Kohei Shiomoto, Ph.D
塩本　公平

Kohei Shiomoto, Ph.D
塩本　公平

Professor, Department of Intelligent Systems, Faculty of Information Technology, Tokyo City University.

Kohei Shiomoto is a Professor, Tokyo City University, Tokyo Japan. Since joining NTT Laboratories in 1989, he has been engaged in research and development in the data communications industry on high-speed computer network architecture, traffic management, and network analysis to create innovative technologies for the Internet, mobile, and cloud computing. From 1996 to 1997, he was a visiting scholar at Washington University in St. Louis, MO, USA. In 2017, he joined Tokyo City University to engage in research and education on data science and computer networking. Current research interests include data mining for network management, human flow analysis, cloud computing and blockchain. He has published more than 70 academic papers, 130 refereed international conference papers, and 6 RFCs in IETF. He served as Guest Co-Editor for a series of special issues established in IEEE Transactions on Network and Service Management. He has served in various roles organizing IEEE ComSoc conferences including IEEE NOMS, IEEE IM, and IEEE NetSoft. He served as the lead Series editor for the Network Softwarization and Management Series in IEEE Communications Magazine, 2018-2021. He is a Fellow of the Institute of Electronics, Information and Communication Engineers (IEICE), a Senior Member of the IEEE, and a member of the ACM and the Information Processing Society of Japan (IPSJ).

Research

Architecture and Operation of Edge Cloud Computing System for Society 5.0

At the boundary between data science and computer networks, we are working to create innovative technologies for computer networks using state-of-the-art machine learning, optimization, and cryptography. Aiming to realize Society 5.0, a human-centered society that balances economic development with the resolution of social issues, it is becoming increasingly important to collect, analyze, and utilize data from a vast array of sensors through systems that highly integrate cyberspace and physical space. We conduct research on innovative technologies for architecture and operation of edge computing and cloud computing systems. In particular, we have recently focused on data analysis and data distribution. In data analysis, we are working on traffic prediction methods and traffic anomaly detection methods, and in data distribution, we are working on edge-cloud data distribution architectures and distributed information exchange infrastructures.

Traffic Prediction Methods

We are working on a method for predicting the amount of communication traffic from GPS devices. Using Bayesian networks, we will develop a method for predicting complex variations in communication traffic in space and time, which has been difficult to predict using conventional methods.

Traffic Anomaly Detection Methods

To cope with ever-changing attack patterns, we implement a communication traffic anomaly detection method using semi-supervised machine learning in order to reduce the work required to label training data for re-training.

Edge-Cloud Data Distribution Architectures

Develop an IoT platform connecting edge computers and data analysis servers to establish a foundation for collecting large amounts of sensor information. Optimization of chip architecture for machine learning inference at edge nodes will be studied. In addition, to ensure that the exchange of machine learning data between edge and cloud nodes is completed within the deadline, we will implement deadline-aware data transfer scheduling using reinforcement learning.

Distributed Information Exchange Infrastructures

Until now, centralized certificates have been required for information distribution on digital infrastructures, and management costs and confidentiality have been major problems. To solve this problem, we use blockchain technology to realize a data distribution infrastructure in which certificates can be authenticated in a decentralized manner. Furthermore, we will realize machine learning and federative learning using data that is encrypted using secret computation of fully homomorphic encryption.

Teaching

Dr. Shiomoto teaches Computer Networks, Cloud Computing, Network Algorithms, and Case Studies for undergraduate students, and Advanced Communication Networks for graduate students. He supervises research for graduation theses, master's theses, and doctoral dissertations. He is a class mentor for undergraduate students and is responsible for the education, career, and daily life of undergraduate students.

Computer Network

Computer networking is an indispensable technology required in all fields as information technology advances. Computer network technology has evolved with the times and will continue to do so. It will continue to evolve in the future. It is of course important to acquire knowledge of computer network technology, but it is even more important to understand its principles. The lectures are designed to help students acquire knowledge while understanding the essence of computers and networks. Students will learn the structure of computer networks with the Internet as the main subject.Students will learn the structure of computer networks with the Internet as the main subject. Students will learn the structure of computer networks with the Internet as the main subject. By learning the principles of architecture, protocols, and layer structures, students will develop the ability to master the most advanced technologies that are constantly evolving by themselves. Practical training (creation of a simple Web server by socket programming, protocol analysis by packet capture) is also included to deepen understanding and cultivate practical skills through hands-on experience of what students have learned in the lectures.

Cloud Computing

Acquire the basic concepts of cloud computing (computing, networking, storage). Acquire the ability to master new cloud computing technologies that evolve daily based on the basic concepts. Acquire the basic knowledge, concepts, and abilities to understand the issues of cloud computing that evolve daily, to produce new methods, and to evaluate the effectiveness of these methods. Understand the basic concepts of cloud computing (computing, networking, and storage) and be able to explain cloud computing in writing based on an understanding of these concepts. Based on the acquired knowledge, master the construction and operation of virtualization environments and the construction and operation of Linux operating system environments.

Network Algorithms

Queueing theory is a theory for considering how long people should wait in line at a bank ATM, or at a bus stop waiting for a bus, etc., and how to reduce the waiting time. Queueing theory can be used to evaluate the performance of systems such as the Internet, mobile, and cloud computing. The ability to capture the essence of complex systems, model them, and evaluate their performance is a skill that is necessary after entering the workforce. The lecture is designed to help students acquire the ability to grasp the essence of a system and evaluate its performance using theoretical analysis and simulation, as well as the ability to design a network that meets the requirements by formulating a problem-specific formulation. The ability to evaluate the performance of computer networks, computers, and other systems, which are advancing day by day, and to use them appropriately, is an essential skill now and in the future. Students learn stochastic process modeling methods and queueing theory necessary to evaluate performance indices such as system throughput, latency, and packet loss. Students master formulation methods based on network flow problems, mathematical programming, and various algorithms necessary to design networks that satisfy desired requirements. Acquire the knowledge, thinking, and abilities necessary to understand the issues involved in the design and operation of computer networks and computer systems, to devise innovative methods for solving these issues, and to evaluate the effectiveness of these methods. Students will be able to explain probability distributions such as Poisson, exponential, and Erlang distributions, to calculate expected value, variance value, etc. from probability distributions, to explain what queueing theory is, to calculate average waiting time, average processing time, etc. for a basic queueing system, and to understand the algorithm for finding the shortest path in a network.