The underutilization of disk parallelism and file cache buffers by traditional file systems induces I/O stall time that degrades the performance of modern microprocessor-based systems. In this paper, we present aggressive mechanisms that tailor file system resource management to the needs of I/O-intensive applications. In particular, we show how to use application-disclosed access patterns (hints) to expose and exploit I/O parallelism and to allocate dynamically file buffers among three competing demands: prefetching hinted blocks, caching hinted blocks for reuse, and caching recently used data for unhinted accesses. Our approach estimates the impact of alternative buffer allocations on application execution time and applies a cost-benefit analysis to allocate buffers where they will have the greatest impact. We implemented informed prefetching and caching in DEC’s OSF/1 operating system and measured its performance on a 150 MHz Alpha equipped with 15 disks running a range of applications including text search, 3D scientific visualization, relational database queries, speech recognition, and computational chemistry. Informed prefetching reduces the execution time of the first four of these applications by 20 % to 87%. Informed caching reduces the execution time of the fifth application by up to 30%.

Prefetching and caching are techniques commonly used in I/O systems to reduce latency. Many researchers have advocated the use of caching and prefetching to reduce latency in the Web. We derive several bounds on the performance improvements seen from these techniques, and then use traces of Web proxy activity taken at Digital Equipment Corporation to quantify these bounds. We found that for these traces, local proxy caching could reduce latency by at best 26%, prefetching could reduce latency by at best 57%, and a combined caching and prefetching proxy could provide at best a 60% latency reduction. Furthermore, we found that how far in advance a prefetching algorithm was able to prefetch an object was a significant factor in its ability to reduce latency. We note that the latency reduction from caching is significantly limited by the rapid changes of objects in the Web. We conclude that for the workload studied caching offers moderate assistance in reducing latency. Prefetching can of...

Prefetching and caching are effective techniques for improving the performance of file systems, but they have not been studied in an integrated fashion. This paper proposes four properties that optimal integrated strategies for prefetching and caching must satisfy, and then presents and studies two such integrated strategies, called aggressive and conservative. We prove that the performance of the conservative approach is within a factor of two of optimal and that the performance of the aggressive strategy is a factor significantly less than twice that of the optimal case. We have evaluated these two approaches by trace-driven simulation with a collection of file access traces. Our results show that the two integrated prefetching and caching strategies are indeed close to optimal and that these strategies can reduce the running time of applications by up to 50%.

. We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic finite automata which we name Probabilistic Suffix Automata (PSA). Though hardness results are known for learning distributions generated by general probabilistic automata, we prove that the algorithm we present can efficiently learn distributions generated by PSAs. In particular, we show that for any target PSA, the KL-divergence between the distribution generated by the target and the distribution generated by the hypothesis the learning algorithm outputs, can be made small with high confidence in polynomial time and sample complexity. The learning algorithm is motivated by applications in human-machine interaction. Here we present two applications of the algorithm. In the first one we apply the algorithm in order to construct a model of the English language, and use this model to correct corrupted text. In the second ...

Current operating systems offer poor performance when a numeric application's working set does not fit in main memory. As a result, programmers who wish to solve "out-of-core" problems efficiently are typically faced with the onerous task of rewriting an application to use explicit I/O operations (e.g., read/write). In this paper, we propose and evaluate a fully-automatic technique which liberates the programmer from this task, provides high performance, and requires only minimal changes to current operating systems. In our scheme, the compiler provides the crucial information on future access patterns without burdening the programmer, the operating system supports non-binding prefetch and re- lease hints for managing I/O, and the operating sys- tem cooperates with a run-time layer to accelerate performance by adapting to dynamic behavior and minimizing prefetch overhead. This approach maintains the abstraction of unlimited virtual memory for the programmer, gives the compiler the flexibility to aggressively move prefetches back ahead of references, and gives the operating system the flexibility to arbitrate between the competing resource demands of multiple applications. We have implemented our scheme using the SUIF compiler and the Hurricane operating system. Our experimental results demonstrate that our fully-automatic scheme effectively hides the I/O latency in out-of- core versions of the entire NAS Parallel benchmark suite, thus resulting in speedups of roughly twofold for five of the eight applications, with one application speeding up by over threefold.

File prefetching is an effective technique for improving file access performance. In this paper, we present a file prefetching mechanism that is based on on-line analytic modeling of interesting system events and is transparent to higher levels. The mechanism, incorporated into a client's file cache manager, seeks to build semantic structures that capture the intrinsic correlations between file accesses. It then heuristically uses these structures to represent distinct file usage patterns and exploits them to prefetch files from a file server. We show results of a simulation study and of a working implementation. Measurements suggest that our method can predict future file accesses with an accuracy around 90%, that it can reduce cache miss rate by up to 47% and application latency by up to 40%. Our method imposes little overhead, even under antagonistic circumstances. 1 Introduction This paper reports the effectiveness of a predictive file prefetching technique that operates automat...

Traditional file system implementations do not allow applications to control file caching replacement decisions. We have implemented two-level replacement, a scheme that allows applications to control their own cache replacement, while letting the kernel control the allocation of cache space among processes. We designed an interface to let applications exert control on replacement via a set of directives to the kernel. This is effective and requires low overhead. We demonstrate that for applications that do not perform well under traditional caching policies, the combination of good application-chosen replacement strategies, and our kernel allocation policy LRU-SP, can reduce the number of block I/Os by up to 80%, and can reduce the elapsed time by up to 45%. We also show that LRU-SP is crucial to the performance improvement for multiple concurrent applications: LRUSP fairly distributes cache blocks and offers protection against foolish applications.

The complexity of the mobility tracking problem in a cellular environment has been characterized under an information-theoretic framework. Shannon’s entropy measure is iden-tified as a basis for comparing user mobility models. By building and maintaining a dictionary of individual user’s path updates (as opposed to the widely used location up-dates), the proposed adaptive on-line algorithm can learn subscribers’ profiles. This technique evolves out of the con-cepts of lossless compression. The compressibility of the variable-to-fixed length encoding of the acclaimed Lempel-Ziv family of algorithms reduces the update cost, whereas their built-in predictive power can be effectively used to re-duce paging cost.

Although file caching and prefetching are known techniques to improve the performance of file systems, little work has been done on intergrating caching and prefetching. Optimal prefetching is nontrivial because prefetching may require early cache block replacements. Moreover, the tradeoff between the latency-hiding benefits of prefetching and the increase in the number of fetches required must be considered. This paper presents the design and implementation of a file system that integrates application-controlled caching, prefetching and disk scheduling. We use a two-level cache management strategy. The kernel uses the LRU-SP policy [CFL94a] to allocate blocks to processes, and each process uses the controlledaggressive policy, an algorithm previously shown in a theoretical sense to be near-optimal, for managing its cache. Each process then improves its disk access latency by submitting its prefetches in batches and schedules the requests in each batch to optimize disk access performa...

Aggressive prefetching is an effective technique for reducing the execution times of disk-bound applications; that is, applications that manipulate data too large or too infrequently used to be found in file or disk caches. While automatic prefetching approaches based on static analysis or historical access patterns are effective for some workloads, they are not as effective as manually-driven (programmer-inserted) prefetching for applications with irregular or input-dependent access patterns. In this paper, we propose to exploit whatever processor cycles are left idle while an application is stalled on I/O by using these cycles to dynamically analyze the application and predict its future I/O accesses. Our approach is to speculatively pre-execute the application’s code in order to discover and issue hints for its future read accesses. Coupled with an