Reliability Evaluation for Distributed PONs With Ring and Tree Topologies

In this paper we study the optimization issues of ring networks employing novel parallel multi-granularity hierarchical optical add-drop multiplexers (OADMs). In particular, we attempt to minimize the number of control elements for the off-line case. We present an integer linear programming formulation to obtain the lower bound in optimization, and propose an efficient heuristic algorithm called global bandwidth resource assignment that is suitable for the design of large-scale OADM networks.

Rapid development of optical communication techniques has made possible large scale wavelength routing optical networks. Nowadays, these networks are deployed worldwide. Large scale networks have natural hierarchies due to either geographical reasons or administrative consideration and are therefore managed in a hierarchical way correspondingly. That is, networks are composed of several subnetworks, which are themselves composed of other even smaller subnetworks. Erlang fixed point approximation (reduced load approximation) has been extensively used in performance evaluation of end to end call blocking probability in traditional circuit switching networks, which are also called loss networks. This method has also been extended for hierarchical loss networks. However, performance evaluation of call blocking probability in hierarchical wavelength routing optical networks has not yet been presented. In this paper, a kind of approximation method is developed for analyzing end to end call blocking probability in hierarchical wavelength routing optical networks. By iteratively running processes combining a top-down load distribution and a bottom-up calculation of node-pair blocking in the network hierarchy, a steady state will be reached and approximate end to end call blocking probability in hierarchical wavelength routing optical network is obtained. Both the link independent model and the link correlation model are used in our analysis. By comparing theoretical results derived from our approximation method and results obtained from discrete event simulation, we illustrate that our method gives reasonable estimate of end to end call blocking probability in hierarchical wavelength routing optical networks, with the correlation model giving much more accurate estimate than the independent model.

A New Method for Scheduling Divisible Data on a Heterogeneous Two-Levels Hierarchical System

A flat mobile ad hoc network has an inherent scalability limitation. When the network size increases, per node throughput of an ad hoc network rapidly decreases. This is due to the act that in large scale networks, flat structure of networks results in long hop paths which are prone to breaks. These long hop paths can be avoided by building a physically hierarchical backbone network. These networks have some specific backbone capable nodes that have powerful radios and are functionally more capable than ordinary nodes. In this paper, a hybrid routing protocol for large scale networks with mobile backbones has been proposed. This routing protocol uses different types of routing schemes in different layers of hierarchical network which makes it easily extendable to support QoS as well. Along with hierarchical structure, a low-overhead clustering scheme to elect backbone nodes has been proposed and works with our routing protocol without causing any extra overhead.

Animal research and human postmortem evidence highlight the importance of brain dopamine D3 receptor (D3R) function in multiple neuropsychiatric disorders, including addiction. Separate anatomical and functional neuroimaging findings implicate disrupted frontal cortical connectivity with distributed brain networks in processes relevant for these diseases. This potential conjunction between molecular and functional markers has not, however, been tested directly. Here, we used a novel combination of [(11)C]-(+)-PHNO positron emission tomography and resting-state functional magnetic resonance imaging in the same healthy individuals to investigate whether differences in midbrain D3R availability are associated with functional interactions between large-scale networks and regions involved in reward processing and cognition. High midbrain D3R availability was associated with reduced functional connectivity between orbitofrontal cortex (OFC) and networks implicated in cognitive control and salience processing. The opposite pattern was observed in subcortical reward circuitry and the "default mode" network, which showed greater connectivity with OFC in individuals with high D3R availability. These findings demonstrate that differential interactions between OFC and networks implicated in cognitive control and reward are associated with midbrain D3R availability, consistent with the hypothesis that dopamine D3R signaling is an important molecular pathway underlying goal-directed behavior.

The deployment of the new 5G system and ultra-broadband access networks, on one side, and the advent of network disaggregation and cloudification paradigms, on the other, are producing disruptive changes in metropolitan area networks (MANs). The new central offices will be more like small data centers than traditional Telco-type central offices. The presence of general-purpose servers, in partial or total replacement of specialized equipment or cards, and also hosting new functions and services, significantly changes the way the central office is organized and how the equipment internetworks. After having defined a reference architecture named cloudified-MAN, the paper briefly introduces the enabling technologies that include information technology (IT) systems, packet switches, and optical equipment. A disaggregated model is applied to all layers and systems. In the optics field, special insight on high data rate (up to 1.6 Tb/s) transceiver technology developed by the TERIPHIC project is provided as an example of low-cost and low-power technology. Through a quantitative techno-economic case study on a realistic network scenario aimed at short-term and medium-term time horizons, the paper provides indications on what could be the impact of the new architecture on costs and energy consumption. The main outcome of the study is that the IT and optical parts are those with the highest fractions of overall cost, and specifically for the optics, the cost of wavelength division multiplexing transceivers increases significantly in the medium term due to the demand for high-capacity connections ( > 400 G b / s ), while the line system cost (reconfigurable optical add-drop multiplexer) decreases in percentage. Another significant result is that in terms of energy consumption, the IT component has been found to be the dominant one (70%–75% of the total), followed by packet devices (about 20%–25%) and optical systems, the latter with significantly lower percentage values (less than 5%) in all cases analyzed.

In simulating large-scale networks, due to the limitation of available resources on computers, the size of the networks and the scale of simulation scenarios are often restricted. Especially, routing tables, which indicate the directions to forward packets, are considered to consume memory space. A simple general routing table requires O(N/sup 2/) space where N is the number of nodes. An algorithmic routing approach recently proposed by Heung et al. only requires O(N) space for representing routing tables, however this can be applied in the case that all the routes between two nodes are contained in a spanning tree (i.e. very limited routing strategies are allowed). We propose a new method to reduce the size of routing tables under any routing strategy. Given a general simple routing table, our method represents a routing table as the combination of an algorithmic routing based table and a general routing table, by translating a part of the given general routing table into the algorithmic routing based one. In order to reduce the size of the routing table, we find a (near-optimal)algorithmic routing based table that represents most part of the given routing table. Our experimental results have shown that our method could reduce the size of the table to 10 % of the given routing table in hierarchical networks.

In Software Defined Networks (SDN), reliability plays a major role in maintaining a consistent end-to-end connectivity and dealing with controller failures. A traditional SDN implementation relies on a central controller that runs the control plane. However, in a large-scale Wide Area Network (WAN) deployment, centralized approach has several limitations related to performance and scalability. To address these issues, distributed SDN where multiple controllers that work cooperatively to control a network are deployed. One key limitation of such a distributed network is failure of one or more controllers may cause severe issues like network outage and reduced Quality of Service (QoS). In this paper, we address the problem of controller failure in distributed SDN by proposing two controller failover techniques, namely (a) RandomWeightLoadBalancing and (b) ProgressiveAssignmentLoadBalancing. Our work focuses on load balancing the orphan switches, connected to a failed controller, among available controllers rather than overloading a single controller with all orphan switches. Simulation results show that our proposed solutions perform a successful reassignment of orphan switches and ensure that the load of each controller is well balanced.

Entangled states shared among distant nodes are frequently used in quantum network applications. When quantum resources are abundant, entangled states can be continuously distributed across the network, allowing nodes to consume them whenever necessary. This continuous distribution of entanglement enables quantum network applications to operate continuously while being regularly supplied with entangled states. Here, we focus on the steady-state performance analysis of protocols for continuous distribution of entanglement. We propose the virtual neighborhood size and the virtual node degree as performance metrics. We utilize the concept of Pareto optimality to formulate a multiobjective optimization problem to maximize the performance. As an example, we solve the problem for a quantum network with a tree topology. One of the main conclusions from our analysis is that the entanglement consumption rate has a greater impact on the protocol performance than the fidelity requirements. The metrics that we establish in this paper can be utilized to assess the feasibility of entanglement distribution protocols for large-scale quantum networks.

We consider the distributed network routing problem in a large-scale hierarchical network whereby the nodes are partitioned into subnetworks, each managed by a network controller (NC), and there is a central NC to coordinate the operation of the distributed NCs. We propose a semi-asynchronous routing algorithm for such a network, whereby the computation is distributed across the NCs and is parallel within each NC. A key feature of the algorithm is its ability to handle a certain degree of asynchronism: the distributed NCs can perform their local computation asynchronously at different processing speed. The efficiency of the proposed algorithm is validated through numerical experiments.

Wireless sensor networks (WSN) will witness a huge growth in the near future and both the academia and industry have commenced intensive research. Routing protocol design in WSN is one of the most important areas of active research due to the open issues and its importance. WSNs usually contain a large number of nodes typically with highly correlated collected data. These networks can be categorized according to the network structure and their protocol operations. Network structure based categorization of WSN can be flat or hierarchical. In flat networks, all the nodes in the networks take the same responsibility, while in hierarchical networks the cluster-heads perform several special functions such as maintaining the clusters and aggregation. To support large-scale wireless sensor network management and local data aggregation, hierarchical routing techniques can be regarded as superior to flat routing approaches. Low power consumption as well as smart way of distributing the load is crucial to the routing protocol design in order to attain elongated WSN lifetime. We have already proposed a distributed routing algorithm; optimized forwarding by fuzzy inference systems (OFFIS) for the flat networks, where the decision is based on the distance power and link uses. In addition, we have proposed a two-layer OFFIS (2L-OFFIS) for environmental data collection in cluster-based sensor networks. In this talk, major routing protocols for the WSNs will be presented and the results will be compared with those that are achievable by the OFFIS and 2L-OFFIS techniques. Simulation results show that the network lifetime can be significantly elongated by utilizing the new protocol in hierarchical sensor networks.

Large scale 1580 nm-band WDM network over dispersion-shifted fiber employing add-drop multiplexers is demonstrated for the first time. By shifting the signal wavelength region to 1580 nm, the limitation due to four wave mixing can be avoided, which enables us to construct a 1200 km large scale network with the large node spacing of 120 km.

Due to the effectiveness, simple deployment and low cost, radio frequency identification (RFID) systems are used in a variety of applications to uniquely identify physical objects. The operation of RFID systems often involves a situation in which multiple readers physically located near one another may interfere with one another’s operation. Such reader collision must be minimized to avoid the faulty or miss reads. Specifically, scheduling the colliding RFID readers to reduce the total system transaction time or response time is the challenging problem for large-scale RFID network deployment. Therefore, the aim of this work is to use a successful multi-swarm cooperative optimizer called PS2O to minimize both the reader-to-reader interference and total system transaction time in RFID reader networks. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarm model by constructing hierarchical interaction topology and enhanced dynamical update equations. As the RFID network scheduling model formulated in this work is a discrete problem, a binary version of PS2O algorithm is proposed. With seven discrete benchmark functions, PS2O is proved to have significantly better performance than the original PSO and a binary genetic algorithm. PS2O is then used for solving the real-world RFID network scheduling problem. Numerical results for four test cases with different scales, ranging from 30 to 200 readers, demonstrate the performance of the proposed methodology.

Bayesian network learning is a useful tool for exploratory data analysis. However, applying Bayesian networks to the analysis of large-scale data, consisting of thousands of attributes, is not straightforward because of the heavy computational burden in learning and visualization. In this paper, we propose a novel method for large-scale data analysis based on hierarchical compression of information and constrained structural learning, i.e., hierarchical Bayesian networks (HBNs). The HBN can compactly visualize global probabilistic structure through a small number of hidden variables, approximately representing a large number of observed variables. An efficient learning algorithm for HBNs, which incrementally maximizes the lower bound of the likelihood function, is also suggested. The effectiveness of our method is demonstrated by the experiments on synthetic large-scale Bayesian networks and a real-life microarray dataset.

Wireless sensor networks (WSNs) usually contain a large number of nodes typically with highly correlated collected data. To support large-scale wireless sensor network management and local data aggregation, hierarchical routing techniques can be regarded as superior to flat routing approaches. Low power consumption as well as smart way of distributing the load is crucial to the routing protocol design in order to attain elongated WSN lifetime. We have already proposed a distributed routing algorithm; optimized forwarding by fuzzy inference systems (OFFIS) for the flat networks, where the decision is based on the distance power and link uses. In this paper, we propose a two-layer OFFIS (2L-OFFIS) for environmental data collection in cluster-based sensor networks. Simulation results show that the network lifetime can be significantly elongated by utilizing the new protocol in hierarchical sensor networks