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Faodel: Data management for next-generation application workflows

Proceedings of the 9th Workshop on Scientific Cloud Computing, ScienceCloud 2018 - Co-located with HPDC 2018

Ulmer, Craig D.; Mukherjee, Shyamali M.; Templet, Gary J.; Kordenbrock, Todd; Levy, Scott; Lofstead, Jay; Widener, Patrick W.; Lawson, Margaret R.

Composition of computational science applications, whether into ad hoc pipelines for analysis of simulation data or into well-defined and repeatable workflows, is becoming commonplace. In order to scale well as projected system and data sizes increase, developers will have to address a number of looming challenges. Increased contention for parallel filesystem bandwidth, accomodating in situ and ex situ processing, and the advent of decentralized programming models will all complicate application composition for next-generation systems. In this paper, we introduce a set of data services, Faodel, which provide scalable data management for workflows and composed applications. Faodel allows workflow components to directly and efficiently exchange data in semantically appropriate forms, rather than those dictated by the storage hierarchy or programming model in use. We describe the architecture of Faodel and present preliminary performance results demonstrating its potential for scalability in workflow scenarios.

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9 Results
9 Results