Towards Automating IP-Network Operations with Machine Learning from Raw Network Data

Abstract

The ever-increasing size and complexity of communication networks today complicate Network Operation Centers (NOC) to function efficiently in manually operated tasks such as network status detection, network fault localization, cost-aware traffic engineering, failure management, and network quality assurance. These tasks have traditionally been managed by expert technicians who make decisions on when and where to take which actions based on specific network rules. Due to the complexity of the process, NOC actions are still performed manually. However, automating this process could be a valuable input for network providers and service operators. In this context, we developed an Artificial Intelligence based (AI-based) action recommendation engine (ARE) which, as its name suggests, recommends the best available operational expenditure aware (OPEX-aware) action, either with (Stateful ARE) or without (Stateless ARE) measuring the network state. Our experimental results show that Stateful ARE can recommend the suitable action and yield up to 99% accuracy. This high accuracy percentage is due to the correct classification of the Normal state, which represents 64.5% of the dataset, and its corresponding action of Do Nothing, which accounts for 68.3% of all actions While Stateful ARE’s overall accuracy is satisfactory, it was unable to achieve this performance in minority classes, and it suffered from performance degradation due to state classification process. Therefore, we introduced Stateless ARE, which recommends actions without measuring the network state. The initial results of Stateless ARE using a Feed Forward Neural Network (FFNN) did not exceed Stateful ARE’s performance. The classification accuracy of minority classes were still around 89% and 93%, but it outperformed the static network, indicating that it could be improved with further optimization techniques. Based on this insight, we introduced state-of-the-art Transformer model as Stateless ARE model. The transformer model significantly improved the accuracy of the minority classes to 97% and 99%, which other methodologies struggled to classify. This result shows that the transformer model can be an effective tool in improving the performance of action recommendation engines.