Speculative Execution for Information Agents

Greg Barish, Craig A. Knoblock, and Steven Minton, University of Southern California

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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