Speculative Execution for Information Gathering Plans

Greg Barish and 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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