Abstract
In recent years, Grid computing has emerged as an attractive platform to tackle various large-scale problems, especially in the field of science and engineering. Scheduling Grid resources involves a number of challenging issues, mainly due to the distributed and dynamic nature of the Grids. This paper focuses on the resource allocation for a particular type of resource intensive tasks called Processable Bulk Data Transfer (PBDT) tasks in a Grid environment. The defining trait of a PBDT task is a large raw data-file at a source node that needs to be processed in some way before it can be used at a set of sink nodes. Our scheduling approach uses a Bi-level decision-making architecture. This paper analyzes the performance of the proposed architecture at various workload conditions. This architecture can be extended for other types of tasks using the concepts presented.
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References
Abbas, A.: Grid Computing: A Practical Guide to Technology and Applications. Charles River Media (2004)
Ahmad, I., Majumdar, S.: An adaptive high performance architecture for processable bulk data transfers on a Grid. In: 2nd International Conference on Broadband Networks (Broadnets), October 3-7, pp. 1482–1491. IEEE, Boston (2005)
Ahmad, I., Majumdar, S.: Efficient Allocation of Grid Resources Using a Bi-level Decision-Making Architecture for ”Processable” Bulk Data. In: Meersman, R., Tari, Z. (eds.) OTM 2007, Part II. LNCS, vol. 4804, pp. 1313–1321. Springer, Heidelberg (2007)
Allcock, B., Chervenak, A., Foster, I., Kesselman, C., Livny, M.: Data Grid tools: enabling science on big distributed data. Journal of Physics: Conference Series 16(1), 571–575 (2005)
Bunn, J., Newman, H.: Data-intensive Grids for high energy physics. In: Berman, G., Hey, E. (eds.) Grid Computing: Making the Global Infrastructure a Reality. John Wiley & Sons, New York (2003)
Downey, A.B.: Lognormal and Pareto distributions in the Internet. Comput. Commun. 28(7), 790–801 (2005)
Foster, I., Kesselman, C.: The Grid: Blueprint for a Future Computing Infrastructure. Morgan Kaufmann Publishers, USA (1999)
Foster, I., Kesselman, C., Lee, C., Lindell, B., Nahrstedt, K., Roy, A.: A distributed resource management architecture that supports advance reservations and co-allocation. In: Anonymous Proceedings of IWQoS 1999 - Seventh International Workshop on Quality of Service, May 31-June 4, pp. 27–36. IEEE, London (1999)
Elayeb, M.: Efficient Data Scheduling For Real-Time Large Scale Data Intensive Distributed Applications (Masters Dissertation, The Ohio State University)
Paranhos, D., Cirne, W., Brasileiro, F.: Trading Cycles for Information: Using Replication to Schedule Bag-of-Tasks Applications on Computational Grids. In: Kosch, H., Böszörményi, L., Hellwagner, H. (eds.) Euro-Par 2003, vol. 2790, pp. 150–169. Springer, Heidelberg (2003)
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Ahmad, I., Majumdar, S. (2008). A Two Level Approach for Managing Resource and Data Intensive Tasks in Grids. In: Meersman, R., Tari, Z. (eds) On the Move to Meaningful Internet Systems: OTM 2008. OTM 2008. Lecture Notes in Computer Science, vol 5331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88871-0_56
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DOI: https://doi.org/10.1007/978-3-540-88871-0_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88870-3
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