Recently, deep learning has been introduced to automate network management to reduce human costs. However, the amount of log data obtained from the large-scale network is huge, and conventional centralized deep learning faces communication and computation costs. This paper aims to reduce communication and computation costs by training deep learning models using federated learning on data generated in the network and to deploy deep learning models as soon as possible. In this scheme, data generated at each point in the network are transferred to servers in the network, and deep learning models are trained by federated learning among the servers. In this paper, we first reveal that the training time depends on the transfer routes and the destinations of data and model parameters. Then, we introduce a simultaneous optimization method for (1) to which servers each point transfers the data through which routes and (2) through which routes the servers transfer the parameters to others. In the experiments, we numerically and experimentally compared the proposed method and naive methods in complicated wired network environments. We show that the proposed method reduced the total training time by 34% to 79% compared with the naive methods.

The advent of the Internet of Things (IoT) has led to an exponential increase in data from devices usable for monitoring and managing various systems. Traditional central server data processing is becoming impractical due to the surge in IoT devices and data volume, resulting in high bandwidth and computational costs. In federated learning (FL), servers train models independently using their own data and transfer their models to a central aggregator where the models are aggregated. This process reduces the computational load and bandwidth usage inherent in centralized systems because data transfer can be reduced in FL. The study focuses on a scenario where base stations (BSs), each with a server, receive data from IoT devices, and BSs train models with their own data as participants of FL. Since the accuracy of the model is influenced by the data's amount and distribution across servers, which BS IoT devices transfer their data to is critical, while bandwidth limitations constrain that choice. The paper introduces a conditional optimization problem and solves the problem with a genetic algorithm to maximize FL model accuracy while adhering to bandwidth constraints. In the experiments, we compared the proposed optimization and naive method by numerical simulations and actual training deep learning models. As a result, the accuracies of all deep learning models improved when FL was performed based on the results obtained from our optimization compared to the naive method.

Vehicle communication services via mobile networks would further increase communication traffic volume. To prevent network congestion caused by the increase in communication traffic volume, network operators expand the number of base stations by predicting the maximum communication traffic volume. Although conventional methods for predicting communication traffic volume use previous mobile traffic data, vehicle communication services lack sufficient data due to limited usage. In this paper, we attempt to predict the maximum communication traffic volume without using mobile traffic data by relying on the maximum vehicle traffic density, which represents the number of vehicles per kilometer of road, although this is challenging since the vehicle traffic density is measured on only some roads. Meanwhile, road characteristics (e.g., the number of lanes) can be obtained for all roads. Therefore, we propose a method to estimate the maximum vehicle traffic density from road characteristics using a deep learning (DL) model to predict the maximum communication traffic for all roads. The proposed method uses the maximum vehicle traffic density or the parameters of the probability density function (PDF) of vehicle traffic density as labels. Training to estimate the parameters of the PDF allows us to calculate the maximum vehicle traffic density with a limited number of measurement days of data. Experimental results showed that the proposed method is more accurate than the baseline method in estimating the maximum vehicle traffic density.

Federated learning is a distributed machine learning technique that addresses the challenges of traditional centralized machine learning, such as high computational expenses and network congestion. In federated learning, a model is distributed to each server from a central server, and then each server trains the model using its own data. After training, the models are integrated at the central server, and the integrated model is re-distributed to the servers for further training. Federated learning can train a model based on all the data without collecting the data in one place, reducing the burden on the network and addressing the problem of training costs for extensive amounts of data. However, federated learning has two unique challenges: prolonged training time depending on the amount of data in each server, the processing capacity of each server, and the bandwidth of links in a network, and the accuracy of the trained model, which is influenced by the heterogeneity of the datasets in each server. We propose an optimization problem for the training time and the convergence behavior in federated learning. Specifically, we present a nonlinear programming problem to minimize the total training time while optimizing the data transfer route and destination of each router, subject to constraints on network congestion avoidance and convergence of the trained model. We evaluate the proposed method in an experiment using a virtual GPU cluster and show that the proposed method improves both the accuracy of the trained models and the training time compared to a prior method.

Federated learning, which can distributedly and efficiently train a deep learning model, attracts much attention since a large amount of data generated on a wide network can be utilized. When data generated in a network are collected, how much data is collected on which servers affects the model training time. However, the bandwidths of the links constrain the amount of data collected at each server and transfer routes of data. In this paper, we propose the optimization formula for data destinations and transfer routes in each generating data site under the constraint of the bandwidth of links in a network to minimize the time consumed by training a model. In the experiments, first, we numerically compared the amount of data collected in each server between the proposed algorithm and a naive method. The experiment shows that the proposed method could conduct the federated learning using all data generated in a network even when the bandwidths of links were less than one-third compared to the naive method. Then, we compared the training time of a deep learning model on the basis of the amount of data calculated in the former numerical experiments. We exhibit that the proposed algorithm reduced the training time by 27% to 47% compared to the naive method.

Machine learning (ML) has been used for various tasks in network operations in recent years. However, since the scale of networks has grown and the amount of data generated has increased, it has been increasingly difficult for network operators to conduct their tasks with a single server using ML. Thus, ML with edge-cloud cooperation has been attracting attention for efficiently processing and analyzing a large amount of data. In the edge-cloud cooperation setting, although transmission latency, bandwidth congestion, and accuracy of tasks using ML depend on the load balance of processing data with edge servers and a cloud server in edge-cloud cooperation, the relationship is too complex to estimate. In this paper, we focus on monitoring anomalous traffic as an example of ML tasks for network operations and formulate transmission latency, bandwidth congestion, and the accuracy of the task with edge-cloud cooperation considering the ratio of the amount of data preprocessed in edge servers to that in a cloud server. Moreover, we formulate an optimization problem under constraints for transmission latency and bandwidth congestion to select the proper ratio by using our formulation. By solving our optimization problem, the optimal load balance between edge servers and a cloud server can be selected, and the accuracy of anomalous traffic monitoring can be estimated. Our formulation and optimization framework can be used for other ML tasks by considering the generating distribution of data and the type of an ML model. In accordance with our formulation, we simulated the optimal load balance of edge-cloud cooperation in a topology that mimicked a Japanese network and conducted an anomalous traffic detection experiment by using real traffic data to compare the estimated accuracy based on our formulation and the actual accuracy based on the experiment.

Health monitoring is important for maintaining reliable information and communications technology (ICT) systems. Anomaly detection methods based on machine learning, which train a model for describing "normality" are promising for monitoring the state of ICT systems. However, these methods cannot be used when the type of monitored log data changes from that of training data due to the replacement of certain equipment. Therefore, such methods may dismiss an anomaly that appears when log data changes. To solve this problem, we propose an ICT-systems-monitoring method with deep learning models divided based on the correlation of log data. We also propose an algorithm for extracting the correlations of log data from a deep learning model and separating log data based on the correlation. When some of the log data changes, our method can continue health monitoring with the divided models which are not affected by changes in the log data. We present the results from experiments involving benchmark data and real log data, which indicate that our method using divided models does not decrease anomaly detection accuracy and a model for anomaly detection can be divided to continue monitoring a network state even if some the log data change.