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Agent-based modeling supporting the migration of registry systems to grid based architectures
1University of Utah, Salt Lake City, UT.
Summit on Translational Bioinformatics
|February 25, 2011
Summary
Migrating the National Cancer Institute
Area of Science:
- Bioinformatics
- Computational Biology
- Health Informatics
Background:
- The National Cancer Institute's (NCI) Surveillance, Epidemiology, and End Results (SEER) platform faces challenges due to aging infrastructure and rising operational costs.
- Essential cancer research resources like SEER require migration to modern, scalable platforms such as Grid-based systems.
Purpose of the Study:
- To model and predict the performance implications of migrating the NCI SEER system to a Grid-based platform.
- To evaluate the integration of SEER with the caBIG(™) project's caGRID for enhanced translational research opportunities.
Main Methods:
- Utilizing agent-based modeling (ABM) simulations to represent complex, distributed services on a Grid computing platform.
- Comparing the performance of current SEER configurations with Grid-native implementations under various scaling scenarios.
Main Results:
- Agent-based modeling simulations predict that Grid technology can significantly improve system response times as SEER systems scale.
- Grid-native SEER applications demonstrated nearly constant user response times with increasing numbers of distributed registry silos.
- Current SEER architecture showed a linear increase in response time as the number of silos increased.
Conclusions:
- Grid-based systems, specifically caGRID, offer a promising technological foundation for migrating and enhancing the NCI SEER platform.
- Agent-based modeling is an effective technique for simulating and predicting the performance of large-scale distributed systems in cancer research.
- The proposed Grid migration strategy has the potential to improve the efficiency and scalability of cancer surveillance and research data management.
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