Sadjadi, S.M., Florida International University (FIU), Miami, FL, United States; Shimizu, S., IBM Tokyo Research Laboratory, Tokyo, Japan; Figueroa, J., Florida International University (FIU), Miami, FL, United States, University of Miami, Coral Gables, FL, United States; Rangaswami, R., Florida International University (FIU), Miami, FL, United States; Delgado, J., Florida International University (FIU), Miami, FL, United States; Duran, H., University of Guadalajara, CUCEA, Mexico; Collazo-Mojica, X.J., University of Puerto Rico, Mayaguez Campus, Puerto Rico.
In a Grid computing environment, resources are shared among a large number of applications. Brokers and schedulers find matching resources and schedule the execution of the applications by monitoring dynamic resource availability and employing policies such as first-come-first-served and back-filling. To support applications with timeliness requirements in such an environment, brokering and scheduling algorithms must address an additional problem - they must be able to estimate the execution time of the application on the currently available resources. In this paper, we present a modeling approach to estimating the execution time of long-running scientific applications. The modeling approach we propose is generic; models can be constructed by merely observing the application execution "externally" without using intrusive techniques such as code inspection or instrumentation. The model is cross-platform; it enables prediction without the need for the application to be profiled first on the target hardware. To show the feasibility and effectiveness of this approach, we developed a resource usage model that estimates the execution time of a weather forecasting application in a multi-cluster Grid computing environment. We validated the model through extensive benchmarking and profiling experiments and observed prediction errors that were within 10% of the measured values. Based on our initial experience, we believe that our approach can be used to model the execution time of other time-sensitive scientific applications; thereby, enabling the development of more intelligent brokering and scheduling algorithms. "2008 IEEE.",,,,,,"10.1109/IPDPS.2008.4536214",,,"http://hdl.handle.net/20.500.12104/39020","http://www.scopus.com/inward/record.url?eid=2-s2.0-51049089563&partnerID=40&md5=6103515c32ca79ce994612f655783642",,,,,,,,"IPDPS Miami 2008 - Proceedings of the 22nd IEEE International Parallel and Distributed Processing Symposium, Program and CD-ROM",,,,,,"Scopus",,,,,,,,,,,,"A modeling approach for estimating execution time of long-running scientific applications",,"Conference Paper"
"44682","123456789/35008",,"Barba, J., Universidad de Guadalajara, Centro Universitario de Ciencias Biológicas y Agropecuarias, Departamento de Salud Pública, Km 15.5 carretera Guadalajara-Nogales, C.P. 44171, Zapopan, Jalisco, Mexico; Alvarez, A.H., Centro de Investigacion y Asistencia en Tecnología y diseño del Estado de Jalisco, A.C., Biotecnología Médica y Farmacéutica, Av. Normalistas 800, Col. Colinas de la Normal, CP 44270, Guadalajara, Jalisco, Mexico; Flores-Valdez, M.A., Centro de Investigacion y Asistencia en Tecnología y diseño del Estado de Jalisco, A.C., Biotecnología Médica y Farmacéutica, Av. Normalistas 800, Col. Colinas de la Normal, CP 44270, Guadalajara, Jalisco, Mexico",,"Barba, J..