Genetic Ant Colony Algorithm and Its Design and Research in Cloud Computing Platform Resource Scheduling

Abstract

In order to solve the problems of slow convergence speed and low efficiency in finding precise solutions in existing cloud computing resource scheduling algorithms, the author proposes a genetic ant colony algorithm and its design and research in cloud computing platform resource scheduling. The author introduces a hybrid algorithm that integrates genetic algorithms with ant colony optimization. This approach begins by encoding parameters and seeks the best combination through evolutionary processes. It effectively merges the ant colony algorithm’s feedback mechanism with the genetic algorithm’s global search capabilities and rapid convergence. Then, multi-dimensional QoS constraints are proposed according to the needs of different users to perform local and global updates of pheromones. Finally, comparative simulation experiments were conducted on the cloud simulation platform CloudSim with simulated annealing algorithm (SA) and basic ant colony algorithm (ACO). The experimental results show that GAACO has a better time cost than ACO, but the time cost is longer than SA, and as the number of tasks increases, the time gap becomes larger. Compared with ACO, the time is reduced by 50.8%, and compared with SA, the time difference is 4%. Therefore, in terms of time cost, this algorithm is better than ACO. The algorithm proposed by the author effectively shortens the completion time of task scheduling, reduces operating costs, and has superior comprehensive performance.