研究者詳細 - 横平　徳美

論文 - 横平　徳美

分割表示 149 件中 1 - 20 件目／全件表示 >>

Performance Improvement of a Virtual Network Embedding Algorithm based on Temporal-difference Learning by Resource-Constraint-Aware Candidate Solution Selection

Yukinobu Fukushima, Yuta Sagawa, Yuya Tarutani, Tokumi Yokohira

IEIE Transactions on Smart Processing and Computing 13 ( 2 ) 158 - 166 2024年

掲載種別：研究論文（学術雑誌）

In this paper, we tackle a virtual network embedding problem in network virtualization. For this problem, an algorithm (VNE-TD) based on temporal difference learning has been proposed. VNE-TD, however, does not consider the node and link resource constraints in selecting the candidate solutions. Therefore, when attempting to construct a virtual network, the embedding of a virtual network may fail due to insufficient resources. In this paper, we modify VNE-TD to select only those candidate solutions that satisfy the node and link resource constraints. We add a function to check the satisfiability of the node and link resource constraints to VNE-TD. The simulation results show that our modified methods reduce the blocking ratio of virtual network requests by up to 80% compared to VNE-TD.

DOI： 10.5573/IEIESPC.2024.13.2.158

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A Protection Scheme with Speech Processing against Audio Adversarial Examples

Yuya Tarutani, Taisei Yamamoto, Yukinobu Fukushima, Tokumi Yokohira

IEEE Access 1 - 1 2024年

Statistical Analysis of Effectiveness of Cross-Disciplinary Team’s Practice in Engineering Project Based Learning

Masaya MIKAMI, Takahiro HIWAKI, Yoshihiro HAGIHARA, Yuya TARUTANI, Tokumi YOKOHIRA

Journal of JSEE 71 ( 2 ) 2_14 - 2_20 2023年

An Improvement of an IP Fast Reroute Method Using Multiple Routing Tables 査読

23 ( 6 ) 1315 - 1324 2022年11月

Consensus Building using Deep Reinforcement Learning for Energy Management

Yuya Tarutani, Isato Oishi, Yukinobu Fukushima, Tokumi Yokohira

IEIE Transactions on Smart Processing & Computing 11 ( 4 ) 284 - 291 2022年8月

Minimizing the monetary penalty and energy cost of server migration service

Yukinobu Fukushima, Tatsuya Suda, Tutomu Murase, Yuya Tarutani, Tokumi Yokohira

Transactions on Emerging Telecommunications Technologies 2022年

掲載種別：研究論文（学術雑誌）

Current IaaS (infrastructure as a service) cloud service may not satisfy communication QoS (quality of service) requirements of delay-sensitive network applications, if there is a significant physical distance between a server of the network application (NetApp server) at a data center and its network application clients (NetApp clients). In order to improve communication QoS of NetApp clients, we propose server migration service (SMS) in this article. SMS allows NetApp servers to migrate among different locations in the network (1) to optimally locate themselves in relation to NetApp clients and mitigate the QoS degradation caused by location-related factors (ie, propagation delays on network links) and (2) to optimally distribute traffic load over routers and processing load over (physical) computers and decrease the energy consumption. We develop a mixed-integer programming model that determines when and to which locations NetApp servers migrate to minimize the total operating cost of SMS, that is, the sum of the monetary penalty incurred due to QoS violation and energy cost incurred due to energy consumption, while preventing NetApp servers from excessively migrating and adversely impacting QoS of the non-SMS service that share the resource of the substrate network with SMS. Simulation results show that the model developed in this article achieves up to 42% lower total operating cost of SMS compared to the model that only minimizes the monetary penalty of SMS without considering the energy cost of SMS.

DOI： 10.1002/ett.4511

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Throughput Fairness in Congestion Control of Multipath TCP.

Yu Deguchi, Aoshi Kobayashi, Yuya Tarutani, Yukinobu Fukushima, Tokumi Yokohira

ICTC 123 - 126 2022年

A Secret Sharing Scheme to Reduce the Total Data Size

Kuniaki Tsuji, Shiden Kishimoto, Yuya Tarutani, Yukinobu Fukushima, Tokumi Yokohira

2021 International Conference on Information and Communication Technology Convergence (ICTC) 2021年10月

A Server Migration Method Using Q-Learning with Dimension Reduction in Edge Computing 査読

Ryo Urimoto, Yukinobu Fukushima, Yuya Tarutani, Tutomu Murase, Tokumi Yokohira

International Conference on Information Networking 2021-January 301 - 304 2021年1月

掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：IEEE

Edge computing is a promising computing paradigm that satisfies QoS requirements of delay-sensitive applications. In edge computing, server migration control is indispensable for managing client mobility. As a server migration method for edge computing, the method based on Q-learning has been proposed. However, the method assumes that there is only one application client and the number of destination edge servers is limited to one. In this paper, we propose a server migration method using Q-learning that copes with realistic situations where there are multiple application clients and destination edge servers. The contributions of this paper are as follows: 1) we clarify that, under the situation with multiple application clients and multiple destination edge servers, a straightforward server migration method using Q-learning (RL method) does not scale due to state space explosion, and 2) we propose a server migration method using Q-learning (RL-DR method) that reduces the dimensionality of state space by abstracting the numbers of application clients at all locations into a center of the gravity (COG) of application clients. The simulation results show that 1) RL method shows up to 248% worse performance than conventional server migration methods because of state space explosion and 2) RL-DR method achieves up to 38.3% better performance than the conventional methods.

DOI： 10.1109/ICOIN50884.2021.9333965

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その他リンク： https://dblp.uni-trier.de/db/conf/icoin/icoin2021.html#UrimotoFTMY21

Proposal of device control method based on consensus building using reinforcement learning 査読

Isato Oishi, Yuya Tarutani, Yukinobu Fukushima, Tokumi Yokohira

International Conference on Information Networking 2021-January 451 - 456 2021年1月

掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：IEEE

Various information is collected from IoT devices through the network. As such device becomes more familiar to the user, services are required to consider the influence of user. However, it is difficult to set the parameters of actuators that build consensus among all users in an environment where people with various preferences coexist. The conventional method minimizes the power consumption under the constraints of the user stress. However, this method has a problem that the calculation overhead is increased as the number of devices and users is increased. In this study, we propose a device control method based on consensus building with reinforcement learning. In the proposed method, the state is reduced by applying reinforcement learning for reducing the calculation overhead. As a result of evaluation, we clarified that our method obtains the device parameters that improve the reward by 1.5 times compared with the conventional method. Moreover, we also clarified that a reward value of 98.6% can be achieved compared to the optimum value.

DOI： 10.1109/ICOIN50884.2021.9333958

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その他リンク： https://dblp.uni-trier.de/db/conf/icoin/icoin2021.html#OishiTFY21

An IP Fast Reroute Method against Multiple Node Failures 査読

Naoto Numata, Masashi Ishigai, Yuya Tarutani, Yukinobu Fukushima, Tokumi Yokohira

International Conference on ICT Convergence 2020-October 714 - 719 2020年10月

掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：IEEE

Since network failures have a huge negative impact on our daily life, networks should be recovered from the failures as soon as possible. Although a lot of IP fast reroute methods have been proposed, most methods focus on multiple link failures or a single node failure. This paper proposes an IP fast reroute method which can reroute packets against multiple node failures. The paper is the first paper which deals with multiple node failures in the research area on IP fast reroute. The proposed method generates spanning trees to bypass the failures from a given network topology in network design stage, and reroutes a packet using one of the generated spanning trees every time the packet encounters a node failure in network operation stage. Numerical example shows that such spanning trees can be easily generated using our proposed method.

DOI： 10.1109/ICTC49870.2020.9289623

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その他リンク： https://dblp.uni-trier.de/db/conf/ictc/ictc2020.html#NumataITFY20

Performance Evaluation of Feature Encoding Methods in Network Traffic Prediction Using Recurrent Neural Networks 査読

Yusuke Tokuyama, Ryo Miki, Yukinobu Fukushima, Yuya Tarutani, Tokumi Yokohira

ACM International Conference Proceeding Series 279 - 283 2020年3月

掲載種別：研究論文（国際会議プロシーディングス）

© 2020 ACM. Recurrent neural network method considering traffic volume, timestamp and day of the week (RNN-VTD method) is a promising network traffic prediction method because of its high prediction accuracy. The RNN-VTD method encodes timestamp and day of the week, which are categorical data, to numerical data using label encoding. The label encoding, however, gives magnitude to the encoded values, which may cause misunderstanding of recurrent neural network models, and consequently, the prediction accuracy of the RNN-VTD method may be degraded. In this paper, we investigate the effect of using one-hot encoding instead of label encoding for a feature encoding method in the RNN-VTD method. In the one-hot encoding, each input data is encoded to an k-dimensional 0 - 1 vector where k is the number of category types. Because the encoded data do not have magnitude, it is expected that the prediction accuracy of the RNN-VTD method is improved.

DOI： 10.1145/3395245.3396441

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A TCP Incast Avoidance Method Based on Retransmission Requests from a Client. 査読

Shiden Kishimoto, Shigeyuki Osada, Yuya Tarutani, Yukinobu Fukushima, Tokumi Yokohira

2019 International Conference on Information and Communication Technology Convergence(ICTC) 153 - 158 2019年

The Effect of Using Attribute Information in Network Traffic Prediction with Deep Learning

Yusuke Tokuyama, Yukinobu Fukushima, Tokumi Yokohira

9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018 521 - 525 2018年11月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Institute of Electrical and Electronics Engineers Inc.

It is crucial for network operators to predict network traffic in the future as accurate as possible for appropriate resource provisioning and traffic engineering. Recurrent neural network (RNN) methods are considered to be the most promising prediction methods because of their high prediction accuracy. In conventional studies, RNN methods use only time series of traffic volume as input, and do not use any attribute information (e.g., timestamp and day of the week) of the time series data. However, traffic volume changes depending on both time and day of the week. Therefore, it is possible that we can further improve the prediction accuracy of the RNN methods by using the attribute information as input, in addition to the time series of traffic volume. In this paper, we investigate the effect of using the attribute information of time series of traffic volume on prediction accuracy in network traffic prediction. We propose two RNN methods: RNN-VT method and RNN-VTD method. The RNN-VT method uses timestamp information and the RNN-VTD method uses both timestamp and day of the week information as input, in addition to the time series of traffic volume. Experimental results show that day of the week information is significantly effective for improving prediction accuracy of the RNN methods while timestamp information is not effective.

DOI： 10.1109/ICTC.2018.8539488

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Determining Server Locations in Server Migration Service to Minimize Monetary Penalty of Dynamic Server Migration 査読

Yukinobu Fukushima, Tutomu Murase, Gen Motoyoshi, Tokumi Yokohira, Tatsuya Suda

Journal of Network and Systems Management 26 ( 4 ) 1 - 41 2018年2月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Springer New York LLC

In this paper, we propose a new class of service called server migration service (SMS) to augment the existing IaaS (Infrastructure as a Service). SMS allows servers (server-side processes of a network application) to dynamically and automatically migrate as their clients (client-side processes of a network application) change their locations in order to reduce the total monetary penalty that the SMS provider pays to its SMS subscribers when failing to provide them with the guaranteed level of QoS. In this paper, we consider the monetary impact that arises from QoS degradation due to server migration and build an integer programming model to determine when and to which location servers should migrate to minimize the total monetary penalty incurred by the SMS provider. Numerical examples show that SMS achieves up to 96% lower total monetary penalty compared to that without server migration. Numerical examples also show that the integer programming model developed in this paper requires reasonable computation time under realistic parameter settings.

DOI： 10.1007/s10922-018-9451-6

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Workplace Capacity Design Using the Minimum Dominating Set in Server Migration Services 査読

Yukinobu Fukushima, Itsuho Goda, Tutomu Murase, Tokumi Yokohira

The 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS 2018) ???-??? 2018年

Link Capacity Provisioning and Server Location Decision in Server Migration Service 査読

Yukinobu Fukushima, Tutomu Murase, Tokumi Yokohira

IEEE International Conference on Cloud Networking (CloudNet 2018) ???-??? 2018年

The Effect of Using Attribute Information in Network Traffic Prefdiction with Deep Learning 査読

Yusuke Tokuyama, Yukinobu Fukushima, Tokumi Yokohira

International Conference on ICT Convergence 2018 (ICTC 2018) ???-??? 2018年

Backoff Algorithms to Avoid TCP Incast in Data Center Networks 査読

Shigeyuki Osada, Daichi Izumi, Shiden Kishimoto, Yukinobu Fukushima, Tokumi Yokohira

International Conference on ICT Convergence 2018 (ICTC 2018) ???-??? 2018年

Improvement of a TCP incast avoidance method using a fine-grained kernel timer 査読

Shigeyuki Osada, Shogo Wakai, Yukinobu Fukushima, Tokumi Yokohira

International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017 2017- 147 - 152 2017年12月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Institute of Electrical and Electronics Engineers Inc.

When a standard TCP implementation using the minimum retransmission timeout (RTOmin) of 200 ms is used in distributed file systems in data centers, a well-known throughput degradation called TCP Incast occurs, because 200 ms is too large as an RTOmin in data centers. In order to avoid TCP Incast, a TCP implementation using a much smaller RTOmin attained by a fine-grained kernel timer is proposed. However, throughput still decreases in the implementation when the number of servers in distributed file systems is large. In this paper, we propose an improvement method introducing a new TCP option which enables a client to request servers to retransmit packets in a better timing. Numerical examples obtained by extensive simulation runs show the effectiveness of the improved method.

DOI： 10.1109/ICTC.2017.8190962

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