Abstract
Selecting appropriate resources for running a job efficiently is one of the common objectives in a computational grid. Resource scheduling should consider the specific characteristics of the application, and decide the metrics to be used accordingly. This paper presents a distributed resource scheduling framework mainly consisting of a job scheduler and a local scheduler. In order to meet the requirements of different applications, we adopt HGSA, a Heuristic-based Greedy Scheduling Algorithm, to schedule jobs in the grid, where the heuristic knowledge is the metric weights of the computing resources and the metric workload impact factors. The metric weight is used to control the effect of the metric on the application. For different applications, only metric weights and the metric workload impact factors need to be changed, while the scheduling algorithm remains the same. Experimental results are presented to demonstrate the adaptability of the HGSA.
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Aggarwal, A.K., Kent, R.D., 2005. An Adaptive Generalized Scheduler for Grid Applications. Proceedings of the 19th Annual International Symposium on High Performance Computing Systems and Applications (HPCS’05). Guelph, Ontario, Canada, p.15–18.
Berman, F., Wolski, R., Casanova, H., Cirne, W., Dail, H., Faerman, M., Figueira, S., Hayes, J., Obertelli, G., Schopf, J., et al., 2003. Adaptive computing on the grid using AppLeS. IEEE Transactions on Parallel and Distributed Systems, 14(4):369–382. [doi:10.1109/TPDS.2003.1195409]
Casanova, H., 2001. Simgrid: A Toolkit for the Simulation of Application Scheduling. Proceedings of the IEEE Symposium on Cluster Computing and the Grid (CCGrid’01). IEEE Computer Society, p.430–437.
Casanova, H., Obertelli, G., Berman, F., Wolski, R., 2000. The AppLeS Parameter Sweep Template: User-Level Middleware for the Grid. Proceedings of Supercomputing 2000. IEEE Computer Society Press, Dallas, USA, p.75–76.
Chapin, S.J., Spafford, E.H., 1994. Support for implementing scheduling algorithms using MESSIAHS. Scientific Programming, 3:325–340.
Foster, I., Kesselman, C., 1998. The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers, San Francisco, CA, USA.
Foster, I., Kesselman, C., Tuecke, S., 2001. The anatomy of the grid: enabling scalable virtual organizations. International Journal of Supercomputer Applications, 15(3):200–222.
Gao, Y., Rong, H.Q., Huang, J.Z.X., 2005. Adaptive grid job scheduling with genetic algorithms. Future Generation Computer Systems, 21(1):151–161. [doi:10.1016/j.future.2004.09.033]
Huedo, E., Montero, R.S., Llorente, I.M., 2004. Experiences on Adaptive Grid Scheduling of Parameter Sweep Applications. Proceedings of the 12th Euromicro Conference on Parallel, Distributed and Network-based Processing (PDP’04). A Coruña, Spain, p.28–33. [doi:10.1109/EMPDP.2004.1271423]
Jin, H., Shi, X., Qiang, W., Zou, D., 2005. An adaptive meta-scheduler for data-intensive applications. International Journal of Grid and Utility Computing, 1(1):32–37. [doi:10.1504/IJGUC.2005.007058]
Legrand, A., Marchal, L., Casanova, H., 2003. Scheduling Distributed Applications: The SimGrid Simulation Framework. Proceedings of the 3rd IEEE International Symposium on Cluster Computing and the Grid (CCGrid’03). Tokyo, Japan, p.138–145.
Liu, C., Yang, L.Y., Foster, I., Angulo, D., 2002. Design and Evaluation of a Resource Selection Framework for Grid Applications. Proceedings of IEEE International Symposium on High Performance Distributed Computing (HPDC-11). IEEE CS Press, p.63–72.
Petitet, A., Blackford, S., Dongarra, J., Ellis, B., Fagg, G., Roche, K., Vadhiyar, S., 2001. Numerical libraries and the grid. The International Journal of High Performance Computing Applications, 15(4):359–374. [doi:10.1177/109434200101500403]
Ranganathan, K., Foster, I., 2002. Decoupling Computation and Data Scheduling in Distributed Data-Intensive Applications. Proceedings of 11th IEEE International Symposium on High Performance Distributed Computing (HPDC-11). IEEE CS Press, p.352–358.
Yang, L.Y., Schopf, J.M., Foster, I., 2003. Conservative Scheduling: Using Predicted Variance to Improve Scheduling Decisions in Dynamic Environments. Proceedings of Supercomputing 2003. ACM Press, Phoenix, AZ, USA, p.31–46.
YarKhan, A., Dongarra, J.J., 2002. Experiments with Scheduling Using Simulated Annealing in a Grid Environment. Third International Workshop on Grid Computing. LNCS 2536, p.232–242.
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Project supported by the National Natural Science Foundation of China (No. 60225009), and the National Science Fund for Distinguished Young Scholars, China
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Luan, Cj., Song, Gh. & Zheng, Y. Application-adaptive resource scheduling in a computational grid. J. Zhejiang Univ. - Sci. A 7, 1634–1641 (2006). https://doi.org/10.1631/jzus.2006.A1634
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DOI: https://doi.org/10.1631/jzus.2006.A1634