Efficient use of network resources has long been an important problem for large-scale network operators. To this end, several recent research efforts have proposed automated methods for optimizing routes based on traffic measurements. However, these efforts have not considered the stability of the dual feedback control mechanisms of adaptive routing and congestion control, when operating together. In this paper, we demonstrate that an important class of adaptive routing algorithms can yield stable optimal routes in the presence of congestion control, provided that either the congestion control mechanism is fair or the network workload behaves under reasonable constraints. We further show that one or the other of these assumptions is necessary for this class of adaptive routing algorithms -otherwise, unstable, sub-optimal routes may result in some pathological cases.

We examine the application of offline algorithms for determining the optical sequence of loads and superloads (a load of multiple consecutive cache lines) for direct-mapped caches. We evaluate potential gains in terms of miss rate and bandwidth and find that in many cases optimal superloading can noticeably reduce the miss rate without appreciably increasing bandwidth. Then we examine how this performance potential might be realized. We examine the effectiveness of a dynamic online algorithm and of static analysis (profiling) for superloading and compare these to next-line prefetching. Experimental results show improvements comparable to those of the optimal algorithm in terms of miss rates.

This paper presents cooperative prefetching and caching --- the use of network-wide global resources (memories, CPUs, and disks) to support prefetching and caching in the presence of hints of future demands. Cooperative prefetching and caching effectively unites disk-latency reduction techniques from three lines of research: prefetching algorithms, cluster-wide memory management, and parallel I/O. When used together, these techniques greatly increase the power of prefetching relative to a conventional (non-global-memory) system. We have designed and implemented PGMS, a cooperative prefetching and caching system, under the Digital Unix operating system running on a 1.28 Gb/sec Myrinet-connected cluster of DEC Alpha workstations. Our measurements and analysis show that by using available global resources, cooperative prefetching can obtain significant speedups for I/O-bound programs. For example, for a graphics rendering application, our system achieves a speedup of 4.9 over a non-prefetching version of the same program, and a 3.1-fold improvement over that program using local-disk prefetching alone.