中圖分類(lèi)號(hào)： TP39 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.211725 中文引用格式： 劉燈明，荊俊峰，劉凱，等. 基于改進(jìn)蟻群算法的云計(jì)算資源分配策略研究[J].電子技術(shù)應(yīng)用，2022，48(5)：104-109. 英文引用格式： Liu Dengming，Jing Junfeng，Liu Kai，et al. Research on cloud computing resource allocation strategy based on improved ant colony algorithm[J]. Application of Electronic Technique，2022，48(5)：104-109.
Research on cloud computing resource allocation strategy based on improved ant colony algorithm
Liu Dengming，Jing Junfeng，Liu Kai，F(xiàn)ang Zhiqi
North China Institute of Computer Systems Engineering，Beijing 100083，China
Abstract： In actual projects, it is found that if the ant colony algorithm is directly applied to cloud computing resource allocation, there will often be load imbalances, resulting in low resource utilization. And at the same time, the task completion time is too long, and the number of algorithm iterations is too large. This situation will not only greatly reduce the efficiency of the cloud computing system, but also cause system instability. Therefore, this article has made a series of improvements to the ant colony algorithm,including: the introduction of pseudo-random proportional rules, global pheromone enhancement, the introduction of cross mutation operations,and integration of ant colony algorithm and genetic algorithm. And then MATLAB simulation experiments are carried out.The experimental results show that the task completion time of the improved algorithm is shorter, the number of algorithm iterations is less, and the load balancing effect is better. From this, it can be concluded that the ant colony algorithm is better. The improvement is effective.
Key words : ant colony algorithm；improvement；cloud computing；load balancing
