| Title |
Research on Task Scheduling Model of Ant Colony Optimization Cloud Computing Platform for Online Practical Customer-training Application |
| DOI |
https://doi.org/10.5573/IEIESPC.2024.13.3.243 |
| Keywords |
Online practical training; Cloud computing; Task scheduling; ACO optimization |
| Abstract |
With the continuous development of internet information technology, cloud-computing task-scheduling platform technology is gradually maturing. Cloud computing is profoundly changing every aspect of people's lives and providing many conveniences. With the application of cloud computing in more fields, more extensive applications and efficient task scheduling algorithms have become increasingly important. This research focuses on the problem of task-scheduling methods for cloud computing platforms in customer-oriented online training systems. Based on the optimization of the ant colony algorithm, an ant colony optimization (ACO) cloud-computing task-scheduling algorithm is proposed. The research results indicate that when the number of tasks is 300, the makespan value of the optimized ant colony cloud scheduling algorithm (OACC) is 340, that of the discrete firefly algorithm (DFA) is 350, that of multi-objective differential evolution (MODE) is 380, and that of improved group search optimization (IGSO) is 409. The overall performance of OACC was 20.3% higher than that of IGSO. OACC maintained a low and stable degree of imbalance (DI) in different task count tests. At a task volume of 300, the overall utility evaluation of the ACO cloud-computing task-scheduling algorithm was 146, which is 31.5% higher than ACO, 18.7% higher than TACO, and 8.1% higher than LB-AACO. The experimental results meet expectations and indicate that the OACC cloud-computing task-scheduling algorithm proposed in the study has high task-processing ability and efficiency and is capable of scheduling tasks on cloud computing platforms for customer-oriented online training systems. |