Advertisement

Process Scheduling Using Ant Colony Optimization Techniques

  • Bruno Rodrigues Nery
  • Rodrigo Fernandes de Mello
  • André Carlos Ponce de Leon Ferreira de Carvalho
  • Laurence Tianruo Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4330)

Abstract

The growing availability of low cost microprocessors and the evolution of computing networks have enabled the construction of sophisticated distributed systems. The computing capacity of these systems motivated the adoption of clusters to build high performance solutions. The improvement of the process scheduling over clusters originated several proposals of scheduling and load balancing algorithms. These proposals have motivated this work, which defines, evaluates and implements a new load balancing algorithm for heterogeneous capacity clusters. This algorithm, named Ant Scheduler, uses concepts of ant colonies for the development of optimization solutions. Experimental results obtained in the comparison of Ant Scheduler with other approaches investigated in the literature show its ability to minimize process mean response times, improving the performance.

Keywords

Schedule Algorithm Load Balance Parallel Application Load Information Load Balance Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Shivaratri, N.G., Krueger, P., Singhal, M.: Load distributing for locally distributed systems. IEEE Computer 25(12), 33–44 (1992)Google Scholar
  2. 2.
    Krueger, P., Livny, M.: The diverse objectives of distributed scheduling policies. In: Seventh Int. Conf. Distributed Computing Systems, pp. 242–249. IEEE CS Press, Los Alamitos (1987)Google Scholar
  3. 3.
    Zhou, S., Ferrari, D.: An experimental study of load balancing performance. Technical Report UCB/CSD 87/336, PROGRES Report N.o 86.8, Computer Science Division (EECS), Universidade da California, Berkeley, California 94720 (1987)Google Scholar
  4. 4.
    Theimer, M.M., Lantz, K.A.: Finding idle machines in a workstation-based distributed system. IEEE Transactions on Software Engineering 15(11), 1444–1458 (1989)CrossRefGoogle Scholar
  5. 5.
    Mello, R.F., Trevelin, L.C., Paiva, M.S., Yang, L.T.: Comparative analysis of the prototype and the simulator of a new load balancing algorithm for heterogeneous computing environments. International Journal of High Performance Computing and Networking, Interscience 1(1/2/3), 64–74 (2004)CrossRefGoogle Scholar
  6. 6.
    Senger, L.J., de Mello, R.F., Santana, M.J., Santana, R.H.C., Yang, L.T.: Improving scheduling decisions by using knowledge about parallel applications resource usage. In: Yang, L.T., Rana, O.F., Di Martino, B., Dongarra, J. (eds.) HPCC 2005. LNCS, vol. 3726, pp. 487–498. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B 26, 1–13 (1996)CrossRefGoogle Scholar
  8. 8.
    Shmygelska, A., Hoos, H.H.: An ant colony optimisation algorithm for the 2d and 3d hydrophobic polar protein folding problem. BMC Bioinformatics, 1–22 (2005)Google Scholar
  9. 9.
    Acan, A.: An external memory implementation in ant colony optimization. In: Proceedings of the 4th International Workshop on Ant Colony Optimization and Swarm Intelligence, Brussels, Belgium, pp. 73–82 (2004)Google Scholar
  10. 10.
    Feitelson, D.G., Jette, M.A.: Improved utilization and responsiveness with gang scheduling. In: Feitelson, D.G., Rudolph, L. (eds.) IPPS-WS 1997 and JSSPP 1997. LNCS, vol. 1291, pp. 238–261. Springer, Heidelberg (1997)Google Scholar
  11. 11.
    de Mello, R.F., Senger, L.J.: Model for simulation of heterogeneous high-performance computing environments. In: Daydé, M., Palma, J.M.L.M., Coutinho, Á.L.G.A., Pacitti, E., Lopes, J.C. (eds.) VECPAR 2006. LNCS, vol. 4395, pp. 107–119. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Hockney, R.W.: The Science of Computer Benchmarking. Soc. for Industrial & Applied Math. (1996)Google Scholar
  13. 13.
    Araújo, A.P.F., Santana, M.J., Santana, R.H.C., Souza, P.S.L.: DPWP: A new load balancing algorithm. In: ISAS 1999, Orlando, U.S.A. (1999)Google Scholar
  14. 14.
    Souza, P.S.L.: AMIGO: Uma Contribuição para a Convergência na Área de Escalonamento de Processos. PhD thesis, IFSC-USP (2000)Google Scholar
  15. 15.
    Santos, R.R.: Escalonamento de aplicações paralelas: Interface amigo-corba. Master’s thesis, Instituto de Ciências Matemátaicas e de Computação da Universidade de São Paulo (2001)Google Scholar
  16. 16.
    Shefler, W.C.: Statistics: Concepts and Applications. The Benjamin/Cummings (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bruno Rodrigues Nery
    • 1
  • Rodrigo Fernandes de Mello
    • 1
  • André Carlos Ponce de Leon Ferreira de Carvalho
    • 1
  • Laurence Tianruo Yang
    • 2
  1. 1.Instituto de Ciências Matemáticas e de ComputaçãoUniversidade de São PauloSão CarlosBrazil
  2. 2.St. Francis Xavier UniversityAntigonishCanada

Personalised recommendations