The Community for Technology Leaders
RSS Icon
Issue No.02 - March/April (2011 vol.15)
pp: 19-26
Alexandru Iosup , Delft University of Technology
Dick Epema , Delft University of Technology
<p>In the mid 1990s, the grid computing community promised the "compute power grid," a utility computing infrastructure for scientists and engineers. Since then, a variety of grids have been built worldwide, for academic purposes, specific application domains, and general production work. Understanding grid workloads is important for the design and tuning of future grid resource managers and applications, especially in the recent wake of commercial grids and clouds. This article presents an overview of the most important characteristics of grid workloads in the past seven years (2003-2010). Although grid user populations range from tens to hundreds of individuals, a few users dominate each grid's workload both in terms of consumed resources and the number of jobs submitted to the system. Real grid workloads include very few parallel jobs but many independent single-machine jobs (tasks) grouped into single "bags of tasks."</p>
Workload characterization, workload analysis, grid computing, workflows, bags of tasks
Alexandru Iosup, Dick Epema, "Grid Computing Workloads", IEEE Internet Computing, vol.15, no. 2, pp. 19-26, March/April 2011, doi:10.1109/MIC.2010.130
1. I. Foster, C. Kesselman, and S. Tuecke, "The Anatomy of the Grid: Enabling Scalable Virtual Organizations," Int'l J High Performance Computing Applications, vol. 15, no. 3, 2001, pp. 200–222.
2. E. Deelman et al., "Workflows and E-Science: An Overview of Workflow System Features and Capabilities," Future Generation Computer Systems, vol. 25, no. 5, 2009, pp. 528–540.
3. T. Hey, S. Tansley, and K. Tolle eds., The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Research, 2009.
4. A. Iosup et al., "The Grid Workloads Archive," Future Generation Computer Systems, vol. 24, no. 7, 2008, pp. 672–686.
5. D. Thain et al., , "Pipeline and Batch Sharing in Grid Workloads," Proc. 12th IEEE Int'l Symp. High Performance Distributed Computing, IEEE CS Press, 2003, pp. 152–161.
6. A. Iamnitchi, S. Doraimani, and G. Garzoglio, "Filecules in High-Energy Physics: Characteristics and Impact on Resource Management," Proc. 15th IEEE Int'l Symp. High Performance Distributed Computing, IEEE CS Press, 2006, pp. 69–80.
7. A. Iosup et al., "The Characteristics and Performance of Groups of Jobs in Grids," International Euro-Par Conference on Parallel Processing, LNCS, vol. 4641, Springer Verlag, 2007, pp. 382–393.
8. A. Iosup et al., "The Performance of Bags-of-Tasks in Large-Scale Distributed Systems," Proc. 17th IEEE Int'l Symp. High Performance Distributed Computing, IEEE CS Press, 2008, pp. 97–108.
9. D. Kondo et al., "The Failure Trace Archive: Enabling Comparative Analysis of Failures in Diverse Distributed Systems," Proc. 10th IEEE/ACM Int'l Conf. Cluster, Cloud, and Grid Computing (CCGrid), IEEE Press, 2010, pp. 398–407.
10. S. Bharathi et al., "Characterization of Scientific Workflows," Workflows in Support of Large-Scale Science (WORKS), ACM Press, 2008, pp. 1–11.
11. S. Ostermann et al., "On the Characteristics of Grid Workflows," Integrated Research in Grid Computing, Springer Verlag, 2008, pp. 431–442.
12. O. Sonmez, H. Mohamed, and D. Epema, "On the Benefit of Processor Coallocation in Multicluster Grid Systems," IEEE Trans. Parallel Distributed Systems, vol. 21, no. 6, 2010, pp. 778–789.
13. I. Raicu et al., "Falkon: a Fast and Light-Weight Task Execution Framework," Proc. 2007 ACM/IEEE Conf. Supercomputing, 2007, p. 43.
14. M. Silberstein et al., "Gridbot: Execution of Bags of Tasks in Multiple Grids," Proc. 2009 ACM/IEEE Conf. Supercomputing, ACM Press, 2009, p. 11.
15. C. Stratan, A. Iosup, and D. Epema, "A Performance Study of Grid Workflow Engines," Proc. 9th IEEE/ACM Int'l Conf. Grid Computing, IEEE Press, 2008, pp. 25–32.
460 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool