Performance Evaluation of List Based Scheduling on Heterogeneous Systems

  • Hamid Arabnejad
  • Jorge G. Barbosa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7155)


This paper addresses the problem of evaluating the schedules produced by list based scheduling algorithms, with metaheuristic algorithms. Task scheduling in heterogeneous systems is a NP-problem, therefore several heuristic approaches were proposed to solve it. These heuristics are categorized into several classes, such as list based, clustering and task duplication scheduling. Here we consider the list scheduling approach. The objective of this study is to assess the solutions obtained by list based algorithms to verify the space of improvement that new heuristics can have considering the solutions obtained with metaheuritcs that are higher time complexity approaches. We concluded that for a low Communication to Computation Ratio (CCR) of 0.1, the schedules given by the list scheduling approach is in average close to metaheuristic solutions. And for CCRs up to 1 the solutions are below 11% worse than the metaheuristic solutions, showing that it may not be worth to use higher complexity approaches and that the space to improve is narrow.


Schedule Algorithm Tabu Search Direct Acyclic Graph Task Graph Metaheuristic 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cerny, V.: Thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm. Journal of Optimization Theory and Applications, 41–51 (1985)Google Scholar
  2. 2.
    Coffman, E.G.: Computer and job-shop scheduling theory. Wiley (1976)Google Scholar
  3. 3.
    Dhodhi, M.K., Ahmad, I., Yatama, A., et al.: An integrated technique for task matching and scheduling onto distributed heterogeneous computing system. Journal of Parallel and Distributed Computing 62(9), 1338–1361 (2002)zbMATHCrossRefGoogle Scholar
  4. 4.
    Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)CrossRefGoogle Scholar
  5. 5.
    Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artifitial Life 5, 137–172 (1999)CrossRefGoogle Scholar
  6. 6.
    El-Rewini, H., Lewis, T.G.: Scheduling parallel program tasks onto arbitrary target machines. Journal of Parallel and Distributed Computing 9(2 ), 138–153 (1990)CrossRefGoogle Scholar
  7. 7.
    Glover, F.: Tabu search-part i. ORSA Journal on Computing 1(3), 190–206 (1989)zbMATHCrossRefGoogle Scholar
  8. 8.
    Glover, F.: Tabu search-part ii. ORSA Journal on Computing 2(1), 4–32 (1990)zbMATHCrossRefGoogle Scholar
  9. 9.
    Kim, D., Yi, B.-G.: A two-pass scheduling algorithm for parallel programs. Parallel Computing 20, 869–885 (1994)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Kohler, W.H., Steiglitz, K.: Characterization and theoretical comparison of branch-and-bound algorithms for permutation problems. Journal of ACM 2, 140–156 (1974)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Kwok, Y., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Computing Surveys 31(4), 406–471 (1999)CrossRefGoogle Scholar
  13. 13.
    Kwok, Y.-K., Ahmad, I.: Dynamic critical-path scheduling: an effective technique for allocating task graphs to multiprocessors. IEEE Transactions on Parallel and Distributed Systems 7(5), 506–521 (1996)CrossRefGoogle Scholar
  14. 14.
    Liou, J.-C., Palis, M.A.: A comparison of general approaches to multiprocessor scheduling. In: International Parallel Processing Symposium, pp. 152–156 (1997)Google Scholar
  15. 15.
    Papadimitriou, C., Yannakakis, M.: Scheduling interval ordered tasks. SIAM Journal of Computing 5, 73–82 (1976)CrossRefGoogle Scholar
  16. 16.
    DAG Generation Program (2010),
  17. 17.
    Sinnen, O., Sousa, L.: List scheduling: extension for contention awareness and evaluation of node priorities for heterogeneous cluster architectures. Parallel Computing 30, 81–101 (2004)CrossRefGoogle Scholar
  18. 18.
    Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems 13(3), 260–274 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hamid Arabnejad
    • 1
  • Jorge G. Barbosa
    • 1
  1. 1.Faculdade de Engenharia, Dep. de Engenharia Informática, Laboratório de Intelegência Artificial e Ciência dos ComputadoresUniversidade do PortoPortoPortugal

Personalised recommendations