摘要
认知网络能够提高网络端到端的性能,确保服务质量(QoS)要求。而目前普遍使用的路由算法不具备网络认知能力。针对这一问题,提出一种具有认知能力的负载均衡多路径路由算法,该算法结合了Q学习算法和蚁群算法各自的优点,通过蚁群算法完成路径的建立和维护,Q学习算法实现拥塞规避和负载均衡。使用OPNET仿真比较,表明该算法在时延、带宽利用方面均具有较好的性能。
Cognitive network can improve the end-to-end performance of the network, and ensure QoS （Quality of Service） requirements. The existing routing algorithm does not have cognitive ability. To solve this problem, a multi-path routing algorithm of cognitiveload balancing was proposed, which combined the advantages of Q-learning algorithm and ant algorithm, to establish and maintain the route through ant algorithm, and to achieve congestion avoidance and load balancing by Q-learning algorithm. The simulation contrast with OPNET shows that the algorithm is valid and effective at controlling packet loss ratio, delay and bandwidth utilization.
出处
《计算机应用》
CSCD
北大核心
2013年第10期2750-2752,共3页
journal of Computer Applications
基金
甘肃省高等学校研究生导师科研项目(1215-04)
甘肃联合大学科研能力提升计划骨干项目(2012GGTS01)
关键词
多路径路由
认知网络
Q学习算法
蚁群算法
拥塞避免
multiple-path routing
cognitive networks
Q-learning algorithm
ant colony algorithm
congestion avoidance
