College of Artificial Intelligence and Big Data, Chongqing Industry Polytechnic College, Chongqing, China.
Comput Intell Neurosci. 2022 Apr 12;2022:3120883. doi: 10.1155/2022/3120883. eCollection 2022.
The networking scale and traffic have exploded. At the same time, the rapid development of virtualization and cloud computing technologies not only poses a considerable challenge to the endurance of the network, but also causes more and more problems to the traditional network architecture with IP as the core. Cloud computing is a supercomputing model based on the Internet. With the rapid growth of network access and data traffic, the processing power and computing intensity will also increase, and a single server cannot afford the increase in business. In order to reduce network pressure and improve computing efficiency, load balancing for network computing is particularly important. This paper uses ant colony algorithm to design cloud computing load balance. The ant colony algorithm runs in the controller. According to the real-time network load situation provided by the controller, it calculates the link with the smallest load and provides a dynamic data stream forwarding strategy. The result of the experiments shows that the load-balanced ACO optimized technique can significantly provide an improved computational response. In the ACO algorithm, the average response time is about 30% lower than that in other algorithms. This shows that the use of the ant colony algorithm achieves a good optimization effect.
网络规模和流量呈爆炸式增长。与此同时，虚拟化和云计算技术的快速发展不仅对网络的承受能力构成了相当大的挑战，而且对以 IP 为核心的传统网络架构也造成了越来越多的问题。云计算是一种基于互联网的超级计算模式。随着网络接入和数据流量的快速增长，处理能力和计算强度也将增加，单个服务器无法承受业务量的增加。为了降低网络压力和提高计算效率，网络计算的负载均衡尤为重要。本文采用蚁群算法设计云计算负载均衡。蚁群算法在控制器中运行。根据控制器提供的实时网络负载情况，计算负载最小的链路，并提供动态数据流转发策略。实验结果表明，负载均衡的 ACO 优化技术可以显著提高计算响应。在 ACO 算法中，平均响应时间比其他算法低约 30%。这表明，蚁群算法的使用达到了很好的优化效果。
