A modeling approach for estimating execution time of long-running scientific applications Conference

Sadjadi, SM, Shimizu, S, Figueroa, J et al. (2008). A modeling approach for estimating execution time of long-running scientific applications . 10.1109/IPDPS.2008.4536214

cited authors

  • Sadjadi, SM; Shimizu, S; Figueroa, J; Rangaswami, R; Delgado, J; Duran, H; Collazo-Mojica, XJ

authors

abstract

  • 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.

publication date

  • September 10, 2008

Digital Object Identifier (DOI)

International Standard Book Number (ISBN) 13