This paper proposes fast repairing methods that uses hierarchical software defined network controllers for recovering from massive failure in a large-scale IP over a wavelength-division multiplexing network. The network consists of multiple domains, and slave controllers are deployed in each domain. While each slave controller configures transport paths in its domain, the master controller manages end-to-end paths, which are established across multiple domains. For fast repair of intra-domain paths by the slave controllers, we define the optimization problem of path configuration order and propose a heuristic method, which minimizes the repair time to move from a disrupted state to a suboptimal state. For fast repair of end-to-end path through multiple domains, we also propose a network abstraction method, which efficiently manages the entire network. Evaluation results suggest that fast repair within a few minutes can be achieved by applying the proposed methods to the repairing scenario, where multiple links and nodes fail, in a 10,000-node network.

When a massive network disruption occurs, repair of the damaged network takes time, and the recovery process involves multiple stages. We propose a fast and flow-controlled multi-stage network recovery method for determining the pareto-optimal recovery order of failed physical components reflecting the balance requirement between maximizing the total amount of traffic on all logical paths, called total network flow, and providing adequate logical path flows. The pareto-optimal problem is formulated by mixed integer linear programming (MILP). A heuristic algorithm, called the grouped-stage recovery (GSR), is also introduced to solve the problem when the problem formulated by MILP is computationally intractable in a large-scale failure. The effectiveness of the proposed method was numerically evaluated. The results show that the pareto-optimal recovery order can be determined from the balance between total network flow and adequate logical path flows, the allocated minimum bandwidth of the logical path can be drastically improved while maximizing total network flow, and the proposed method with GSR is applicable to large-scale failures because a nearly optimal recovery order with less than 10% difference rate can be determined within practical computation time.

This paper proposes a recommendation-based bandwidth calendaring system for packet transport networks. The system provides a user-portal interface with which users can directly reserve packet transport resources. In this regard, the system recommends multi-grade (e.g., multi-price) reservation plans. By adjusting grades of plans in accordance with network resource utilization, this system provides not only reservation flexibility for users but also efficient utilization of network resources. For recommending multi-grade plans, pre-computation of resource allocation is required for every time slot. Because the number of time slots is huge, we also propose an algorithm for fast computation of resource allocation based on time-slot aggregation. Our evaluation suggests that our algorithm can produce a sub-optimal solution within quasi-real time for a large-scale network. We also show that our recommendation-based system can increase the service-provider-revenue in peaky traffic demand environments.

A multicast ATM switch with a new external copy module is proposed. The copy module, called copy-trunk, has two new functions to guarantee the QoS of P-MP cells: The delay priority control function and the output-cell-spacing control function. By using the delay priority control function, copied cells with high-priority are always released earlier than those with low-priority so as to avoid increasing the delay time of real-time traffic. The output-cell-spacing control function is used to reduce the burstiness of the output traffic. The output pattern is adaptively controlled by measuring the input load. The effects of these two controls are quantitatively described. The copy-trunk allows the multicast capability of a switch to be efficiently and economically increased to satisfy future traffic volume and services.

This paper describes an innovative heat-pipe cooling technology for asynchronous transfer mode (ATM) switching multichip modules (MCMs) operating with a throughput of 40 Gb/s. Although high-speed ATM link-wires are connected at the top surface of the MCMs, there is no room to cool the MCM by forced air convection, because power and the system clock signal are supplied by connectors on the rear and periphery of the MCM. We therefore chose to attach a cold-plate to the back of each MCM. The condenser part of the heat pipe, which is mounted behind the power supply printed circuit board, is cooled by low-velocity forced air. Total power dissipation is about 30 watts per MCM. With a 2 m/s foreced airflow, the sub-switching-element module (four MCMs) operates at a throughput of 80 Gb/s with a maximum junction temperature of less than 85. Measured thermal resistance between the switch LSI junction and air is about 6/W. This heat-pipe cooling system has a small system footprint, compact hardware, and good cooling capacity.

This paper proposes a disaster recovery method for transport networks. In a scenario of recovery from a disaster, a network is repaired through multiple restoration stages because repair resources are limited. In a practical case, a network should provide the reachability of important traffic in transient stages, even as service interruption risks and/or operational overheads caused by transport paths switching are suppressed. Then, we define the multi-objective optimization problem: maximizing the traffic recovery ratio and minimizing the number of switched transport paths at each stage. We formulate our problem as linear programming, and show that it yields pareto-optimal solutions of traffic recovery versus the number of switched paths. We also propose a heuristic algorithm for applying to networks consisting of a few hundred nodes, and show that it can produce sub-optimal solutions that differ only slightly from optimal solutions.