Published papers

In an IoT (Internet of Things) system, if image data can be collected in addition to sensor data, the amount of information obtained will increase significantly, which is very useful for monitoring natural disasters, for example. Considering communication speed and covering range, it is assumed that Wi-Fi multi-hop communication is appropriate for image data transmission. Since Wi-Fi consumes a large amount of power, a power supply for sensor nodes is required. Therefore, we are developing a self-sustaining wind power supply called Nishikaze which employs the potential energy conversion method. In this paper, we produced the Nishikaze second prototype. The measurement results of the amount of the generated power show that the Nishikaze second prototype can cover the power required to transmit an image of 500 kbytes at a 10 minute interval assuming that 3 m/s of wind blows for 6 hours in a day, 100 % of the generated power is available, and the active period of sensor nodes is 5 seconds.

In a Multi-access Edge Computing (MEC) environment, an application will be composed of multiple containers running on edge servers, and a container orchestration method determines which containers should be deployed on which edge servers. This paper proposes a container orchestration method considering shared containers. These containers will be introduced in an MEC environment to reduce application resource usage, similar to a shared library in an operating system. The proposed method adopts a multi-stage grouping of edge servers to aggregate computation resource information and network information of the edge servers for efficient orchestration calculation. The formulation of the proposed orchestration method has been successfully completed, marking a significant milestone in this research. Evaluation will be conducted assuming an environment that contains 2,048 edge servers and 200 user terminals.

The number of IoT devices is increasing, but applications that utilize IoT devices and their data are still developing. One reason is the tight coupling between M2M infrastructure and applications and the difficulty of handling mobile devices in a unified manner. To address these challenges, we developed a versatile M2M system called "MECM2M"that utilizes the MEC paradigm. This paper proposes "MECM2Mv2"by adopting the Pub-Sub model and redesigning modules to improve the latency and efficiency, which were issues in MECM2M. Our future research evaluates the safety of using MECM2Mv2 in autonomous driving applications.

Network Function Virtualization Infrastructure (NFVI) enables the operation of Network Function Chaining (NFC) in a low-cost and flexible manner by realizing network functions as software on general-purpose servers. One of NFVI's security requirements is access control to packet buffers, which can be achieved using Intra-process Isolation techniques. This paper proposes a secure NFVI using Software Fault Isolation (SFI) for access control to packet buffers by inserting bounds check instructions before indirect memory access instructions. The measurement results show that the proposed method is faster than ERIM, another Intra-process isolation method based on domain switching.

Demands for acquiring Internet behavior are increasing for Internet-scale network understanding such as inter-AS path management and traffic engineering. Although there are several efforts to make Internet behavior public, most of the public information is not structured and it is hard for applications to use such information. This paper proposes a network information sharing framework called KANVAS. It defines a network ontology called Bonsai which models network structure from viewpoints of physical, logical, service, and operation network structures. Bonsai can express network virtualization technologies such as link aggregation (LAG), VLAN, L2 over L3 tunneling, and virtual routing and forwarding (VRF). Applications can access network information via useful API. As a first step of development of KANVAS and Bonsai, this paper describes network information sharing within a single domain focusing on failure localization and throughput monitoring as examples. Evaluation results on a PoC system show that the time for failure localization is short enough and a throughput monitoring tool can choose appropriate monitoring points.

This paper proposes an autonomous network management system based on closed-loop control to deal with network failures and congestion. The proposed system is realized by collaboration of Weaver, an automated system configuration designer based on Intent-based Networking, and KANVAS (Knowledge base system in wide Area Networks with Versatility, Availability, and Scalability), a framework for collecting and utilizing network information. In the proposed system, the KANVAS system collects and analyzes network conditions, and the Weaver system plans and executes countermeasures against a failure event based on the analysis results. This paper shows two case studies. In the first case study, the proposed system can automatically recover from a service failure caused by a node failure in approximately 8.5 minutes. In the second case study, the proposed system can reroute VPN due to congestion in the underlay network in approximately 35 seconds. These results show that the proposed system can automatically recover service networks from a failure and congestion that occur on the underlay network in shorter time than that required for manual recovery.

This paper proposes an architecture called Cont-MEC for enabling UEs (User Equipments) to offload a part of applications to MEC (Multi-access Edge Computing) servers. It is expected that a MEC infrastructure is composed of a small number of centralized data centers and geographically distributed edge stations, in each of which multiple edge servers are installed. It is also expected that applications on UEs are implemented as container clusters similar to cloud-native applications and they are deployed on computing clusters. ContMEC has the following three features: (i) constructing a computing cluster per edge station for scalability to the number of UEs, (ii) hierarchical resource management for scalability and efficient resource sharing among computing clusters, and (iii) overlapped computing clusters for efficient resource sharing. A PoC (Proof-of Concept) implementation of ContMEC employs Kubernetes as a container orchestration system without modifications although Kubernetes does not take account of the MEC infrastructure. The PoC implementation shows that advantage of offloading is larger than disadvantage of implementing applications as container clusters, control traffic is moderate against the number of UEs, and efficient resource sharing among computing clusters is achieved.

As of 2022, 66% of Internet traffic is generated by video services, among which Netflix and YouTube are the dominant players. By monitoring the traffic volume of individual video services, ISPs (Internet Service Providers) can make appropriate capital investments and determine appropriate peering policies with CDN (Content Delivery Network) providers. Although DPI (Deep Packet Inspection) is widely used to identify network services, packet-level analysis does not scale to the traffic passing through backbone ISPs and is inapplicable to encrypted packets. This paper proposes OLIViS (OSINT-based Lightweight method for Identifying Video Services), a flow-level, label-free method designed for backbone ISPs. OLIViS employs a CNN-VAE (Convolutional Neural Network - Variational AutoEncoder) model to initially identify elephant flows which likely include video service flows and then utilizes OSINT (Open Source INTelligence) data for further services identification. Two datasets are used for evaluation: UTMobileNet2021 and WIDE backbone real trace. The evaluation results show that OLIViS identifies the Netflix and YouTube flows with an accuracy of over 96% in terms of traffic volume. Moreover, OLIViS infers the flow statistics (e.g., mean and peak throughput) of Netflix and YouTube, which closely resemble those inferred with DPI on non-encrypted traffic in a campus network.

Generally, a network administrator designs, constructs, and operates an enterprise network. Since inconsistency between the network design understood by the administrator and the actual network configuration might arise due to mistakes or errors, a method for automatically detecting such inconsistency is needed. The following four techniques are necessary for this purpose. (i) A machine-readable notation to represent the network configuration. (ii) A tool to write down the network design using the machine-readable notation. (iii) A tool to automatically detect the current network configuration and write it down in the machine-readable notation. (iv) A tool to compare the two outputs generated in (ii) and (iii). This paper employs the network ontology called Bonsai for (i). Bonsai can represent not only physical configurations but also virtualization technologies such as VLAN and overlay. This paper proposes three tools, nc-design, nc-detect, and nc-diff for (ii)-(iv), and confirms that they work as expected in the test network. This paper also measures their fundamental performance.

In addition to stationary sensors/actuators (nodes), mobile nodes such as vehicles and smartphones are connected to M2M (Machine-to-Machine) systems. Some M2M systems take into account node mobility, while some M2M systems regard vehicles as compute servers. N one of them, however, handle the mobility of nodes that act as compute servers. This paper proposes an MEC (Multi-access Edge Computing) based M2M system called MECM2M, which can handle the mobility of nodes acting as sensors/actuators and compute servers. The design policy of MECM2M is analogous to that of the Internet. The Internet provides two fundamental services: reliable byte stream and unreliable datagram. How data is used is up to applications. MECM2M provides M2M applications with three fundamental services as the M2M services: area-based data acquisition, node-based data acquisition, and node-based action instruction. How sensing data is used is up to M2M applications. MECM2M introduces the virtual nodes as digital twins in the virtual space for the physical nodes. The virtual nodes conceal differences from the physical nodes and provide a uniform interface. The relation among the virtual nodes, the physical nodes, and the locations of the physical nodes is managed by a domain ontology called Onto- Mecm2m,which enables effective processing of the three fundamental services. The evaluation results on an MECM2M emulator show that the response times of the three fundamental services satisfy the latency constraints, i.e., 100 ms, defined in an ETSI (European Telecommunications Standards Institute) document.

In an MEC (Multi-access Edge Computing) environment, it is suitable to compose an application as a set of containers for flexibility in resource allocation. This paper proposes stateless shared containers (SS-Containers) that can be shared by multiple applications simultaneously in an MEC environment, similar to a shared library in an operating system. It is expected that SS-Containers reduce resource consumption, shorten application startup time, and efficiently manage special hardware such as GPU. When 16 applications share an SS-Container with an encryption function, and each application contains the container with the same encryption function, the memory consumption is reduced by approximately 190 MiB. Comparing the case in which nine applications share an SS-Container that accesses a GPU to solve large-scale simultaneous linear equations with the case in which nine applications randomly access a GPU via a container inside them, the response time is reduced approximately by 57 % (39 seconds).

縮退アドレスモデルという新しいアドレス方式を提案し、これをIPv6に適用したLIN6を設計した。LIN6はMobile Ipv6と比較し、頑健性に優れる、ヘッダのオーバーヘッドがない、最適経路での通信が容易である、などの利点を持つ。さらにLIN6をUNIXオペレーティングシステムに実装し、性能評価を行った。