2015 IEEE 11th International Conference on e-Science (e-Science) (2015)
Aug. 31, 2015 to Sept. 4, 2015
As scientific simulation applications evolve on the path towards exascale, a new model of scientific inquiry is required where concurrently with the running simulation, online analytics operate on the data it produces. By avoiding offline data storage except when absoluately necessary, it enables speeding up the scientific discovery process by providing rapid insights into the simulated science phenomena and affording more frequent, detailed data analytics than is possible with the traditional purely offline approach of using disk for intermediate data storage. However, a challenge for online analytics is to respond to behavior dynamics caused by changing simulation outputs and by unforeseen events on the underlying hardware/software platforms. This paper presents SODA, a set of run-time abstractions for online orchestration of data analytics, realized by embedding analytics tasks into workstations that monitor component behavior and enable responses to run-time changes in their resource demands and in the platform's resource availability. For high end simulations running on a leadership class machine, experimental evaluations show SODA can invoke efficient orchestration operations responding to a diverse set of run-time dynamics at different granularities to meet end-user and analysis specific requirements.
Workstations, Pipelines, Analytical models, Monitoring, Data models, Runtime, Data analysis
J. Dayal et al., "SODA: Science-Driven Orchestration of Data Analytics," 2015 IEEE 11th International Conference on e-Science (e-Science)(E-SCIENCE), Munich, Germany, 2015, pp. 475-484.