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134 results on '"Takaya Miyazawa"'

1. High-Speed Wavelength Resource Reconfiguration System Concurrently Establishing/Removing Multiwavelength Signals

Author

Masaki Shiraiwa, Hideaki Furukawa, Takaya Miyazawa, Yoshinari Awaji, and Naoya Wada

Subjects
wavelength resource reconfiguration system, elastic optical network, burst-mode erbium-doped fiber amplifier, Applied optics. Photonics, TA1501-1820, Optics. Light, QC350-467
Abstract

We propose a novel wavelength resource reconfiguration system with a wavelength selective switch (WSS) and our unique burst-mode erbium-doped fiber amplifier (BM-EDFA) for dynamic and agile new-generation optical networks such as an elastic optical network. The system can concurrently establish or remove multiwavelength channels, while preserving the system stability. In a conventional reconfiguration system, the processing time is increased in proportion to the number of the reconfigured wavelength channels because each wavelength channel is sequentially established or removed. On the other hand, in the proposed system, the multiwavelength channels would be simultaneously established or removed. Therefore, in principle, the processing time for reconfiguration of multiwavelength channels can be 1/(the number of the reconfigured channels) compared to the conventional system with sequential processing. In addition, the system can provide a further reduction effect of the processing time since the adjustment of variable optical attenuators of the WSS is unnecessary after establishing or removing multiwavelength channels. We had demonstrated a proof of concept using a reconfiguration system for the first time. The system employing flexible-grid WSSs and a BM-EDFA showed that the total processing time could be more than 100 s shorter in the case of removing four-wavelength channels with the high-quality data transmission.

Published
2016
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13. Advanced data analytics using three-stage intelligent model pipelining for containerized microservices in 5G networks and beyond

Author

null Hitoshi Asaeda, null Yasushi Naruse, null Yusuke Yokota, null Ved P. Kafle, and null Takaya Miyazawa

Abstract

The recent rapid advancement of cloud-native networking infrastructure has leveraged the resource virtualization technology of containers to realize diverse microservice-based applications in 5G/6G networks and clouds. Containers drastically enhance the efficiency of computational resource allocation and utilization as compared to the related virtualization technology of Virtual Machines (VMs). The networking environment leveraging both VM and container mixed virtualization technologies makes the most use of them to realize a computational platform whose resources can be dynamically adjusted to a fine granularity. To continuously meet the required levels of quality of services in 5G/6G networks and clouds in that platform, an agile and autonomous data analytics system in the control plane is essential for the accurate prediction of server workloads and dynamic allocation of enough amount of computational resource. In this paper, we introduce a framework, which complies with Recommendation ITU-T Y.3177, for autonomous computational resource control and management. The framework consists of an advanced data analytics system and a resource control system. We propose an architecture for the advanced data analytics system consisting of learning and prediction components. The learning component includes a three-stage intelligent model pipelining with three cascaded machine learning models, nonlinear regression, clustering, and multiple regression. These models determine the fluctuation trends in CPU utilization, classify services with similarities in the trends, and predict the peak CPU utilization of each containerized microservice. We evaluate the proposed models through experiments and numerical analysis. The results prove that the models support agile data analytics, which can complete data processing in the time granularity of seconds and achieve higher prediction accuracy of CPU utilization.

Published
2023
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