Search Results - (( program e scheduling algorithm ) OR ( java application optimization algorithm ))

Refine Results
  1. 1

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
    “…A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
    Get full text
    Get full text
    Get full text
    Thesis
  2. 2

    Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2012
    “…This research proposes an enhancement of the ant colony optimization algorithm that caters for dynamic scheduling and load balancing in the grid computing system. …”
    Get full text
    Get full text
    Get full text
    Monograph
  3. 3

    Resource management in grid computing using ant colony optimization by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2011
    “…Resources with high pheromone value are selected to process the submitted jobs.Global pheromone update is performed after completion processing the jobs in order to reduce the pheromone value of resources.A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization.Experimental results show that EACO produced better grid resource management solution.…”
    Get full text
    Get full text
    Get full text
    Get full text
    Monograph
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…These results suggests that optimal algorithms may turn to be non-optimal when practically implemented, unlike USG which reveals far less scheduling overhead and hence could be practically implemented in real-world applications. …”
    Get full text
    Get full text
    Conference or Workshop Item
  9. 9
  10. 10

    Route Optimization System by Zulkifli, Abdul Hayy

    Published 2005
    “…After much research into the many algorithms available, and considering some, including Genetic Algorithm (GA), the author selected Dijkstra's Algorithm (DA). …”
    Get full text
    Get full text
    Final Year Project
  11. 11

    Flowshop scheduling using artificial bee colony (ABC) algorithm with varying onlooker bees approaches by Mohd Pauzi, Nur Fazlinda

    Published 2015
    “…The main objective of this research is to develop a computer program with capability of manipulating the onlooker bee approaches in ABC Algorithm for solving flowshop scheduling problem. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  12. 12

    Priority and dynamic quantum time algorithms for central processing unit scheduling by Mohammed, Maysoon A.

    Published 2018
    “…Central Processing Unit is scheduled using different types of scheduling algorithms. …”
    Get full text
    Get full text
    Thesis
  13. 13
  14. 14
  15. 15
  16. 16
  17. 17

    New completion time algorithms for sequence based scheduling in multiproduct batch processes using matrix by Shafeeq , A., M.I., Abdul Mutalib, Amminudin , K.A., Muhammad , A.

    Published 2008
    “…All these parameters directly or indirectly affect the makespan, i.e. completion time of a batch process. The scheduling approaches reported in past literatures mostly refer to the use of complex mathematical models for determining makespan. …”
    Get full text
    Get full text
    Article
  18. 18
  19. 19

    Examination timetable scheduling system (ETSS) / Fong Teng Heng by Fong, Teng Heng

    Published 2000
    “…The algorithm using can be referred to Graph colouring. …”
    Get full text
    Get full text
    Thesis
  20. 20