Abstract
This article presents a multiobjective approach for scheduling large workflows in distributed datacenters. We consider a realistic scheduling scenario of distributed cluster systems composed of multi-core computers, and a multi-objective formulation of the scheduling problem to minimize makespan, energy consumption and deadline violations. The studied schedulers follow a two-level schema: in the higher-level, we apply a multiobjective heuristic and a multiobjective metaheuristic, to distribute jobs between clusters; in the lower-level, specific backfilling-oriented scheduling methods are used for task scheduling locally within each cluster, considering precedence constraints. A new model for energy consumption in multi-core computers is applied. The experimental evaluation performed on a benchmark set of large workloads that model different realistic high performance computing applications demonstrates that the proposed multiobjective schedulers are able to improve both the makespan and energy consumption of the schedules when compared with a standard Optimistic Load Balancing Round Robin approach.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ahmad, I., Ranka, S.: Handbook of Energy-Aware and Green Computing. Chapman & Hall/CRC, Boca Raton (2012)
Bäck, T., Fogel, D., Michalewicz, Z.: Handbook of Evolutionary Computation. Oxford University Press, New York (1997)
Baskiyar, S., Abdel-Kader, R.: Energy aware DAG scheduling on heterogeneous systems. Cluster Comput. 13, 373–383 (2010)
Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for Solving Multi-objective Problems. Kluwer, New York (2002)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley, Chichester (2001)
Dorronsoro, B., Nesmachnow, S., Taheri, J., Zomaya, A., Talbi, E.G., Bouvry, P.: A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems. Sustain. Comput. Inf. Syst. 4(4), 252–261 (2014)
Hirales-Carbajal, A., Tchernykh, A., Yahyapour, R., González-García, J., Röblitz, T., Ramírez-Alcaraz, J.: Multiple workflow scheduling strategies with user run time estimates on a grid. J. Grid Comput. 10(2), 325–346 (2012)
Iturriaga, S., Nesmachnow, S., Dorronsoro, B., Bouvry, P.: Energy efficient scheduling in heterogeneous systems with a parallel multiobjective local search. Comput. Inf. J. 32(2), 273–294 (2013)
Khan, S., Ahmad, I.: A cooperative game theoretical technique for joint optimization of energy consumption and response time in computational grids. IEEE Trans. Parallel Distrib. Syst. 20, 346–360 (2009)
Kim, J.K., Siegel, H., Maciejewski, A., Eigenmann, R.: Dynamic resource management in energy constrained heterogeneous computing systems using voltage scaling. IEEE Trans. Parallel Distrib. Syst. 19, 1445–1457 (2008)
Lee, Y., Zomaya, A.: Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans. Parallel Distrib. Syst. 22, 1374–1381 (2011)
Li, Y., Liu, Y., Qian, D.: A heuristic energy-aware scheduling algorithm for heterogeneous clusters. In: Proceedings of the 15\(^{th}\) International Conference on Parallel and Distributed System, pp. 407–413 (2009)
Lindberg, P., Leingang, J., Lysaker, D., Khan, S., Li, J.: Comparison and analysis of eight scheduling heuristics for the optimization of energy consumption and makespan in large-scale distributed systems. J. Supercomputing 59(1), 323–360 (2012)
Luo, P., Lü, K., Shi, Z.: A revisit of fast greedy heuristics for mapping a class of independent tasks onto heterogeneous computing systems. J. Parallel Distrib. Comput. 67(6), 695–714 (2007)
Mezmaz, M., Melab, N., Kessaci, Y., Lee, Y., Talbi, E.G., Zomaya, A., Tuyttens, D.: A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J. Parallel Distrib. Comput. 71, 1497–1508 (2011)
Nesmachnow, S.: Computación científica de alto desempeño en la Facultad de Ingeniería, Universidad de la República. Revista de la Asociación de Ingenieros del Uruguay 61, pp. 12–15 (2010). (text in Spanish)
Nesmachnow, S., Dorronsoro, B., Pecero, J.E., Bouvry, P.: Energy-aware sche-duling on multicore heterogeneous grid computing systems. J. Grid Comput. 11(4), 653–680 (2013)
Pecero, J., Bouvry, P., Fraire, H., Khan, S.: A multi-objective grasp algorithm for joint optimization of energy consumption and schedule length of precedence-constrained applications. In: International Conference on Cloud and Green Computing, pp. 1–8 (2011)
Pinel, F., Dorronsoro, B., Pecero, J., Bouvry, P., Khan, S.: A two-phase heuristic for the energy-efficient scheduling of independent tasks on computational grids. Cluster Comput. 16(3), 421–433 (2013)
Quezada-Pina, A., Tchernykh, A., González-García, J.L., Hirales-Carbajal, A., Ramírez-Alcaraz, J.M., Schwiegelshohn, U., Yahyapour, R., Miranda-López, V.: Adaptive parallel job scheduling with resource admissible allocation on two-level hierarchical grids. Future Gener. Comput. Syst. 28(7), 965–976 (2012)
Ramírez-Alcaraz, J., Tchernykh, A., Yahyapour, R., Schwiegelshohn, U., Quezada-Pina, A., González-García, J., Hirales-Carbajal, A.: Job allocation strategies with user run time estimates for online scheduling in hierarchical grids. J. Grid Comput. 9(1), 95–116 (2011)
Rizvandi, N., Taheri, J., Zomaya, A.: Some observations on optimal frequency selection in DVFS-based energy consumption minimization. J. Parallel Distrib. Comput. 71(8), 1154–1164 (2011)
Taheri, J., Zomaya, A., Khan, S.: Grid Simulation Tools for Job Scheduling and Datafile Replication in Scalable Computing and Communications: Theory and Practice. Wiley, Hoboken (2013). Chap. 35, pp. 777–797
Tchernykh, A., Lozano, L., Bouvry, P., Pecero, J., Schwiegelshohn, U., Nesmachnow, S.: Energy-aware online scheduling: ensuring quality of service for iaas clouds. In: Proceedings of the International Conference on High Performance Computing Simulation, pp. 911–918 (2014)
Tchernykh, A., Lozano, L., Schwiegelshohn, U., Bouvry, P., Pecero, J., Nesmachnow, S.: Bi-objective online scheduling with quality of service for iaas clouds. In: Proceedings of the 3rd International Conference on Cloud Networking, pp. 307–312 (2014)
Tchernykh, A., Pecero, J.E., Barrondo, A., Schaeffer, E.: Adaptive energy efficient scheduling in peer-to-peer desktop grids. Future Gener. Comput. Syst. 36, 209–220 (2014)
Topcuouglu, H., Hariri, S., Wu, M.Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)
Valentini, G., Lassonde, W., Khan, S., Min-Allah, N., Madani, S., Li, J., Zhang, L., Wang, L., Ghani, N., Kolodziej, J., Li, H., Zomaya, A., Xu, C.Z., Balaji, P., Vishnu, A., Pinel, F., Pecero, J., Kliazovich, D., Bouvry, P.: An overview of energy efficiency techniques in cluster computing systems. Cluster Comput. 16(1), 3–15 (2013)
Zomaya, A., Khan, S.: Handbook on Data Centers. Springer, New York (2014)
Zomaya, A.Y., Lee, Y.C.: Energy Efficient Distributed Computing Systems. Wiley-IEEE Computer Society Press, New York (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Nesmachnow, S., Iturriaga, S., Dorronsoro, B., Tchernykh, A. (2016). Multiobjective Energy-Aware Workflow Scheduling in Distributed Datacenters. In: Gitler, I., Klapp, J. (eds) High Performance Computer Applications. ISUM 2015. Communications in Computer and Information Science, vol 595. Springer, Cham. https://doi.org/10.1007/978-3-319-32243-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-32243-8_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32242-1
Online ISBN: 978-3-319-32243-8
eBook Packages: Computer ScienceComputer Science (R0)