Abstract
This paper focuses on the load imbalance problem in System Wide Information Management (SWIM) task scheduling. In order to meet the quality requirements of users for task completion, we studied large-scale network information system task scheduling methods. Combined with the traditional ant colony optimization (ACO) algorithm, using the hardware performance quality index and load standard deviation function of SWIM resource nodes to update the pheromone, a SWIM ant colony task scheduling algorithm based on load balancing (ACTS-LB) is presented in this paper. The experimental simulation results show that the ACTS-LB algorithm performance is better than the traditional min-min algorithm, ACO algorithm and particle swarm optimization (PSO) algorithm. It not only reduces the task execution time and improves the utilization of system resources, but also can maintain SWIM in a more load balanced state.
Highlights
In 1997, EUROCONTROL proposed the System Wide Information Management (SWIM) concept to the Federal Aviation Administration [1]
We propose a SWIM ant colony task scheduling algorithm based on load balancing (ACTS-LB)
We proposed the SWIM ant colony optimization task scheduling algorithm based on load balancing (ACTS-LB)
Summary
In 1997, EUROCONTROL proposed the System Wide Information Management (SWIM) concept to the Federal Aviation Administration [1]. In [10], the authors proposed a new heuristic algorithm combined with the particle swarm optimization (PSO) algorithm It has the characteristics of strong optimization search ability, fast convergence speed and high solving quality, providing a new direction for solving task scheduling problems in a cloud computing environment. In [15], a cloud computing resource scheduling method based on a parallel genetic algorithm was proposed This method can reduce the overall execution time of scheduling tasks to a certain extent, but it falls into local solutions. Presented an optimized algorithm for task scheduling based on genetic simulated annealing algorithm in cloud computing and its implementation It can improve the global search ability and convergence speed. Proposed a task load balancing scheduling algorithm based on ant colony optimization (WLB-ACO). (2) It can ensure that SWIM has better load balancing performance; (3) It is of great significance to promote the SWIM application for civil aviation industry development
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