Abstract
Scheduling of complex scientific workflows on geographically distributed resources is a challenging problem. Selection and scheduling of a subset of available resources to meet a given demand in a cost efficient manner is the first step of this complex process. In this paper, we develop a method to compute cost-efficient selection and scheduling of resources under demand uncertainties. Building on the techniques of Sample Average Approximation and Genetic Algorithms, we demonstrate that our method can lead up to \(24\%\) improvement in costs when demand uncertainties are explicitly considered. We present the results from our preliminary work in the context of a high energy physics application, the Belle II experiments, and believe that the work will equally benefit other scientific workflows executed on distributed resources with demand uncertainties. The proposed method can also be extended to include uncertainties related to resource availability and network performance.
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Notes
- 1.
HEP SPEC is based on SPEC CPU 2006. Further information is available from http://w3.hepix.org/benchmarks/doku.php/.
References
Asner, D.M., Dart, E., Hara, T.: Belle II experiment network and computing. arXiv preprint arXiv:1308.0672 (2013)
Batista, D.M., Drummond, A.C., da Fonseca, N.L.S.: Scheduling grid tasks under uncertain demands. In: Proceedings of the 2008 ACM Symposium on Applied Computing, SAC 2008, pp. 2041–2045. ACM, New York (2008)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Dogan, A., Ozguner, F.: Genetic algorithm based scheduling of meta-tasks with stochastic execution times in heterogeneous computing systems. Cluster Comput. 7(2), 177–190 (2004)
Friese, R.D.: Efficient genetic algorithm encoding for large-scale multi-objective resource allocation. In: 9th Workshop on Large-Scale Parallel Processing (LSPP 2016), in the Proceedings of the IPDPS 2016 Workshops and PhD Forum (IPDPSW), May 2016
Garshasbi, M.S., Effatparvar, M.: High performance scheduling in parallel heterogeneous multiprocessor systems using evolutionary algorithms. Int. J. Intell. Syst. Appl. 11, 89–95 (2013)
Gholami, A., Shekari, T., Aminifar, F., Shahidehpour, M.: Microgrid scheduling with uncertainty: the quest for resilience. IEEE Trans. Smart Grid 7(6), 2849–2858 (2016)
Gorges-Schleuter, M.: Explicit parallelism of genetic algorithms through population structures. In: 1st Workshop on Parallel Problem Solving from Nature (PPSN), pp. 150–159 (1990)
Halappanavar, M., Schram, M., de la Torre, L., Barker, K., Tallent, N.R., Kerbyson, D.J.: Towards efficient scheduling of data intensive high energy physics workflows. In: Proceedings of the 10th Workshop on Workflows in Support of Large-Scale Science, WORKS 2015, pp. 3:1–3:9. ACM, New York (2015)
Hara, T.: Belle II: computing and network requirements. In: Proceedings of the Asia-Pacific Advanced Network, pp. 115–122 (2014)
Holland, J.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, 1st edn. The University of Michigan, Ann Arbor (1975)
Johannes, A., Borhan, N., Liu, C., Ranjan, R., Chen, J.: A user demand uncertainty based approach for cloud resource management. In: 2013 IEEE 16th International Conference on Computational Science and Engineering, pp. 566–571, December 2013
Johansson, M., Sternad, M.: Resource allocation under uncertainty using the maximum entropy principle. IEEE Trans. Inf. Theor. 51(12), 4103–4117 (2005)
Kazarlis, S.A., Bakirtzis, A., Petridis, V.: A genetic algorithm solution to the unit commitment problem. IEEE Trans. Power Syst. 11(1), 42–51 (1996)
Kim, S., Pasupathy, R., Henderson, S.G.: A guide to sample average approximation. In: Fu, M. (ed.) Handbook of Simulation Optimization, pp. 207–243. Springer, New York (2015)
Li, Z., Ierapetritou, M.: Process scheduling under uncertainty: review and challenges. Comput. Chem. Eng. 32(4–5), 715–727 (2008)
Medernach, E., Sanlaville, E.: Fair resource allocation for different scenarios of demands. Eur. J. Oper. Res. 218(2), 339–350 (2012)
Michalewicz, Z., Fogel, D.B.: How to Solve It: Modern Heuristics, 2nd edn. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-662-07807-5
Miller, B., Goldberg, D.: Genetic algorithms, tournament selection, and the effects of noise. Complex Syst. 9, 193–212 (1995)
Oxley, M., Pasricha, S., Siegel, H.J., Maciejewski, A.A., Apodaca, J., Young, D., Briceno, L., Smite, J., Bahirat, S., Khemka, B., Ramirez, A., Zou, Y.: Makespan and energy robust stochastic static resource allocation of a bag-of-tasks to a heterogeneous computing system. IEEE Trans. Parallel Distrib. Syst. 26(10), 2791–2805 (2015)
Ruiz, P.A., Philbrick, C.R., Zak, E., Cheung, K.W., Sauer, P.W.: Uncertainty management in the unit commitment problem. IEEE Trans. Power Syst. 24(2), 642–651 (2009)
Saravanan, B., Das, S., Sikri, S., Kothari, D.: A solution to the unit commitment problem - a review. Front. Energy 7(2), 223–236 (2013)
Singer, G., Livenson, I., Dumas, M., Srirama, S.N., Norbisrath, U.: Towards a model for cloud computing cost estimation with reserved instances. In: Proceedings of the 2nd International ICST Conference on Cloud Computing, CloudComp 2010 (2010)
Singh, H., Youssef, A.: Mapping and scheduling heterogeneous task graphs using genetic algorithms. In: 5th Heterogeneous Computing Workshop (HCW 1996), pp. 86–97, April 1996
Swarup, K.S., Yamashiro, S.: Unit commitment solution methodology using genetic algorithm. IEEE Trans. Power Syst. 17(1), 87–91 (2002)
Tanese, R.: Distributed genetic algorithms. In: 3rd International Conference on Genetic Algorithms, pp. 434–439 (1989)
Wang, L., Siegel, H.J., Roychowdhury, V.P., Maciejewski, A.A.: Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach. J. Parallel Distrib. Comput. 47(1), 8–22 (1997). Special Issue on Parallel Evolutionary Computing
Wang, Q., Wang, J., Guan, Y.: Stochastic unit commitment with uncertain demand response. IEEE Trans. Power Syst. 28(1), 562–563 (2013)
Whitley, D.: The genitor algorithm and selective pressure: why rank based allocation of reproductive trials is best. In: 3rd International Conference on Genetic Algorithms, pp. 116–121, June 1989
Wright, B.: A review of unit commitment (2013)
Zhang, Q., Gürses, E., Boutaba, R., Xiao, J.: Dynamic resource allocation for spot markets in clouds. In: Proceedings of the 11th USENIX Conference on Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services, Hot-ICE 2011, p. 1. USENIX Association, Berkeley (2011)
Acknowledgment
This work was supported by the Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows (IPPD) Project. IPPD is funded by the U. S. Department of Energy Awards FWP-66406 and DE-SC0012630 at the Pacific Northwest National Laboratory. Pacific Northwest National Laboratory is operated by Battelle for the DOE under Contract DE-AC05-76RL01830. The work of Luis de la Torre was supported in part by the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Visiting Faculty Program (VFP).
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Friese, R.D., Halappanavar, M., Sathanur, A.V., Schram, M., Kerbyson, D.J., de la Torre, L. (2018). Towards Efficient Resource Allocation for Distributed Workflows Under Demand Uncertainties. In: Klusáček, D., Cirne, W., Desai, N. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 2017. Lecture Notes in Computer Science(), vol 10773. Springer, Cham. https://doi.org/10.1007/978-3-319-77398-8_6
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