Network Routing Optimization Based on Machine Learning Using Graph Networks Robust against Topology Change
Network Routing Optimization Based on Machine Learning Using Graph Networks Robust against Topology Change
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DOI：
10.1109/icoin48656.2020.9016573
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发表时间：
2020-01
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影响因子：
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通讯作者：
Kaku Sawada;Daisuke Kotani;Y. Okabe
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文献类型：
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作者：
Kaku Sawada;Daisuke Kotani;Y. Okabe
为了更好的服务质量，使用软件定义网络（SDN）进行实时路由优化的需求越来越多。由于在中等规模或较大规模的网络中，很难找到任何QoS度量的最优路由问题，因此已经研究了使用元算法（如遗传算法（GA）和模拟退火（SA））的准优化，但它仍然不可能满足实时优化的要求。已经有一些尝试用机器学习来解决这个问题。通过预先学习模型，可以在网络运行期间在短时间内输出接近最优的解决方案。这种方法的公开问题是机器学习模型无法处理网络的拓扑变化。在本文中，我们创建了一个模型，这是鲁棒的拓扑结构的变化，使用图网络。将该模型应用于最大带宽利用率问题，遗传算法的求解精度达到61.0%，预测时间比遗传算法快150倍。
There is an increasing demand of real-time routing optimazation using Sotware Defined Networking (SDN) for better Quality of Service. Since the problem of finding the optimum routing for any QoS metric is hard to solve for a medium or larger size network, quasi-optimization using metaheuristics, such as Genetic Algorithm (GA) and Simulated Annealing (SA), have been investigated, but it is still impossible to satisfy the requirement of real-time optimization. There have been some attempts to solve this with machine learning. By learning a model beforehand, it is possible to output a near-optimal solution in a short time during network operation. The open problem with this approach is that machine learning models cannot deal with topology change of the network. In this paper, we create a model which is robust for topology change by using Graph Networks. Applying the proposed model to maximum bandwidth utilization, we have gotten the accuracy of about 61.0% for solution of GA, and the prediction time is 150 times faster than GA.