Using Application Skeletons to Improve eScience Infrastructure

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 easy-to-access, easy-to-build, and easy-to-run bag-of-task, (iterative) map-reduce, and (iterative) multistage workflow applications. The tasks can be serial or parallel or a mix of both. We select three representative applications (Montage, BLAST, CyberShake Postprocessing), then describe and generate skeleton applications for each. We show that the skeleton applications have identical (or close) performance to that of the real applications. We then show examples of using skeleton applications to verify system optimizations such as data caching, I/O tuning, and task scheduling, as well as the system resilience mechanism, in some cases modifying the skeleton applications to emphasize some characteristic, and thus show that using skeleton applications simplifies the process of designing, implementing, a- d testing these optimizations.