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Energy Aware Multiobjective Scheduling in a Federation of Heterogeneous Datacenters

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High Performance Computing (CARLA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 796))

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Abstract

Energy efficiency is key for datacenters, however nowadays datacenters are far from being energy efficient. This article proposes a multiobjective evolutionary approach for energy aware scheduling in a federation of heterogeneous datacenters. The proposed algorithm schedules workflows of tasks aiming at optimizing infrastructure usage, quality of service and energy consumption. We perform an extensive experimental evaluation with 100 problem instances, considering a diverse set of workflows and different size of scenarios. Results show the proposed approach is able to compute accurate schedules, outperforming traditional heuristic schedulers such as round robin or load balancing algorithm.

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References

  1. Ahmad, I., Ranka, S.: Handbook of Energy-Aware and Green Computing. Chapman & Hall/CRC, Boca Raton (2012)

    Google Scholar 

  2. Chen, S., Li, Z., Yang, B., Rudolph, G.: Quantum-inspired hyper-heuristics for energy-aware scheduling on heterogeneous computing systems. IEEE Trans. Parallel Distrib. Syst. 27(6), 1796–1810 (2016)

    Article  Google Scholar 

  3. de Assuncao, M., di Costanzo, A., Buyya, R.: Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters, pp. 141–150 (2009)

    Google Scholar 

  4. Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  5. Dorronsoro, B., Nesmachnow, S., Taheri, J., Zomaya, A.Y., Talbi, E.-G., Bouvry, P.: A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems. Sustain. Comput.: Inform. Syst. 4(4), 252–261 (2014)

    Google Scholar 

  6. Durillo, J., Nebro, A.: jMetal: a Java framework for multi-objective optimization. Adv. Eng. Softw. 42, 760–771 (2011)

    Article  Google Scholar 

  7. Garg, R., Kumar Singh, A.: Energy-aware workflow scheduling in grid under QoS constraints. Arab. J. Sci. Eng. 41(2), 495–511 (2016)

    Article  Google Scholar 

  8. Iturriaga, S., Dorronsoro, B., Nesmachnow, S.: Multiobjective evolutionary algorithms for energy and service level scheduling in a federation of distributed datacenters. Int. Trans. Oper. Res. 24(1–2), 199–228 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  9. Iturriaga, S., Nesmachnow, S.: Multiobjective scheduling of green-powered datacenters considering QoS and budget objectives. In: IEEE Innovative Smart Grid Technologies Latin America, pp. 570–573 (2015)

    Google Scholar 

  10. Iturriaga, S., Nesmachnow, S., Dorronsoro, B., Bouvry, P.: Energy efficient scheduling in heterogeneous systems with a parallel multiobjective local search. Comput. Inform. J. 32(2), 273–294 (2013)

    MathSciNet  Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. Kim, K., Buyya, R., Kim, J.: Power aware scheduling of bag-of-tasks applications with deadline constraints on DVS-enabled clusters. In: 7th IEEE International Symposium on Cluster Computing and the Grid, pp. 541–548 (2007)

    Google Scholar 

  13. Lee, Y., Zomaya, A.: Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans. Parallel Distrib. Syst. 22, 1374–1381 (2011)

    Article  Google Scholar 

  14. Li, Y., Liu, Y., Qian, D.: A heuristic energy-aware scheduling algorithm for heterogeneous clusters. In: 15th International Conference on Parallel and Distributed Systems, pp. 407–413 (2009)

    Google Scholar 

  15. 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. Supercomput. 59(1), 323–360 (2012)

    Article  Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. Moon, H., Chi, Y., Hacigümüş, H.: Performance evaluation of scheduling algorithms for database services with soft and hard SLAs. In: 2nd International Workshop on Data Intensive Computing in the Clouds, pp. 81–90 (2011)

    Google Scholar 

  18. Nesmachnow, S.: An overview of metaheuristics: accurate and efficient methods for optimisation. Int. J. Metaheuristics 3(4), 320–347 (2014)

    Article  Google Scholar 

  19. Nesmachnow, S., Dorronsoro, B., Pecero, J.E., Bouvry, P.: Energy-aware scheduling on multicore heterogeneous grid computing systems. J. Grid Comput. 11(4), 653–680 (2013)

    Article  Google Scholar 

  20. Nesmachnow, S., Perfumo, C., Goiri, Í.: Holistic multiobjective planning of datacenters powered by renewable energy. Cluster Comput. 18(4), 1379–1397 (2015)

    Article  Google Scholar 

  21. 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)

    Google Scholar 

  22. 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)

    Article  Google Scholar 

  23. Ren, Z., Zhang, X., Shi, W.: Resource scheduling in data-centric systems. In: Khan, S.U., Zomaya, A.Y. (eds.) Handbook on Data Centers, pp. 1307–1330. Springer, New York (2015). https://doi.org/10.1007/978-1-4939-2092-1_46

    Google Scholar 

  24. Taheri, J., Zomaya, A., Khan, S.: Grid simulation tools for job scheduling and datafile replication. In: Scalable Computing and Communications: Theory and Practice (Chap. 35), pp. 777–797. Wiley, Hoboken (2013)

    Google Scholar 

  25. Tang, Z., Qi, L., Cheng, Z., Li, K., Khan, S., Li, K.: An energy-efficient task scheduling algorithm in DVFS-enabled cloud environment. J. Grid Comput. 14, 55–74 (2016)

    Article  Google Scholar 

  26. Wang, Y.-R., Huang, K.-C., Wang, F.-J.: Scheduling online mixed-parallel workflows of rigid tasks in heterogeneous multi-cluster environments. Future Gener. Comput. Syst. 60, 35–47 (2016)

    Article  Google Scholar 

  27. Zhu, Z., Zhang, G., Li, M., Liu, X.: Evolutionary multi-objective workflow scheduling in cloud. IEEE Trans. Parallel Distrib. Syst. 27(5), 1344–1357 (2016)

    Article  Google Scholar 

  28. Zomaya, A., Khan, S.: Handbook on Data Centers. Springer, New York (2014). https://doi.org/10.1007/978-1-4939-2092-1

    Google Scholar 

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Authors and Affiliations

  1. Universidad de la República, Montevideo, Uruguay

    Santiago Iturriaga & Sergio Nesmachnow

Authors
  1. Santiago Iturriaga
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  2. Sergio Nesmachnow
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Corresponding author

Correspondence to Santiago Iturriaga .

Editor information

Editors and Affiliations

  1. CSC-CONICET and Universidad de Buenos Aires, Buenos Aires, Argentina

    Esteban Mocskos

  2. Universidad de la República, Montevideo, Uruguay

    Sergio Nesmachnow

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Iturriaga, S., Nesmachnow, S. (2018). Energy Aware Multiobjective Scheduling in a Federation of Heterogeneous Datacenters. In: Mocskos, E., Nesmachnow, S. (eds) High Performance Computing. CARLA 2017. Communications in Computer and Information Science, vol 796. Springer, Cham. https://doi.org/10.1007/978-3-319-73353-1_24

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  • DOI: https://doi.org/10.1007/978-3-319-73353-1_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73352-4

  • Online ISBN: 978-3-319-73353-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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