It is important for distributed computing environments that communication networks are transparent to applications. This allows applications to make the best use of computer resources, To realize network transparency, communication platforms which support distributed computing environments should have a system configuration like an extension of a workstation's internal bus. Such communication platforms require high-speed communication paths, ability to handle different transmission speeds, high reliability, and scalability. This paper proposes a broadband distributed data network which satisfies the above requirements, and provides a distributed computing environment. Our system uses basic nodes called ATM-HUBs and ATM-Gateways (ATM-GWs) as its central components. The nodes consist of cell switch modules which can be made up of building blocks, ATM interface modules, and other functional modules. The switch module is connected to functional modules through a unified interface. The ATM-HUB in particular has conventional LAN interface modules. Using the conventional LAN interface and ATM interface module in an ATM-HUB, a wide variety of terminals, including conventional LAN terminals and ATM terminals, can be accommodated, so offering flexibility of communication modes to users. Furthermore, the use of star wiring around the ATM-HUB and media access control (MAC) address routing gives a higher transfer rate comparable to the speed of a physical transmission line for communication between ATM terminals, or between conventional LAN terminals.

This paper describes the application of a neural network to the optimal routing problem in broadband multimedia networks, where the objective is to maximize network utilization while considering the performance required for each call. In a multimedia environment, the performance required for each call is different, and an optimal path must be found whenever a call arrives. A neural network is appropriate for the computation of an optimal path, as it provides real-time solutions to difficult optimization problems. We formulated optimal routing based on the Hop field neural network model, and evaluated the basic behavior of neural networks. This evaluation confirmed the validity of the neural network formulation, which has a small computation time even if there are many nodes. This characteristic is especially suitable for a large-scale system. In addition, we performed a computer simulation of the proposed routing scheme and compared it with conventional alternate routing schemes. The results show the benefit of neural networks for the routing problem, as our scheme always balances the network load and attains high network utilization.

As use of the Internet continues to spread rapidly, Traffic Engineering (TE) is needed to optimize IP network resource utilization. In particular, load balancing with TE can prevent traffic concentration on a single path between ingress and egress routers. To apply TE, we have constructed an MPLS (Multi-Protocol Label Switching) network with TE capability in the JGN (Japan Gigabit Network), and evaluated dynamic load balancing behavior in it from the viewpoint of control stability. We confirmed that with this method, setting appropriate control parameter values enables traffic to be equally distributed over two or more routes in an actual large-scale network. In addition, we verified the method's effectiveness by using a digital cinema application as input traffic.