The ever-increasing diversity of devices and the complexity of communication content pose significant challenges for network traffic analysis. These limitations can hinder anomaly detection and network control, both essential for guaranteeing stable communication in next-generation networks. This paper proposes an approach leveraging Latent Dirichlet Allocation (LDA) topic modeling for network traffic analysis. LDA, a powerful machine learning technique, excels at identifying hidden thematic structures within data. When applied to network traffic, LDA can differentiate traffic types, such as web browsing, email, or file sharing. To optimize the effectiveness of traffic demand capture, we conducted experiments to determine the appropriate window size for individual connections and the ideal number of topics needed for accurate traffic analysis. Our proposed topic estimation algorithm, utilizing the bag-of-words representation, demonstrates its efficacy in identifying multiple topics within real-world network traffic datasets. This proposed approach has the potential to significantly improve network performance, security, and management.

This study focuses on cooperative beamforming among base stations in wireless communication technology and proposes a new approach based on the Free Energy Principle (FEP). Traditionally, the trade-off between the accuracy of channel information acquisition and overhead has posed a challenge in beamforming. FEP addresses this trade-off by balancing the value from observation and the value from action to select the optimal behavior. This enables adaptive responses to dynamic environmental fluctuations through integrated search, reasoning, and learning. Additionally, by introducing a hierarchical coordination structure, information sharing among base stations and indirect state sharing among agents are achieved, enhancing the efficiency and stability of beamforming. Furthermore, this study utilizes Integrated Sensing and Communication (ISAC) to perform simultaneous communication and real-world sensing. By integrating feedback from terminals and terminal location information for channel state estimation, the overhead is reduced. Simulation results demonstrate that the proposed method effectively improves the Signal to Interference and Noise Ratio (SINR) and energy efficiency.

Network slicing technology is required to dynamically provide virtual networks in response to user requirements with a wide variety of services operating on the network. Generally, optimal allocation of virtual networks to resources on the real network is a combinatorial optimization problem, and it is difficult to find an exact solution in realistic time in the current large-scale and complex networks. In addition, user requirements change dynamically, and therefore, optimization methods that can cope with such temporal variations in the situation are required. In this paper, we propose a method to solve a virtual network embedding problem using quality-diversity (QD) algorithms, especially the MAP-Elites algorithm, and evaluate its effectiveness through computer simulations.

Beamforming technology using massive MIMO in the millimeter wave (mmWave) band is attracting attention as a fundamental technology for next-generation wireless communication systems. Beamforming increases the signal-to-noise ratio (SNR) of signals received by terminals and enables high-speed communications. In beamforming, it is necessary to search for a beam with appropriate directivity from a predefined codebook and irradiate the beam toward the terminal. Although hierarchical codebooks can be used to reduce the search overhead, conventional beam training methods in hierarchical codebooks are not suitable under conditions where channel conditions change over time. This is because each time the beam is re-searched, a non-optimal beam is applied, and the SNR is repeatedly degraded temporarily and significantly. To solve this problem, this paper proposes a method to predict the optimal beam using active inference. This method avoids the problem of temporarily degrading SNR by predicting the optimal beam without searching for it. As a result, the method using active inference can increase the average SNR compared to the conventional beam training method in hierarchical codebooks.

In 5G, flexible resource management, mainly by base stations, will enable support for a variety of use cases. However, in a situation where a large number of devices exist, such as in mMTC, devices need to allocate resources appropriately in an autonomous decentralized manner. In this paper, autonomous decentralized timeslot allocation is achieved by using a decision model for each device. As a decision model, we propose an extension of the Bayesian Attractor Model (BAM) using Bayesian estimation. The proposed model incorporates a feature of human decision-making called magnitude sensitivity, where the time to decision varies with the sum of the values of all alternatives. This allows the natural introduction of the behavior of making a decision quickly when a time slot is available and waiting otherwise. Simulation-based evaluations show that the proposed method can avoid time slot conflicts during congestion more effectively than conventional Q-learning based time slot selection.

In recent years, with the spread of video streaming services and remote web conferencing systems, the increase in the number of end hosts connected to the Internet, the improvement of end host performance, and the diversification and sophistication of applications, the demand for communication quality of service (QoS) from users and service providers has become even higher. However, the amount of traffic flowing through the network is increasing year by year, and it has become difficult to operate a system that guarantees a certain level of QoS for users. In other words, best-effort type network systems have become the mainstream. Not only QoS, but also the user's own quality of experience (QoE) has become important, and application level control to improve user QoE within limited communication resources has become very important for both users and service providers. Since QoE is a subjective measure of a user's perception of a service, it is considered to be affected by cognitive biases that are observed in human subjective decision-making. In this paper, we conduct an experiment focusing on the choice-supportive bias, which is one of the cognitive biases, and clarify the effect of this cognitive bias on users during video viewing.

In 5G, the network is divided into slices to provide communications with different characteristics, such as low latency and reliable communications (URRLC), multiple connections (MTC), and high speed and high capacity communications (eMBB), for different applications. Although the selection of network slices is often static, in practice, dynamic slice selection is required depending on the application situation. However, there are issues such as the slice change itself changing the application situation and the delay associated with the slice change. In this paper, we realize dynamic slice selection by recognizing the rough situation and the mapping between the recognized situation and the slice. The Bayesian Attractor Model (BAM) is used for recognition to achieve consistent recognition and is extended to the Dirichlet Process Mixture Model (DPMM) to achieve automatic attractor construction. The mapping between situations and slices is also automatically learned by using feedback. As an application of dynamic slice selection, we also show slice selection based on the video streaming situation. Through numerical examples, we show that our method can keep the quality of video streaming high while reducing slice changes.

Network virtualization is expected to handle various forms of network traffic induced by Internet of Things applications and other Internet-based services. Because traffic patterns change with time, virtual networks should be dynamically reconstructed to accommodate increasing traffic and to free unused resources. However, collecting all traffic information is difficult when applications are deployed on a wide-area network. It is therefore necessary to consider uncertainty of information due to data incompleteness or traffic dynamics. Our research group has proposed a virtual network reconstruction method based on a Bayesian attractor model that deals with uncertain information in decision-making. However, this method requires advance knowledge of the assumed environment as an attractor. When the environment changes, attractors must also be changed. In this study, we use control feedback to automatically update attractors when the environment changes. Simulation-based evaluations demonstrate that the proposed method deals with unknown situations while maintaining noise tolerance.

For Internet service providers to efficiently use network resources, they need to conduct traffic engineering to dynamically control traffic routes to accommodate traffic with limited network resources. The performance of traffic engineering depends on the accuracy of traffic prediction. However, the volume of network traffic has been changing drastically in recent years due to the growth of various types of network services, making traffic prediction increasingly difficult. Our simple ideas to overcome this challenge are to separate traffic into predictable and unpredictable parts and to apply different control policies to predictable and unpredictable traffic. To promote these ideas, we use software-defined networking technology, particularly Open-Flow, that can control macroflows defined by any combination of L2-L4 packet header information such as 5-tuple. In this paper, we therefore propose the macroflow-generating method for separating traffic into predictable macroflows that have little traffic variation and unpredictable macroflows that have large traffic variation within a limited flow table size. We also propose a macroflow-based traffic engineering scheme that uses different routing policies in accordance with traffic predictability. Simulation evaluation results suggest that our proposed scheme can reduce the maximum link load in a network at the most congested time by 34% and the average link load in a network on average by 11% compared with the current traffic engineering schemes.

Dynamic placement of the virtual network functions (VNFs) is one of the promising approaches to handling time-varying demands; when demands are small, the energy consumption can be reduced by placing the VNFs to a small number of physical nodes and shutting down unused nodes. If the demands becomes large, the VNFs are migrated to allocate the sufficient resources. In the dynamic placement of the VNFs, it is important to avoid a large number of migrations at each time because the migration requires a large amount of bandwidth. In this paper, we propose a new method to dynamically place the VNFs to follow the traffic variation without migrating a large number of VNFs. Our method is based on the model predictive control (MPC). By applying the MPC to the dynamic placement of the VNFs, our method starts migration in advance by considering the predicted future demands. As a result, our method allocates sufficient resources to the VNFs without migrating a large number of VNFs at the same time even when traffic variation occurs. Through simulation, we demonstrate that our method handles the time variation of the demands without requiring a large number of migration at any time slot.

Traffic engineering (TE) plays an essential role in deciding routes that effectively use network resources. This is particularly important when one considers the increasing time variation of Internet traffic such as streaming and cloud services. Traffic engineering with traffic prediction is one approach to stably accommodating time-varying traffic. This approach calculates routes from predicted traffic to avoid congestion, but predictions may include errors that instead cause congestion. We propose a prediction-based traffic engineering method that is robust to prediction errors by considering the probability distribution of predicted traffic. Our approach is based on a control-theoretic approach called stochastic model predictive control. Routes are calculated using a probability distribution of prediction errors so that the occurrence probability of congestion is lower than an operator-specified level. By considering the multi-step future dynamics of traffic, the routes are changed gradually to avoid route oscillation. We also show a relaxation method for unreliable far-future probabilistic constraints to avoid overly conservative route changes. Through simulations using backbone network traffic traces, we demonstrate that our method can accommodate most traffic variations under a given target link capacity without sudden large routes changes.

Although the use of software-defined networking (SDN) enables routes of packets to be controlled with finer granularity (down to the individual flow level) by using traffic engineering (TE) and thereby enables better balancing of the link loads, the corresponding increase in the number of states that need to be managed at routers and controller is problematic in large-scale networks. Aggregating flows into macro flows and assigning routes by macro flow should be an effective approach to solving this problem. However, when macro flows are constructed as TE targets, variations of traffic rates in each macro flow should be minimized to improve route stability. We propose two methods for generating macro flows: one is based on a greedy algorithm that minimizes the variation in rates, and the other clusters micro flows with similar traffic variation patterns into groups and optimizes the traffic ratio of extracted from each cluster to aggregate into each macro flow. Evaluation using traffic demand matrixes for 48 hours of Internet2 traffic demonstrated that the proposed methods can reduce the number of TE targets to about 1/50 ~ 1/400 without degrading the link-load balancing effect of TE.

Traffic engineering with traffic prediction is one approach to accommodate time-varying traffic without frequent route changes. In this approach, the routes are calculated so as to avoid congestion based on the predicted traffic. The accuracy of the traffic prediction however has large impacts on this approach. Especially, if the predicted traffic amount is significantly less than the actual traffic, the congestion may occur. In this paper, we propose the traffic prediction methods suitable to the traffic engineering. In our method, we perform preprocessing before the prediction in order to predict the periodical variation accurately. Moreover, we consider the confidence interval for the prediction error and the variation excluded by the preprocessing to avoid the congestion caused by the temporal traffic variation. In this paper, we discuss three preprocessing approaches; the trend component, the lowpass filter, and the envelope. Through simulation, we clarify that the preprocessing by the trend component or the lowpass filter increases the accuracy of the prediction. In addition, considering the confidence interval achieves the lower link utilization within a fixed control period.