolating common trends and platform-dependent v ariances. Using this comparison, we are able to gain more insight i n to the general principles that should guide parallel le-system design.

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The emergence of pervasive networked data sources, such as web services, sensors, and mobile devices, enables context-sensitive, mobile applications. We have developed a programming model for writing such applications, in which entities called composers accept data from one or more sources, and act as sources of higher-level data. We have defined and implemented a nonprocedural language, iQL, specifying the behavior of composers. An iQL programmer expresses requirements for data sources rather than identifying specific sources; a runtime system discovers appropriate data sources, binds to them, and rebinds when properties of data sources change. The language has powerful operators useful in composition, including operators to generate, filter, and abstract streams of values.

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