GRAPLEr: A distributed collaborative environment for lake ecosystem modeling that integrates overlay networks, high‐throughput computing, and WEB services. (18th April 2017)

• Record Type: Journal Article
• Authors: Subratie, Kensworth C.; Aditya, Saumitra; Mahesula, Srinivas; Figueiredo, Renato; Carey, Cayelan C.; Hanson, Paul C.
• Other Names: Plale Beth guestEditor.; Chen Maio guestEditor.; Escudero‐Sahuquillo Jesús guestEditor.; Garcia Pedro Javier guestEditor.
• Is Part Of: Concurrency and computation. Volume 29:Number 13(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-04-18
• Subjects: climate change -- distributed computing -- general lake model -- HTCondor -- IPOP‐VPN -- lake modeling -- overlay networks; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4139
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The GLEON Research And PRAGMA Lake Expedition—GRAPLE—is a collaborative effort between computer science and lake ecology researchers. It aims to improve our understanding and predictive capacity of the threats to the water quality of our freshwater resources, including climate change. This paper presents GRAPLEr, a distributed computing system used to address the modeling needs of GRAPLE researchers. GRAPLEr integrates and applies overlay virtual network, high‐throughput computing, and WEB service technologies in a novel way. First, its user‐level IP‐over‐P2P overlay network allows compute and storage resources distributed across independently administered institutions (including private and public clouds) to be aggregated into a common virtual network, despite the presence of firewalls and network address translators. Second, resources aggregated by the IP‐over‐P2P virtual network run unmodified high‐throughput‐computing middleware to enable large numbers of model simulations to be executed concurrently across the distributed computing resources. Third, a WEB service interface allows end users to submit job requests to the system using client libraries that integrate with the R statistical computing environment. The paper presents the GRAPLEr architecture, describes its implementation and reports on its performance for batches of general lake model simulations across 3 cloud infrastructures (University of Florida, CloudLab, and Microsoft Azure).