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.
This work is supported by the National Natural Science Foundation of China (No. 90412012, 60429202, 60372019 and 60373013), NSFC and RGC (No. 60218003), and the National Grand Fundamental Research 973 Program of China (No.2003CB314804).
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Li, Y., Lin, C., Li, Q., Shan, Z. (2005). Performance Modeling and Analysis for Resource Scheduling in Data Grids. In: Jin, H., Reed, D., Jiang, W. (eds) Network and Parallel Computing. NPC 2005. Lecture Notes in Computer Science, vol 3779. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11577188_5
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DOI: https://doi.org/10.1007/11577188_5
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