In: Hai Jin, Daniel Reed, Wenbin Jiang (Eds.): Lecture Notes in Computer Science, 3779: Network and Parallel Computing: IFIP International Conference, NPC 2005, Beijing, China, November 30 - December 3, 2005., pages 32-39. Springer-Verlag, November 2005. URL: http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/115771885,.
Abstract: Data Grids normally deal with large data-intensive problems on geographically distributed resources; yet, most current research on performance evaluation of resource scheduling in Data Grids is based on simulation techniques, which can only consider a limited range of scenarios. In this paper, we propose a formal framework via Stochastic Petri Nets to deal with this problem. Within this framework, we model and analyze the performance of resource scheduling in Data Grids, allowing for a wide variety of job and data scheduling algorithms. As a result of our research, we can investigate more scenarios with multiple input parameters. Moreover, we can evaluate the combined effectiveness of job and data scheduling algorithms, rather than study them separately.