Open AccessMulti-dimensional QoS Cloud Computing Task Scheduling Strategy Based on Improved Ant Colony Algorithm
Author(s) -
Junwei Ge,
Dehua Yu,
Yiqiou Fang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012031
Subject(s) - cloudsim , computer science , cloud computing , ant colony optimization algorithms , load balancing (electrical power) , quality of service , distributed computing , scheduling (production processes) , algorithm , ant colony , fitness function , mathematical optimization , computer network , operating system , genetic algorithm , mathematics , machine learning , grid , geometry
This paper proposed a multi-dimensional QoS cloud computing task scheduling algorithm based on improved ant colony algorithm, considering QoS demand of users and load balancing of cloud platform comprehensively. First, this paper defines a QoS model composed of the completion time and execution cost of tasks, and defines the cloud platform load balancing constraint function. Secondly, in view of the shortcomings of ant colony algorithm such as slow convergence speed and easy to fall into local optimum, the pheromone update method and expected heuristic function are improved, and the pheromone strength is dynamically changed. Finally, the simulation is carried out in cloudsim and compared with the ACS algorithm and the MMAS algorithm. Experimental results show that the algorithm in this paper is better than these two algorithms in terms of user satisfaction and cloud platform load.