Network segmentation and design algorithm for large-scale optical path networks based on traffic distribution information

This paper presents a new synthesis procedure (design algorithm) for cellular neural networks with space-invariant cloning template with applications to associative memories. In the present synthesis procedure, the design problem is formulated as a set of linear inequalities and the inequalities are solved using the well-known perceptron training algorithm. When the desired memory patterns are given by a set of bipolar vectors, it is guaranteed that a cellular neural network with a space-invariant cloning template can be designed using the design algorithm developed herein. A specific example is included to demonstrate the applicability of the methodology developed.

We propose a quasi-dynamic network design algorithm considering traffic growth in multi-layered photonic networks that consist of electrical paths and optical paths. The quasi-dynamic network design proposed herein leaves existing paths unchanged in order to minimize service disruption due to path rerouting as well as human error. Simulations verify that the network cost strongly depends on the network design algorithms adopted and the holding terms of service traffic. We, then, introduce a new parameter that represents expected service holding terms. The parameter is shown to represent effectively the network cost trend versus the number of the incremental designs. Furthermore, lower network cost is obtained by taking advantage of the network design algorithms that consider service holding times. This study is the first to address the criteria in designing future multi-layered photonic networks that offer cost-effective expansion while accommodating different services.

Machine learning (ML) is an effective solution to deal with some networking issues in large scale optical transport networks. Alarm (caused by failure, disaster, etc.) prediction is a typical and important use-case, where ML algorithm can work to predict events in advance. By accurate prediction, network administrators can be guided to undertake preventive measures ahead of time, thus avoiding the adverse effect. For training and testing of such event prediction models, it’s first and foremost to build a high-quality data set from real networks. However, the data collected from large-scale optical transport networks are inferior in quality and demand prompt pre-processing, for they are sometimes invalid, incomplete, inconsistent, and even inaccurate. After data pre-processing, there is only a small amount of available data left to build an ML data set, which extremely limits the performance of the alarm prediction model. This paper proposes a machine-learning-based alarm prediction method using self-optimizing data augmentation based on generative adversarial networks (GANs) for optical transport networks. Experimental results on a commercial backbone synchronous digital hierarchy (SDH) network with 274 nodes and 487 links demonstrate that the proposed method can achieve high accuracy for alarm prediction.

A Mesh Network Topological Optimization and Routing II (MENTOR-II) algorithm is a low complexity and efficient network design algorithm for IP networks with OSPF routing protocol in which appropriate link weights are assigned to the installed links such that the traffic always routes on the path with minimum distance. However, it should be noted that the flow delay of traffic assigned by MENTOR-II are not controlled because MENTOR-II gives the link weights for only assigning traffic flow into the worthy path, therefore routing of traffic flow does not get together with the traffic engineering. This study proposed a new modified version of MENTOR called Q-MENTOR which is able to assign traffic under flow delay controlled. The Q-MENTOR are evaluated in terms of installation cost, routing cost and reliability for 486 network design conditions. The design results are compared with MENTOR-II and it was found that Q-MENTOR is suitable for designing high maximum utilization networks with good delay and reliability. The results also show that low traffic flow delay requirements often present the worst installation cost, routing cost and reliability.

A typical operating environment of a packet switching (store-and-forward) computer communication network is that it is shared by many users with different classes of packets. Packets may be classified in a very general fashion by types of users, messages, applications, transactions, response time requirements, packet parameters such as packet rate and length, and by network parameters such as source-destination and path length. A well-designed network must provide access and performance assurance to all packet classes. This paper presents a heuristic algorithm for designing such a communication network. The algorithm presented contains heuristic algorithms for discrete link capacity assignment, priority assignment, and flow assignment problems with an additional feature which allows one to alter network topology interactively. Sample results from applications of the overall network design are also given.

Efficient operation of wireless networks requires distributed routing and scheduling algorithms that take into account interference constraints. Recently, a few algorithms for networks with primary- or secondary-interference constraints have been developed. Due to their distributed operation, these algorithms can achieve only a guaranteed fraction of the maximum possible throughput. It was also recently shown that if a set of conditions (known as Local Pooling) is satisfied, simple distributed scheduling algorithms achieve 100% throughput. However, previous work regarding Local Pooling focused mostly on obtaining abstract conditions and on networks with single-hop interference or single-hop traffic. In this paper, we identify several graph classes that satisfy the Local Pooling conditions, thereby enabling the use of such graphs in network design algorithms. Then, we study the multihop implications of Local Pooling. We show that in many cases, as the interference degree increases, the Local Pooling conditions are more likely to hold. Consequently, although increased interference reduces the maximum achievable throughput of the network, it tends to enable distributed algorithms to achieve 100% of this throughput. Regarding multihop traffic, we show that if the network satisfies only the single-hop Local Pooling conditions, distributed joint routing and scheduling algorithms are not guaranteed to achieve maximum throughput. Therefore, we present new conditions for Multihop Local Pooling, under which distributed algorithms achieve 100% throughout. Finally, we identify network topologies in which the conditions hold and discuss the algorithmic implications of the results.

This paper studies efficient algorithms for dynamic curing policies and the corresponding network design problems to guarantee the fast extinction of epidemic spread in a susceptible-infected-susceptible (SIS) model. We consider a Markov process-based SIS epidemic model. We provide a computationally efficient curing algorithm based on the curing policy proposed by Drakopoulos, Ozdaglar, and Tsitsiklis (2014). Since the corresponding optimization problem is NP-hard, finding optimal policies is intractable for large graphs. We provide approximation guarantees on the curing budget of the proposed dynamic curing algorithm. We also present a curing algorithm fair to demographic groups. When the total infection rate is high, the original curing policy includes a waiting period in which no measurements are taken to mitigate the spread until the rate slows down. By utilizing network design strategies that either delete edges or reduce their weights, we limit the total infection rate, allowing the curing process to proceed continuously without requiring a waiting period. We provide algorithms with provable guarantees for the considered network design problems. In summary, the proposed curing and network design algorithms together provide an effective and computationally efficient approach that mitigates SIS epidemic spread in networks.

Wireless sensor-actuator networks (WSANs) have recently been suggested as an enhancement to the conventional sensor networks. The powerful and mobile actuators can patrol along different routes and communicate with the static sensor nodes. This work is motivated by applications in which the objective is to minimize the data collection time in a stochastic and dynamically changing sensing environment. This is a departure from the previous static and deterministic mobile element scheduling problems. In this paper, we propose PROUD, a probabilistic route design algorithm for wireless sensor-actuator networks. PROUD offers delay-minimized routes for actuators and adapts well to network dynamics and sensors with non-uniform weights. This is achieved through a probabilistic visiting scheme along pre-calculated routes. We present a distributed implementation for route calculation in PROUD and extend it to accommodate actuators with variable speeds. We also propose the Multi-Route Improvement and the Task Exchange algorithms for balancing load among actuators. Simulation results demonstrate that our algorithms can effectively reduce the overall data collection time in wireless sensor-actuator networks. It well adapts to network dynamics and evenly distributes the energy consumption of the actuators.

We focus on the design of the logical layer on an optical infrastructure for three different architectures: fixed grid WDM with fixed-rate multilevel modulation (WDM-FRMF), fixed grid WDM with time-domain hybrid modulation formats (WDM-TDHFM) and flexible grid (FG). A fairly detailed physical layer modeling is considered in the network design algorithms. Simulations are performed on a 20 nodes network with varying traffic loads. Besides comparing the two paradigms of flexible and fixed grid networks, including both pure and hybrid modulation formats, in terms of their spectral efficiency and number of blocked requests, we show the importance of integrating a detailed physical layer modeling in the network design phase. We mostly focus on the investigation of the impact of changing physical layer design parameters such as fiber type and span length on network level performance.

The North-East Corridor (NEC) Testbed project is the 3rd of three NIST (National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions (footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

Broadband impedance matching via lossless unsymmetrical lattice networks

We propose a hierarchical optical path network design algorithm that considers wavelength conversion. Numerical experiments prove that, by employing wavelength conversion, hierarchical optical path networks will be more cost effective than single-layer optical path networks over a wide range of traffic demand, which is not possible without using wavelength conversion.

Signal re-amplification, re-shaping, and re-timing (3R) opto-electronic regeneration is a crucial function to scale elastic optical networks. These devices are especially important on large-scale optical networks, enabling long-distance communications. In this work, we analyze the impact of these devices on the quality of service in wide-area optical networks in terms of blocking probability. To this end, we performed network simulations to obtain the blocking probability of users on diverse scenarios, modifying the number of regeneration devices per node jointly with several parameters such as link distances, bit-error-rate (BER) threshold, and network capacity. We use a physical layer model considering linear and non-linear impairments to compute the maximum reach of each communication for a given bitrate and modulation format for a variety of BER thresholds. The results show the importance of using 3R regeneration in wide-area elastic optical networks (EON). Notwithstanding, a limit was found where adding regenerators per node does not substantially improve the blocking probability. Therefore, a cost-benefit analysis must be done considering the cost involved in installing more of these devices.

Chapter 11 - The fifth generation fixed network (F5G)

In recent years, deep learning has achieved good results in the semantic segmentation of medical images. A typical architecture for segmentation networks is an encoder-decoder structure. However, the design of the segmentation networks is fragmented and lacks a mathematical explanation. Consequently, segmentation networks are inefficient and less generalizable across different organs. To solve these problems, we reconstructed the segmentation network based on mathematical methods. We introduced the dynamical systems view into semantic segmentation and proposed a novel segmentation network based on Runge-Kutta methods, referred to hereafter as the Runge-Kutta segmentation network (RKSeg). RKSegs were evaluated on ten organ image datasets from the Medical Segmentation Decathlon. The experimental results show that RKSegs far outperform other segmentation networks. RKSegs use few parameters and short inference time, yet they can achieve competitive or even better segmentation results compared to other models. RKSegs pioneer a new architectural design pattern for segmentation networks.