研究発表

In a high-speed backbone network, the failure of network links may cause large data losses, so it is necessary to reserve spare network resources for faster recovery. The conventional protection methods that reserve backup paths do not consider the failure probability of each network link and allocate same amount of network resources for the backup paths regardless of the failure rate of network links. This leads to the excessive or insufficient capacity allocation. This paper proposes the routing method that guarantees the expected value of the allocated capacity based on dynamically changed link failure rate. We formulate a Mixed Integer Liner Programming (MILP) model for the proposed method. We conduct simulations to investigate the effect of the expected capacity guaranteed routing over the conventional routing method in terms of bandwidth blocking probability, transmitted capacity achieved ratio, and the ratio of total transmitted capacity to total requested capacity. The results show that our proposed method marks higher transmitted capacity achieved ratio compared to conventional routing methods. We also find the transmitted capacity achieved ratio of the proposed method maintains 90% under high failure rate.

Biological Attractor Selection (BAS) is a promised network control technology to ensure environmental fluctuation. This demonstration shows BAS and software defined networking control interworking for controlling the virtual packet optical node network.

Autonomous driving vehicle control by edge computer network having very short response time has been proposed. Each vehicle has an agent program on the edge and automatically moved to adjacent edge computer following to the vehicle movement. On the edge computer, the agent program is processing with other vehicles' agents in a cyber network. We employ triple redundancy and majority rule to achieve high-reliability and less than 10 ms control latency try to be guaranteed. In addition, each vehicle has an IoT sensor including fine-GPS, so all precious position and speed of the vehicle can be monitored. We constructed the autonomous driving experimental course, the autonomous driving vehicle, and the edge computer system with the center cloud and tested in the campus test bed. According to the vehicle movement, network orchestrator setup a new optical path automatically and send the agent program to the adjacent target edge computer. This orchestration function is newly proposed application triggered dynamic optical network. In this presentation, I will show the detailed experimental results. This architecture and experimental results can be applied to the future smart and connected community.

The prototype system of the energy efficient data center network HOLST was introduced and estimated 49% reduction of switching power consumption with application triggered flow classification using operated data center traffic data.

Dynamic resource allocation in virtual packet optical node is deployed and confirmed that required service is placed based on the calculation result of biological attractor selection.