![]() The modeling of potential target systems will contribute to risk reduction in planning such migrations. Designers can choose as whether to augment their existing systems under existing architectures for the short term, or move directly to caBIG ™ based Grid systems, based upon model predicted future requirements for performance, computational services and increased repository capacity. ![]() Grid based systems such as caBIG ™ not only provide a potential architectural solution to the distributed data access requirements of registry data silos, but for the future translational research requirements of personalized medicine.Īgent-based models of potential system implementations will allow legacy database based project architects to make informed decisions regarding registry data silo expansion and adoption. The pressure on legacy client-server architecture derives not only from requirements for more widespread multi-project access in a semantically standardized manner, but also from significantly increased data volumes. The data storage requirements of translational registry data silos are under tremendous growth pressure with the incorporation of not only expanding genomic data volumes, but also with the inclusion of scanning imaging data and its extremely large data set sizes. Such resources in the future should be readily available to multiple translational research teams of investigators in a secure and federated structure, as provided by the caBIG ™ Grid system. In the existing, or legacy, client-server architecture systems, translational registry silos cannot be readily utilized in a standalone or isolated manner due to issues of security, semantic translation and extensive requirements for application interfacing between client and server systems. Grid systems make both database and analysis software available in a distributed manner across a number of loosely coupled nodes, interfacing via a number of well-defined, semantically equivalent, software services. More recent trends have been to provide database silos based within a Grid computing architecture system, such as provided by the caBIG ™ project. The registries provide data regarding cancer incidence, survival and mortality in the US. Existing translational registry silos are implemented with client-server system architectures, such as the Surveillance, Epidemiology and End Results (SEER) registries. In the healthcare field existing translational registry silos are a valuable resource, likely to significantly contribute to the future success of personalized medicine initiatives. With such models it is also possible to illustrate scaling effects for various distributed computer system architectures as a result of making topology changes to a Grid computing network, for instance by adding additional database silos to the system. ![]() With an agent-based model it is possible to reasonably and accurately model distributed computer system architectures such as exhibited with Grid computing and client-server system platforms. In modeling SEER nodes accessing multiple registry silos, we show that the performance of SEER applications re-implemented in a Grid native manner exhibits a nearly constant user response time with increasing numbers of distributed registry silos, compared with the current application architecture which exhibits a linear increase in response time for increasing numbers of silos. The model illustrates how the use of Grid technology can potentially improve system response time as systems under test are scaled. In order to investigate such a migration to a Grid based platform technology, this paper proposes using agent-based modeling simulations to predict the performance of current and Grid configurations of the NCI SEER system integrated with the existing translational opportunities afforded by caGRID. This modeling technique allows for simulation of complex and distributed services provided by a large scale Grid computing platform such as the caBIG ™ project’s caGRID. In order to model this migration, a simulation is proposed based upon an agent modeling technology. With the increasing age and cost of operation of the existing NCI SEER platform core technologies, such essential resources in the fight against cancer as these will eventually have to be migrated to Grid based systems.
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