This paper describes an architectural design and related services of a new Japanese academic backbone network, called SINET5, which will be launched in April 2016. The network will cover all 47 prefectures with 100-Gigabit Ethernet technology and connect each pair of prefectures with a minimized latency. This will enable users to leverage evolving cloud-computing powers as well as draw on a high-performance platform for data-intensive applications. The transmission layer will form a fully meshed, SDN-friendly, and reliable network. The services will evolve to be more dynamic and cloud-oriented in response to user demands. Cyber-security measures for the backbone network and tools for performance acceleration and visualization are also discussed.

This paper considers optical transport and packet networks and discusses the constraints and solutions in computation of traffic engineering paths. We categorize the constraints into prunable or non-prunable classes. The former involves a simple metric which can be applied for filtering to determine the path. The latter requires a methodic consideration of more complicated network element attributes. An example of this type of constraints is path loss in which the metric can be evaluated only on a path basis, as opposed to simply applying the metric to the link. Another form of non-prunable constraint requires adaptation and common vector operation. Examples are the switching type adaptation and wavelength continuity, respectively. We provide possible solutions to cases with different classes of constraints and address the problem of path computation in support of traffic engineering in multi-layer networks where a set of constrains are concurrently present. The solutions include the application of channel graph and common vector to support switching type adaptation and label continuity, respectively.

This paper proposes an advanced hybrid network architecture and a comprehensive network design of the next-generation science information network, called SINET3. Effectively combining layer-1 switches and IP/MPLS routers, the network provides layer-1 end-to-end circuit services as well as IP and Ethernet services and enables flexible resource allocation in response to service demands. The detailed network design focuses on the tangible achievement of providing a wide range of network services, such as multiple layer services, multiple virtual private network services, advanced qualities of service, and layer-1 bandwidth on demand services. It also covers high-availability capabilities and effective resource assignment in the hybrid network. The cost reduction effect of our network architecture is also shown in this paper.

This paper proposes a Generalized Traffic Engineering Protocol (GTEP). GTEP is a protocol that permits communication between a Path Computation Element (PCE) and a Generalized Multi-Protocol Label Switching (GMPLS) controller (CNTL). The latter is hosted by each GMPLS node; it handles GMPLS and MPLS protocols such as routing and signaling protocols as well as controlling the GMPLS node host. The PCE provides multi-layer traffic engineering; it calculates Label Switched Path (LSP) routes and judges whether a new lower-layer LSP should be established. GTEP functions are implemented in both the PCE and GMPLS router. We demonstrate a multi-layer traffic engineering experiment conducted with GTEP.

A model for a large network with an unidirectional linear respone (ULR) is proposed in this letter. This deterministic system has powerful computing properties in very close correspondence with earlier stochastic model based on McCulloch-Pitts neurons and graded neuron model based on sigmoid input-output relation. The exclusive OR problems and other digital computation properties of the earlier models also are present in the ULR model. Furthermore, many analog and continuous signal processing can also be performed using the simple ULR neural network. Several examples of the ULR neural networks for analog and continuous signal processing are presented and show extemely promising results in terms of performance, density and potential for analog and continuous signal processing. An algorithm for the ULR neural network is also developed and used to train the ULR network for many digital and analog as well as continuous problems successfully.

An adaptive fuzzy network (AFN) is described that can be used to implement most of fuzzy logic functions. We introduce a learning algorithm largely borrowed from backpropagation algorithm and train the AFN system for several typical fuzzy problems. Simulations show that an adaptive fuzzy network can be implemented with the proposed network and algorithm, which would be impractical for a conventional fuzzy system.