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An Autonomous Agent Approach to Query Optimization in Stream Grids

An Autonomous Agent Approach to Query Optimization in Stream Grids

Saikat Mukherjee, Srinath Srinivasa, Krithi Ramamritham
Copyright: © 2010 |Volume: 1 |Issue: 4 |Pages: 22
ISSN: 1947-9344|EISSN: 1947-9352|EISBN13: 9781613502976|DOI: 10.4018/joci.2010100102
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MLA

Mukherjee, Saikat, et al. "An Autonomous Agent Approach to Query Optimization in Stream Grids." IJOCI vol.1, no.4 2010: pp.18-39. http://doi.org/10.4018/joci.2010100102

APA

Mukherjee, S., Srinivasa, S., & Ramamritham, K. (2010). An Autonomous Agent Approach to Query Optimization in Stream Grids. International Journal of Organizational and Collective Intelligence (IJOCI), 1(4), 18-39. http://doi.org/10.4018/joci.2010100102

Chicago

Mukherjee, Saikat, Srinath Srinivasa, and Krithi Ramamritham. "An Autonomous Agent Approach to Query Optimization in Stream Grids," International Journal of Organizational and Collective Intelligence (IJOCI) 1, no.4: 18-39. http://doi.org/10.4018/joci.2010100102

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Abstract

Stream grids are wide-area grid computing environments that are fed by a set of stream data sources, and Queries arrive at the grid from users and applications external to the system. The kind of queries considered in this work is long-running continuous (LRC) queries, which are neither short-lived nor infinitely long lived. The queries are “open” from the grid perspective as the grid cannot control or predict the arrival of a query with time, location, required data and query revocations. Query optimization in such an environment has two major challenges, i.e., optimizing in a multi-query environment and continuous optimization, due to new query arrivals and revocations. As generating a globally optimal query plan is an intractable problem, this work explores the idea of emergent optimization where globally optimal query plans emerge as a result of local autonomous decisions taken by the grid nodes. Drawing concepts from evolutionary game theory, grid nodes are modeled as autonomous agents that seek to maximize a self-interest function using one of a set of different strategies. Grid nodes change strategies in response to variations in query arrival and revocation patterns, which is also autonomously decided by each grid node.

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