SI-Based Scheduling of Parameter Sweep Experiments on Federated Clouds

  • Elina Pacini
  • Cristian Mateos
  • Carlos García Garino
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 485)

Abstract

Scientists and engineers often require huge amounts of computing power to execute their experiments. This work focuses on the federated Cloud model, where custom virtual machines (VM) are launched in appropriate hosts belonging to different providers to execute scientific experiments and minimize response time. Here, scheduling is performed at three levels. First, at the broker level, datacenters are selected by their network latencies via three policies –Lowest-Latency-Time-First, First-Latency-Time-First, and Latency-Time-In-Round–. Second, at the infrastructure level, two Cloud VM schedulers based on Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) for mapping VMs to appropriate datacenter hosts are implemented. Finally, at the VM level, jobs are assigned for execution into the preallocated VMs. Simulated experiments show that the combination of policies at the broker level with ACO and PSO succeed in reducing the response time compared to using the broker level policies combined with Genetic Algorithms.

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References

  1. 1.
    Agostinho, L., Feliciano, G., Olivi, L., Cardozo, E., Guimaraes, E.: A Bio-inspired approach to provisioning of virtual resources in federated Clouds. In: Ninth International Conference on Dependable, Autonomic and Secure Computing (DASC), DASC 2011, December 12-14, pp. 598–604. IEEE (2011)Google Scholar
  2. 2.
    Calheiros, R., Ranjan, R., Beloglazov, A., De Rose, C., Buyya, R.: Cloudsim: A toolkit for modeling and simulation of Cloud Computing environments and evaluation of resource provisioning algorithms. Software: Practice & Experience 41(1), 23–50 (2011)Google Scholar
  3. 3.
    Celesti, A., Fazio, M., Villari, M., Puliafito, A.: Virtual machine provisioning through satellite communications in federated Cloud environments. Future Generation Computer Systems 28(1), 85–93 (2012)CrossRefGoogle Scholar
  4. 4.
    de Oliveira, G., Ribeiro, E., Ferreira, D., Araújo, A., Holanda, M., Walter, M.: ACOsched: a scheduling algorithm in a federated Cloud infrastructure for bioinformatics applications. In: International Conference on Bioinformatics and Biomedicine, pp. 8–14. IEEE (2013)Google Scholar
  5. 5.
    Gahlawat, M., Sharma, P.: Survey of virtual machine placement in federated Clouds. In: International Advance Computing Conference (IACC), pp. 735–738. IEEE (2014)Google Scholar
  6. 6.
    García Garino, C., Ribero Vairo, M., Andía Fagés, S., Mirasso, A., Ponthot, J.P.: Numerical simulation of finite strain viscoplastic problems. Journal of Computational and Applied Mathematics 246, 174–184 (2013)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    García Garino, C., Gabaldón, F., Goicolea, J.M.: Finite element simulation of the simple tension test in metals. Finite Elements in Analysis and Design 42(13), 1187–1197 (2006)CrossRefGoogle Scholar
  8. 8.
    Jung, J., Jung, S., Kim, T., Chung, T.: A study on the Cloud simulation with a network topology generator. World Academy of Science, Engineering & Technology 6, 303–306 (2012)Google Scholar
  9. 9.
    Kennedy, J.: Swarm Intelligence. In: Zomaya, A. (ed.) Handbook of Nature-Inspired and Innovative Computing, pp. 187–219. Springer, US (2006)CrossRefGoogle Scholar
  10. 10.
    Lucas-Simarro, J., Moreno-Vozmediano, R., Montero, R., Llorente, I.: Scheduling strategies for optimal service deployment across multiple clouds. Future Generation Computer Systems 29(6), 1431–1441 (2013)Google Scholar
  11. 11.
    Ludwig, S., Moallem, A.: Swarm Intelligence approaches for Grid load balancing. Journal of Grid Computing 9(3), 279–301 (2011)CrossRefGoogle Scholar
  12. 12.
    Malik, S., Huet, F., Caromel, D.: Latency based group discovery algorithm for network aware Cloud scheduling. Future Generation Computer Systems 31, 28–39 (2014)CrossRefGoogle Scholar
  13. 13.
    Mateos, C., Pacini, E., García Garino, C.: An ACO-inspired algorithm for minimizing weighted flowtime in Cloud-based parameter sweep experiments. Advances in Engineering Software 56, 38–50 (2013)CrossRefGoogle Scholar
  14. 14.
    Mauch, V., Kunze, M., Hillenbrand, M.: High performance cloud computing. Future Generation Computer Systems 29(6), 1408–1416 (2013)Google Scholar
  15. 15.
    Moreno Vozmediano, R., Montero, R., Llorente, I.: IaaS Cloud architecture: FromvVirtualized datacenters to federated Cloud infrastructures. IEEE Computer 45(12), 65–72 (2012)CrossRefGoogle Scholar
  16. 16.
    Pacini, E., Mateos, C., García Garino, C.: Dynamic scheduling of scientific experiments on Clouds using Ant Colony Optimization. In: Topping, B.H.V., Iványi, P. (eds.) Proceedings of the Third International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering, paper 33. Civil-Comp Press, Stirlingshire (2013)Google Scholar
  17. 17.
    Pacini, E., Mateos, C., García Garino, C.: Distributed job scheduling based on Swarm Intelligence: A survey. Computers & Electrical Engineering 40(1), 252–269 (2014), 40th-year commemorative issueGoogle Scholar
  18. 18.
    Pacini, E., Mateos, C., García Garino, C.: Multi-objective Swarm Intelligence schedulers for online scientific Clouds. Computing. Special Issue on Cloud Computing, 1–28 (2014)Google Scholar
  19. 19.
    Somasundaram, T., Govindarajan, K.: CLOUDRB: A framework for scheduling and managing High-Performance Computing (HPC) applications in science Cloud. Future Generation Computer Systems 34, 47–65 (2014)CrossRefGoogle Scholar
  20. 20.
    Tordsson, J., Montero, R., Moreno Vozmediano, R., Llorente, I.: Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Generation Computer Systems 28(2), 358–367 (2012)CrossRefGoogle Scholar
  21. 21.
    Woeginger, G.: Exact Algorithms for NP-Hard Problems: A Survey. In: Jünger, M., Reinelt, G., Rinaldi, G. (eds.) Combinatorial Optimization - Eureka, You Shrink! LNCS, vol. 2570, pp. 185–207. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Elina Pacini
    • 1
    • 3
  • Cristian Mateos
    • 2
    • 3
  • Carlos García Garino
    • 1
  1. 1.ITIC - UNCuyo University, MendozaMendozaArgentina
  2. 2.ISISTAN - UNICEN UniversityTandil, Buenos AiresArgentina
  3. 3.Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)Argentina

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