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A Dynamic Load Balancing Algorithm for Computational Grid using Ant Colony Optimization
Objective: To design and implement an algorithm for load balancing with convenient utilization of heterogeneous grid resources. Methods: In this paper, we introduce Ant based Dynamic Load Balancing Algorithm (ADLBA), a decentralized dynamic load balancing algorithm using Ant Colony Optimization (ACO), which selects the best resources to be allocated to the tasks considering economic cost, resources' capacity, and local load. Results: We used the Gridsim toolkit to evaluate the efficiency of ADLBA against the Randomized Algorithm (RA) with various number of tasks and resource allocation polices. Our study results show that ADLBA outperforms RA in terms of execution cost and total application execution time (makespan), and they also show that using time-shared allocation policy in the resources leads to better results in both algorithms. Conclusion: We found that ADLBA is suitable for grid users which aim to execute their applications quickly with lower cost.
Ant Colony Optimization (ACO), Grid Computing, Gridsim, Load Balancing, Makespan.
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