ATLAS grid workload on NDGF resources: Analysis, modeling, and workload generation

Karpenko, Dmytro; Vitenberg, Roman; Read, A. L.

doi:10.1016/j.future.2014.12.005

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…A project focusing on single set of applications, for the ATLAS high energy physics experiment [31] included workload generator software that has some similarity with our application skeletons, though it is much less general.…”

Section: Modeling Background and Related Workmentioning

confidence: 99%

Application skeletons: Construction and use in eScience

Merzky

Zhang

Jha

2016

Future Generation Computer Systems

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Computer scientists who work on tools and systems to support eScience (a variety of parallel and distributed) applications usually use actual applications to prove that their systems will benefit science and engineering (e.g., improve application performance). Accessing and building the applications and necessary data sets can be difficult because of policy or technical issues, and it can be difficult to modify the characteristics of the applications to understand corner cases in the system design. In this paper, we present the Application Skeleton, a simple yet powerful tool to build synthetic applications that represent real applications, with runtime and I/O close to those of the real applications. This allows computer scientists to focus on the system they are building; they can work with the simpler skeleton applications and be sure that their work will also be applicable to the real applications. In addition, skeleton applications support simple reproducible system experiments since they are represented by a compact set of parameters. Our Application Skeleton tool (available as open source at https:// github.com/applicationskeleton/Skeleton) currently can create easyto-access, easy-to-build, and easy-to-run bag-of-task, (iterative) map-reduce, and (iterative) multistage workflow applications. The tasks can be serial, parallel, or a mix of both. The parameters to represent the tasks can either be discovered through a manual profiling of the applications or through an automated method. We select three representative applications (Montage,

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Section: Modeling Background and Related Workmentioning

confidence: 99%