Steady improvements in storage capacities and CPU clock speeds intensify the performance bottleneck at the I/O subsystem of modern computers. Caching data can efficiently short circuit costly delays associated with disk accesses. Recent studies have shown that disk I/O performance gains provided by a cache buffer do not scale with cache size. Therefore, new algorithms have to be investigated to better utilize cache buffer space. Predictive prefetching and caching solutions have been shown to improve I/O performance in an efficient and scalable manner in simulation experiments. However, most predictive prefetching algorithms have not yet been implemented in real-world storage systems due to two main limitations: first, the existing prefetching solutions are unable to self regulate based on changing I/O workload; second, excessive number of unneeded blocks are prefetched. Combined, these drawbacks make predictive prefetching and caching a less attractive solution than the simple LRU management. To address these problems, in this paper we propose an automatic prefetching and caching system (or APACS for short), which mitigates all of these shortcomings through three unique techniques, namely: (1) dynamic cache partitioning, (2) prefetch pipelining, and (3) prefetch buffer management. APACS dynamically partitions the buffer cache memory, used for prefetched and cached blocks, by automatically changing buffer/cache sizes in accordance to global I/O performance. The adaptive partitioning scheme implemented in APACS optimizes cache hit ratios, which subsequently accelerates application execution speeds. Experimental results obtained from trace-driven simulations show that APACS outperforms the LRU cache management and existing prefetching algorithms by an average of over 50%.

In largely distributed clusters, computing nodes are geographically deployed in various computing sites. Information processed in a distributed cluster is shared among a group of distributed processes or users by virtue of messages passing protocols (e.g. message passing interface - MPI) running on the Internet. Because of the open accessible nature of the Internet, data encryption for these large-scale distributed clusters becomes a non-trivial and challenging problem. To address this issue, we enhanced the security of the MPI (Message Passing Interface) protocol by encrypting and decrypting messages sent and received among computing nodes. In this study we focused on MPI rather than other protocols because MPI is one of the most popular communication protocols for cluster computing environments. From among a variety of MPI implementations, we picked MPICH2 developed by the Argonne National Laboratory. The design goal of MPICH2 - a widely used MPI implementation - is to combine portability with high performance. We integrated encryption algorithms into the MPICH2 library so that data confidentiality of MPI applications could be readily preserved without a need to change the source codes of the MPI applications. since we provide a security enhanced MPI-library with the standard MPI interfact, data communications of a conventional MPI program can be secured without converting the program into the corresponding secure version. We used Sandia Micro Benchmark and Intel MPI Benchmarks to evaluate and compared the performance of original MPICH2 and Enhanced Security MPICH2. According to the performance evaluation, ES-MPICH2 provides secured Message Passing Interface by sacrificing reasonable system performance.

We describe a user study that examined the relationship between the quality of an Information Retrieval system and the effectiveness of its users in performing a task. The task involves finding answer facets of questions pertaining to a collection of newswire documents over a six month period. We artificially created sets of ranked lists at increasing levels of quality by blending the output of a state-of-the-art retrieval system with truth data created by annotators. Subjects performed the task by using these ranked lists to guide their labeling of answer passages in the retrieved articles. We found that as system accuracy improves, subject time on task and error rate decrease, and the rate of finding new correct answers increases. There is a large intermediary region in which the utility difference is not significant; our results suggest that there is some threshold of accuracy for this task beyond which user utility improves rapidly, but more experiments are needed to examine the area around that threshold closely.