一种机器学习辅助数据中心传输资源分配优化策略

Optimization strategy of transmission resource allocation in data center assisted by machine learning

针对当前海量数据传输在数据中心光网络应用场景下的优化需求,提出在数据中心流量发送前使用机器学习方法对所要发送的流量类型进行分类,将一种改进的贪婪遗传算法(IGGA)引入数据中心光网络重构问题并进行优化。仿真结果表明:在相同节点分布的情况下,与传统遗传算法(GA)所构建的拓扑结构链路相比,IGGA平均长度明显减小,在20节点和50节点拓扑情况下分别减少了3.06%和6.37%,且改进效果随拓扑节点数量增加而提高。

Aiming at the optimization requirements of massive data transmission in data center optical network application scenarios,this paper proposes to use machine learning method to classify the traffic types to be sent before sending data center traffic,and introduces an improved greedy genetic algorithm(IGGA)into data center optical network reconstruction and optimization.The simulation results show that under the same node distribution,the average length of the improved IGGA is significantly reduced compared with the traditional genetic algorithm(GA),and the average length of the improved IGGA is reduced by 3.06%and 6.37%respectively in the case of 20 nodes and 50 nodes topology,and the improvement effect improves with the number of topology nodes increases.