Husna Jamal Abdul Nasir

Ant Colony Algorithm for Job Scheduling in Grid Computing

Scheduling jobs to resources in grid computing 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. This will lead to resources having high workload and stagnation may occur if computational times of the processed jobs are high. This paper proposed an enhanced ant colony optimization algorithm for jobs and resources scheduling in grid computing. The proposed ant colony algorithm for job scheduling in the grid environment combines the techniques from Ant Colony System and Max – Min Ant System. The algorithm focuses on local pheromone trail update and the trail limit values. A matrix is used to record the status of the available resources. The agent concept is also integrated in this algorithm for the purpose of updating the grid resource table. Experimental results obtained showed that this is a promising ant colony algorithm for job scheduling in grid environment.

Grid Load Balancing Using Ant Colony Optimization

An enhanced ant colony optimization technique for jobs and resources scheduling in grid computing is proposed in this paper. The proposed technique combines the techniques from Ant Colony System and Max – Min Ant System and focused on local pheromone trail update and trail limit. The agent concept is also integrated in this proposed technique for the purpose of updating the grid resource table. This facilitates in scheduling jobs to available resources efficiently which will enable jobs to be processed in minimum time and also balance all the resource in grid system.

Ant Colony Optimization Approaches in Wireless Sensor Network: Performance Evaluation

Wireless Sensor Network (WSN) has been widely implemented in large sectors such as military, habitat, business, industrial, health and environment. WSN is part of a distributed system where elements such as routing, load balancing, energy efficiency, node localization, time synchronization, data aggregation and security need to be addressed to improve its efficiency, robustness, extendibility, applicability and reliability. Despite multiple approaches proposed to improve all these aspects, there is still room for improvement in order to enhance the capability of WSN in terms of routing and energy efficiency. Ant Colony Optimization (ACO) is one of the approaches used to extend WSN capabilities because its heuristic nature is very suitable with distributed and dynamic environments. This study covers the common WSN aspects and performance evaluation criteria in addition to the list of previous studies that have used ACO approaches in WSN.