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  1. 1

    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. …”
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    Monograph
  2. 2

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

    Published 2011
    “…This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system. …”
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    Thesis
  3. 3

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

    Published 2011
    “…Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources.Stagnation in grid computing system may occur when all jobs require or are assigned to the same resources which lead to the resources having high workload or the time taken to process a job is high.This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system.The algorithm consists of three new mechanisms that organize the work of an ant colony i.e. initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism.The resource allocation problem is modeled as a graph that can be used by the ant to deliver its pheromone.This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid resource management element.The proposed EACO algorithm takes into consideration the capacity of resources and the characteristics of jobs in determining the best resource to process a job.EACO selects the resources based on the pheromone value on each resource which is recorded in a matrix form.The initial pheromone value of each resource for each job is calculated based on the estimated transmission time and execution time of a given job. …”
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    Monograph
  4. 4

    Optimization grid scheduling with priority base and bees algorithm by Ahmed, Mohammed Shihab

    Published 2014
    “…The main aim of this current research to propose an optimization of the initial scheduler for grid computing using the bees algorithm. Modern algorithms informed this research. …”
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    Thesis
  5. 5

    Benchmark simulator with dynamic environment for job scheduling in grid computing by Ku-Mahamud, Ku Ruhana

    Published 2014
    “…Job scheduling algorithm has a significant influence on grid computing performance. …”
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    Conference or Workshop Item
  6. 6

    Fuzzy C-Mean And Genetic Algorithms Based Scheduling For Independent Jobs In Computational Grid by Lorpunmanee, Siriluck, Md Sap, Mohd Noor, Abdullah, Abdul Hanan

    Published 2006
    “…In this paper, we combine Fuzzy C-Mean and Genetic Algorithms which are popular algorithms, the Grid can be used for scheduling. …”
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    Article
  7. 7

    COMPARATIVE PERFORMANCE OF SOME HYBRID HEURISTICS SCHEDULING ALGORITHMS BASED ON SLTR AND DEADLINE FOR COMPUTATIONAL GRID by AHMED ABBA, HARUNA

    Published 2013
    “…Grid scheduling is an essential element of a Computational Grid infrastructure. …”
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    Thesis
  8. 8

    A hybrid heuristic of variable neighbourhood descent and great deluge algorithm for efficient task scheduling in grid computing by Muhammed, Abdullah, Mohamed, Mohamad Afendee, Hasan, Sazlinah, Eng, Kailun

    Published 2019
    “…To achieve the ultimate success of global collaborative resource sharing in Grid computing, an effective and efficient Grid resource management system is necessary and it is only possible if its core component, the scheduler, can perform scheduling in an efficient manner. …”
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    Article
  9. 9

    Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing by Alobaedy, Mustafa Muwafak Theab

    Published 2015
    “…The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing environments. …”
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    Thesis
  10. 10

    Implementation of Grid Computing for Cryptosystem (RSA) by Abd Wahab, Mohamed Ferdaus

    Published 2005
    “…One objective ofthis project is to perform a small scale study in order to set up a grid computing that can support faster computation ofRSA Algorithm. …”
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    Final Year Project
  11. 11

    Load balancing using enhanced ant algorithm in grid computing by Abdul Nasir, Husna Jamal, Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2010
    “…Load balancing is one of the critical issues that must he considered in managing a grid computing environment.It is complicated due to the distributed and heterogeneous nature of the resources.An enhanced ant algorithm for load balancing in grid computing is proposed in this papcr.The proposed algorithm will determine the best resource to he allocated to the jobs based on job characteristics and resource capacity, and at the same time to balance the entire resources.The proposed algorithm focuses on local pheromone trail update and trail limit. …”
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    Conference or Workshop Item
  12. 12

    Ant colony optimization algorithm for dynamic scheduling of jobs in computational grid by Ku-Mahamud, Ku Ruhana, Ramli, Razamin, Yusof, Yuhanis, Mohamed Din, Aniza, Mahmuddin, Massudi

    Published 2012
    “…In computational grid, job scheduling is one of the main factors affecting grid computing performance. …”
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    Monograph
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    Meta-scheduler in Grid environment with multiple objectives by using genetic algorithm by Lorpunmanee, Siriluck, Sap, M.N.M, Abdullah, Abdul Hanan, Srinoy, Surat

    Published 2006
    “…Grid computing is the principle in utilizing and sharing large-scale resources of heterogeneous computing systems to solve the complex scientific problem. …”
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    Article
  16. 16

    Resource management in grid computing using enhanced ant colony optimization by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza, Abdul Nasir, Husna Jamal

    Published 2010
    “…Simulation approach has been used to test the performance of the algorithm. The credibility of the proposed algorithm is compared with other approaches and results produced showed that the algorithm can balance the load of the resources.…”
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    Conference or Workshop Item
  17. 17

    Incorporating the range-based method into GridSim for modeling task and resource heterogeneity by Eng, Kailun, Muhammed, Abdullah, Mohamed, Mohamad Afendee, Hasan, Sazlinah

    Published 2017
    “…As heterogeneity is one of the unique characteristics of Grid computing, which induces additional challenges in designing heuristic-based scheduling algorithms, the main concern when performing simulation experiments for evaluating the performance of scheduling algorithms is how to model and simulate different Grid scheduling scenarios or cases that capture the inherent nature of heterogeneity of Grid computing environment. …”
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    Article
  18. 18

    Max-average: an extended max-min scheduling algorithm for Grid computing environtment by Maipan-uku, J. Y., Muhammed, Abdullah, Abdullah, Azizol, Hussin, Masnida

    Published 2016
    “…Grid computing can also be referred as a computing platform for users to utilise the remote heterogeneous resources for solving their large scale jobs that require a huge amount of processing power or a huge data storage. …”
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    Article
  19. 19

    New heuristic function in ant colony system for job scheduling in grid computing by Ku-Mahamud, Ku Ruhana, Alobaedy, Mustafa Muwafak

    Published 2012
    “…Job scheduling is one of the main factors affecting grid computing performance. Job scheduling problem classified as an NP-hard problem.Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms.Ant colony system algorithm is a meta-heuristic algorithm which has the ability to solve different types of NP-hard problems.However, ant colony system algorithm has a deficiency in its heuristic function which affects the algorithm behavior in terms of finding the shortest connection between edges.This paper focuses on enhancing the heuristic function where information about recent ants’ discoveries will be taken into account.Experiments were conducted using a simulator with dynamic environment features to mimic the grid environment.Results show that the proposed enhanced algorithm produce better output in term of utilization and make span.…”
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    Conference or Workshop Item
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