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Greg Barish

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4 papers

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AIJ Journal 2008 Journal Article

Speculative plan execution for information gathering

Greg Barish
Craig A. Knoblock

The execution performance of an information gathering plan can suffer significantly due to remote I/O latencies. A streaming dataflow model of execution addresses the problem to some extent, exploiting all natural opportunities for parallel execution, as allowed by the data dependencies in a plan. Unfortunately, plans that integrate information from multiple sources often use the results of one operation as the basis for forming queries to a subsequent operation. Such cases require sequential execution, an inefficiency that can erase prior gains made through techniques like streaming dataflow. To address this problem, we present a technique called speculative plan execution, an out-of-order method that capitalizes on knowledge gained from prior executions as a means for overcoming remaining data dependencies between plan operators. Our approach inserts additional plan operators that generate and confirm speculative results, while preserving the safety and fairness of overall execution. To increase the utility of speculative execution, we propose a method of value prediction that combines caching with the more effective and space-efficient techniques of classification and transduction. We present experimental results that demonstrate how the performance of information gathering plans can benefit from speculative execution and how its overall utility can be increased through our hybrid method of value prediction.

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IJCAI Conference 2003 Conference Paper

Learning Value Predictors for the Speculative Execution of Information Gathering Plans

Greg Barish
Craig A. Knobbck

Speculative execution of information gathering plans can dramatically reduce the effect of source I/O latencies on overall performance. However, the utility of speculation is closely tied to how accurately data values are predicted at runtime. Caching is one approach that can be used to issue future predictions, but it scales poorly with large data sources and is unable to make intelligent predictions given previously unseen input data, even when there is an obvious relationship between past input and the output it generated. In this paper, we describe a novel way to combine classification and transduction for a more efficient and accurate value prediction strategy, one capable of issuing predictions about previously unseen hints. We show how our approach results in significant speedups for plans that query multiple sources or sources that require multi-page navigation.

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ICAPS Conference 2002 Conference Paper

Speculative Execution for Information Gathering Plans

Greg Barish
Craig A. Knoblock

Although information gathering plans have enabled data from remote heterogeneous sources to be easily combined and queried, their execution performance suffers because access to remote sources is often slow. To address this problem, we have developed a method of speculative execution that increases the degree of run-time parallelism during plan execution. Our approach allows any information gathering plan to be automatically modified to support speculation in a manner that can lead to significant speedups, while ensuring that both safety and fairness are preserved. We demonstrate how speculative execution can be applied to a typical Internet information gathering plan to provide significant performance benefits.

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AAAI Conference 2000 Short Paper

Speculative Execution for Information Agents

Greg Barish
and Steven Minton

Practical deployments of information agents can suffer from sub- optimal performance and scalability for a number of reasons. In the case of web-based information integration, for example, data sources are remote and their latency can have a substantial effect on overall execution performance. Scalability can also be poor, since concurrent queries can cause multiple, simultaneous remote data retrievals (often of the same information), quickly consuming available bandwidth. At the same time, web-based information agents are often I/O-bound and wasting millions of CPU cycles as execution proceeds. In this paper, we describe how speculative execution can be applied to improve performance and scalability in practical agent deployments. Our approach enables both control and data-predictive styles of speculation, as well as a flexible framework for generating spculative hints, and scalable infrastructure for incorporating speculation seamlessly into existing agent plans.

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