Abstract
In this paper, we propose a meta-data based approach for a deliberate job flow distribution in computing environments, such as utility Grids. Under conditions of a heterogeneous job flow composition and a variety of resource domains, we examine how different job and resource characteristics affect the efficiency of the scheduling process. Based on the most significant job flow and resource domain characteristics a heuristic distribution quality indicator is introduced. Additional simulation study is performed to verify the indicator in different distribution strategies and to compare them with a random job flow allocation.
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References
The Moab adaptive computing suite. http://www.adaptivecomputing.com/products/moab-adaptive-computing-suite.php
Berman, F., Wolski, R., Casanova, H.: Adaptive computing on the Grid using AppLeS. Trans. Parallel Distrib. Syst. 14(4), 369–382 (2003)
Buyya, R., Abramson, D., Giddy, J.: Economic models for resource management and scheduling in Grid computing. J. Concurr. Comput. 14(5), 1507–1542 (2002)
Cafaro, M., Mirto, M., Aloisio, G.: Preference-based matchmaking of Grid resources with CP-Nets. J. Grid Comput. 11(2), 211–237 (2013)
Cirne, W., Brasileiro, F., Costa, L., Paranhos, D., Santos-neto, E., Andrade, N., Grande, C.: Scheduling in bag-of-task grids: the PAUA case. In: Proceedings of the 16th Symposium on Computer Architecture and High Performance Computing, pp. 124–131. IEEE Computer Society Press (2004)
Dail, H., Sievert, O., Berman, F., Casanova, H., Yarkhan, A., Vadhiyar S., Dongarra, J., Liu, C., Yang, L., Angulo, D., Foster, I.: Scheduling in the grid application development software project. In: Nabrzyski, J., Schopf, J.M., Weglarz, J. (eds.) Grid Resource Management. State of the Art and Future Trends, pp. 73–98. Kluwer Academic Publisher (2003)
Ernemann, C., Hamscher, V., Yahyapour, R.: Economic scheduling in Grid computing. In: Feitelson, D., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP, vol. 18, pp. 128–152. Springer, Heidelberg (2002)
Garg, S.K., Konugurthi, P., Buyya, R.: A linear programming-driven genetic algorithm for meta-scheduling on utility Grids. J. Par. Emergent Distr. Syst. 26, 493–517 (2011)
Kannan, S., Roberts, M., Mayes, P.: Workload management with LoadLeveler (2001)
Kurowski, K., Oleksiak, A., Nabrzyski, J.: Multi-criteria grid resource management using performance prediction techniques. In: Gorlatch, S., Danelutto, M. (eds.) Integrated Research in GRID Computing, pp. 215–225. Springer, Berlin (2007)
Mutz, A., Wolski, R., Brevik, J.: Eliciting honest value information in a batch-queue environment. In: 8th IEEE/ACM International Conference on Grid Computing, pp. 291–297, New York. ACM (2007)
Soner, S., Ozturan, C.: Integer programming based heterogeneous CPU-GPU cluster scheduler for SLURM resource manager. In: 14th IEEE International Conference on High Performance Computing and Communication and 9th IEEE International Conference on Embedded Software and Systems, pp. 418–424, Liverpool. IEEE (2012)
Toporkov, V., Toporkova, A., Tselishchev, A., Yemelyanov, D.: Slot selection algorithms in distributed computing. J. Supercomput. 69(1), 53–60 (2014)
Toporkov, V., Toporkova, A., Tselishchev, A., Yemelyanov, D., Potekhin, P.: Preference-based fair resource sharing and scheduling optimization in Grid VOs. Procedia Comput. Sci. 29, 831–843 (2014)
Toporkov, V., Tselishchev, A., Yemelyanov, D., Bobchenkov, A.: Composite scheduling strategies in distributed computing with non-dedicated resources. Procedia Comput. Sci. 9, 176–185 (2012)
Toporkov, V.V., Yemelyanov, D.M.: Economic model of scheduling and fair resource sharing in distributed computations. Program. Comput. Softw. 40(1), 35–42 (2014)
Tsafrir, D., Etsion, Y., Feitelson, D.: Backfilling using system-generated predictions rather than user runtime estimates. In: Transactions on Parallel and Distributed Systems, pp. 789–803. IEEE (2007)
Voevodin, V.: The solution of large problems in distributed computational media. Autom. Remote Control 68(5), 773–786 (2007)
Zhou, Z., Lan, Z., Tang, W., Desai, N.: Reducing energy costs for IBM Blue Gene/P via power-aware job scheduling. In: Seventeenth Workshop on Job Scheduling Strategies for Parallel Processing, pp. 96–115, Massachusetts (2013)
Acknowledgments
This work was partially supported by the Council on Grants of the President of the Russian Federation for State Support of Young Scientists and Leading Scientific Schools (grants YPhD-4148.2015.9 and SS-362.2014.9), RFBR (grants 15-07-02259 and 15-07-03401), the Ministry on Education and Science of the Russian Federation, task no. 2014/123 (project no. 2268), and by the Russian Science Foundation (project no. 15-11-10010).
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Toporkov, V., Toporkova, A., Tselishchev, A., Yemelyanov, D., Potekhin, P. (2016). Heuristic-Based Job Flow Allocation in Distributed Computing. In: Novais, P., Camacho, D., Analide, C., El Fallah Seghrouchni, A., Badica, C. (eds) Intelligent Distributed Computing IX. Studies in Computational Intelligence, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-25017-5_18
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DOI: https://doi.org/10.1007/978-3-319-25017-5_18
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