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For large-scale scientific simulations, it is expensive to store rawsimulation results to perform post-analysis. To minimize expensive I/O,"in-situ" analysis is often used, where analysis applications are tightlycoupled with scientific simulations and can access and process the simulationresults in memory. Increasingly, scientific domains employ Big Data approachesto analyze simulations for scientific discoveries. However, it remains achallenge to organize, transform, and transport data at scale between the twosemantically different ecosystems (HPC and Cloud systems). In an effort toaddress these challenges, we design and implement the ElasticBroker softwareframework, which bridges HPC and Cloud applications to form an "in-situ"scientific workflow. Instead of writing simulation results to parallel filesystems, ElasticBroker performs data filtering, aggregation, and formatconversions to close the gap between an HPC ecosystem and a distinct Cloudecosystem. To achieve this goal, ElasticBroker reorganizes simulation snapshotsinto continuous data streams and send them to the Cloud. In the Cloud, wedeploy a distributed stream processing service to perform online data analysis.In our experiments, we use ElasticBroker to setup and execute a cross-ecosystemscientific workflow, which consists of a parallel computational fluid dynamics(CFD) simulation running on a supercomputer, and a parallel dynamic modedecomposition (DMD) analysis application running in a Cloud computing platform.Our results show that running scientific workflows consisting of decoupled HPCand Big Data jobs in their native environments with ElasticBroker, can achievehigh quality of service, good scalability, and provide high-quality analyticsfor ongoing simulations.

ElasticBroker: Combining HPC with Cloud to Provide Realtime Insights into Simulations