Link Failures In Networks Research Articles

Efficient Integration of Logical Topology Design and Survivable Traffic Grooming for Throughput Enhancement in WDM Optical Networks

Optical networks that employ traffic grooming, merge several low-speed traffic streams onto a high-speed lightpath. A fiber cut (or a link failure) in such networks can disrupt a large number of lightpaths and can cause a huge amount of data loss. Designing survivability schemes to make the network withstand a link failure is thus vital. In this paper we address the problem of survivable traffic grooming in a static scenario. In a static scenario the primary objective is to maximize throughput with the available network resources. Since the problem is NP-Complete, we propose an efficient heuristic TATG (Throughput Aware Traffic Grooming) for the problem. TATG employs an efficient approach of integrating logical topology design and survivable traffic grooming with heuristics at sub-problem level. Our approach ensures that the logical topology carrying the traffic always stay connected in the event of a link failure. Time complexity analysis shows that our heuristic runs in polynomial time. We study two separate forms of survivability in heuristics TATG-SC (TATG -Survivability at Connection) and TATG-SL (TATG-Survivability at Lightpath) with three categories of traffic demand. Performance comparison with a well known heuristic shows that the proposed approach provides much better throughput performance when resources are scarce. The average percentage decrease in request blocking over all experiments obtained using TATG-SC and TATG-SL is 54.87% and 59.57% respectively.

ACO-based enhanced energy-efficient intelligent routing protocol for MANET

Mobile ad hoc Network (MANET) consists of a group of mobile nodes and the communication among them is done without any supporting centralised structure. Routing in MANETs is a difficult because of their dynamic features such as high mobility, constrained bandwidth and link failures due to energy loss. The objective of this work is to implement an intelligent routing protocol by selecting efficient nodes for routing the information. To provide good throughput in the network, Ant Colony Optimisation (ACO) based enhanced energy-efficient intelligent routing is proposed. Selecting the best intermediate hop for intelligent routing includes ACO technique which greatly reduces the network delay and link failures by validating the coordinator nodes. Coordinator nodes are picked by validating their metrics and Best Coordinator Nodes (BCN) are selected among them as good intermediate hops for intelligent routing. The performance is evaluated for the proposed protocol and the metrics considered for evaluation are delivery and loss rate of sent data, throughput and lifetime of the network, delay and energy consumption.

ACO-based enhanced energy-efficient intelligent routing protocol for MANET

Mobile ad hoc Network (MANET) consists of a group of mobile nodes and the communication among them is done without any supporting centralised structure. Routing in MANETs is a difficult because of their dynamic features such as high mobility, constrained bandwidth and link failures due to energy loss. The objective of this work is to implement an intelligent routing protocol by selecting efficient nodes for routing the information. To provide good throughput in the network, Ant Colony Optimisation (ACO) based enhanced energy-efficient intelligent routing is proposed. Selecting the best intermediate hop for intelligent routing includes ACO technique which greatly reduces the network delay and link failures by validating the coordinator nodes. Coordinator nodes are picked by validating their metrics and Best Coordinator Nodes (BCN) are selected among them as good intermediate hops for intelligent routing. The performance is evaluated for the proposed protocol and the metrics considered for evaluation are delivery and loss rate of sent data, throughput and lifetime of the network, delay and energy consumption.

Link failure in wheel type networks

Link residual closeness (LRC) is a new sensitive characteristic for network vulnerability analysis. LRC measures the vulnerability even when the removal of links does not disconnect the network. In this paper, the robustness of wheel type network topologies is modeled and analyzed via LRC in the face of possible link destruction.

Time‐varying network link loss rate tracking from unicast end‐to‐end measurements

SummaryTime‐varying network link loss rate is a useful information for network managers to discover and locate the network link failures. This paper proposes a method to track time‐varying network link loss rates from unicast end‐to‐end measurements. The method first trains a state transition matrix to capture the spatio‐temporal characters of packet link transmission probabilities by sending end‐to‐end probe packets and then estimates the time‐varying link loss rates using the state transition matrix and the end‐to‐end measurements obtained from background traffic (the existed packets in network). We also introduce a validation step to check and retrain the state transition matrix in order to ensure the accuracy of the state transition matrix. Our method is capable of tracking the variation of link loss rates without incessantly sending probe packets, which is very feasible for many practical applications. The ns‐2 simulation results show the good performance of our method.

Improved Bounds for Max Consensus in Wireless Networks

In consensus problems, the goal is for the nodes of a network to converge to a certain quantity or a function of their values using local communications. In the maximum value consensus problem, the objective of these communications is for all the nodes to converge to the maximum of their initial values. There are two existing algorithms for the maximum value consensus problem in asynchronous networks: Random-Pairwise-Max and Random-Broadcast-Max for which the bounds on the mean convergence time have been derived in the literature. In this paper, we derive tighter bounds on the expected convergence time of these two algorithms when run on grid networks and random geometric graphs, respectively—two models commonly used to capture salient properties of wireless networks. We show that Random-Pairwise-Max run on a 2-D grid graph with $n$ nodes converges in expectation after $\mathcal {O}(n^{3/2})$ iterations, and Random-Broadcast-Max run on a random geometric graph with $n$ nodes converges in expectation after $\mathcal {O}((n / \log n)^{3/2})$ iterations. These bounds improve over the previous best-known upper bounds by factors of $\sqrt{n}\log n$ and $\log n + \log ^2 n$ , respectively. Experiments illustrate that the proposed bounds can be up to $95\%$ tighter than the previous state-of-the-art bounds. Furthermore, we enhance the proposed bounds by introducing probabilistic network link failures, e.g., to model packet drops in wireless networks.

Non-local impact of link failures in linear flow networks

The failure of a single link can degrade the operation of a supply network up to the point of complete collapse. Yet, the interplay between network topology and locality of the response to such damage is poorly understood. Here, we study how topology affects the redistribution of flow after the failure of a single link in linear flow networks with a special focus on power grids. In particular, we analyze the decay of flow changes with distance after a link failure and map it to the field of an electrical dipole for lattice-like networks. The corresponding inverse-square law is shown to hold for all regular tilings. For sparse networks, a long-range response is found instead. In the case of more realistic topologies, we introduce a rerouting distance, which captures the decay of flow changes better than the traditional geodesic distance. Finally, we are able to derive rigorous bounds on the strength of the decay for arbitrary topologies that we verify through extensive numerical simulations. Our results show that it is possible to forecast flow rerouting after link failures to a large extent based on purely topological measures and that these effects generally decay with distance from the failing link. They might be used to predict links prone to failure in supply networks such as power grids and thus help to construct grids providing a more robust and reliable power supply.

Holarchic structures for decentralized deep learning: a performance analysis

Structure plays a key role in learning performance. In centralized computational systems, hyperparameter optimization and regularization techniques such as dropout are computational means to enhance learning performance by adjusting the deep hierarchical structure. However, in decentralized deep learning by the Internet of Things, the structure is an actual network of autonomous interconnected devices such as smart phones that interact via complex network protocols. Self-adaptation of the learning structure is a challenge. Uncertainties such as network latency, node and link failures or even bottlenecks by limited processing capacity and energy availability can significantly downgrade learning performance. Network self-organization and self-management is complex, while it requires additional computational and network resources that hinder the feasibility of decentralized deep learning. In contrast, this paper introduces a self-adaptive learning approach based on holarchic learning structures for exploring, mitigating and boosting learning performance in distributed environments with uncertainties. A large-scale performance analysis with 864,000 experiments fed with synthetic and real-world data from smart grid and smart city pilot projects confirm the cost-effectiveness of holarchic structures for decentralized deep learning.

SDN Programming for Heterogeneous Switches with Flow Table Pipelining

High-level programming is one of the critical building blocks of the effective use of software-defined networking (SDN). Existing solutions, however, either (1) cannot utilize the state-of-the-art switches with flow table pipelining, a key technique to prevent flow rule set explosion or (2) force programmers to manually organize and manage hardware flow table pipelines, which is time-consuming and error-prone. This paper presents a high-level SDN programming framework to address these issues. The framework can automatically (1) generate rule sets for heterogeneous switches with different flow table pipelining designs and (2) update installed rules when the network state changes. As a result, the framework can not only generate efficient rule sets for switches but also provide programmers a centralized, intuitive, and hence easy-to-use programming API. Experiments show that the framework can generate compact rule sets that are 29–116 times smaller than those generated by other open-source SDN controllers. Besides, the framework is 5 times faster to recover from network link failures in comparison to other controllers.

Distributed sparse diffusion estimation with reduced communication cost

The issue considered in the current study is the problem of adaptive distributed estimation based on diffusion strategy which can exploit sparsity in improving estimation error and reducing communications. It has been shown that distributed estimation leads to a good performance in terms of the error value, convergence rate, and robustness against node and link failures in wireless sensor networks. However, the main focus of many works in the field of distributed estimation research is on convergence speed and estimation error, neglecting the fact that communications among the nodes require a lot of transmissions. In this work, the focus is on a solution based on sparse diffusion least mean squares (LMS) algorithm, and a new version of sparse diffusion LMS algorithm is proposed which takes both communications and error cost into account. Also, the computation complexity and communication cost for every node of the network, as well as performance analysis of the proposed strategy, is provided. The performance of the proposed method in comparison with the existing methods is illustrated by means of simulations in terms of computational and communicational cost, and flexibility to signal changes.

Blocking Probability Analysis for Optical Banyan Networks With Link Failure

Highly fault-tolerant optical switch is becoming a critical network element for large wavelength division multiplexing mesh networks. Optical nodes built on replicated banyan network (RBN) structures are attractive for serving as optical switching systems due to the nice properties of banyan network structures. Furthermore it is expected that an optical RBN structure also has a good fault-tolerant capability because it consists of multiple identical banyan networks. Previous analytical models have been developed to understand the blocking behaviors of optical RBN networks without taking into account the link failure in optical RBN networks. In this paper, we conduct the link failure-tolerant analysis of optical RBN networks and present an analytical model for the blocking probability of optical RBN networks when link failures are taken into account. The model is significant because it provides network developers a quantitative guidance to determine the effects of network failure on the overall blocking behaviors of optical RBN networks and enables a desirable tradeoff to be made between blocking probability and hardware cost in a faulty optical RBN network.

A Multi-Scenario Probabilistic Simulation Approach for Critical Transportation Network Risk Assessment

The reliability, survivability and vulnerability of critical infrastructure systems has received significant attention of the past several decades. Transportation systems are among the many critical lifelines that urban areas and their associated communities are dependent upon. Disruptions to these systems have the potential to create significant human suffering and severe economic damage. As a result, the ability to proactively assess response and recovery options is critical for emergency preparedness in urban areas. Unfortunately, the vast majority of infrastructure disruption studies are deterministic in nature, exploring the impact of node and arc losses with pre-defined interdiction scenarios and ignoring the underlying dynamism of extreme events. We advance existing knowledge by presenting a probabilistic approach for simulating a range of disruption scenarios and for identifying critical links within the network. Specifically, our approach takes advantage of Monte Carlo simulation, network-wide demand modeling and regression analysis to address the probabilistic nature of disaster effects and the joint impacts of network link failures. Using Salt Lake County, Utah as the study area, the resulting analysis effectively identifies and ranks links based on their vulnerability and criticality. The proposed method is easily transferable to any transportation network, regardless of scale, topology or extreme event.

Neural network based automated detection of link failures in wireless sensor networks and extension to a study on the detection of disjoint nodes

In the broad research area of wireless sensor networks (WSN), detection of link failure is still in its infancy. In this paper, we propose to use a neural network model for detection of link failure in WSN. The neural network has been allowed to learn and adapt with the help of gradient descent based learning algorithm. We demonstrate the proposed model with regard to the preparation of training data and implementation of the model. This paper also provides a thorough theoretical and analytical investigation of link failures in WSN. The proposed neural network based model has been evaluated carefully with regard to testbed experiments. The simulation-based experiment has been conducted to justify the applicability of the proposed model for dense networks that could contain around 1000 links. We also analyze the theoretical performance of the proposed neural network based algorithm with regard to various performance evaluative measures such as failure detection accuracy, false alarm rate. The simulated experiments, as well as the testbed experiments in indoor and outdoor environments, suggest that the method is capable of link failure detection with higher detection rate and it is consistent. Furthermore, this article also reports a comprehensive case study as an extension of this present research towards automated detection of disjoint and disconnected nodes in a sensor network.

Classification approach to avoid link failures in wireless sensor networks in mobile virtual communities and teleworking

Security issues are the primary issues in wireless sensor networks (WSN) due to its large network coverage and number of nodes. The attackers in WSN attack the particular node which has a low energy level and converts this node into malicious node. The formation of malicious node is the primary reason for link failures between nodes. This paper proposes an efficient methodology to detect the malicious nodes in WSN using feed forward back propagation neural network classifier. This classifier differentiates the malicious node from trusty node based on the extracted features of the test node. The performance of the proposed malicious node detection system is analysed in terms of detection rate, packet delivery ratio (PDR) and latency. The experimental results are compared with state-of-art methods.

Classification approach to avoid link failures in wireless sensor networks in mobile virtual communities and teleworking

Security issues are the primary issues in wireless sensor networks (WSN) due to its large network coverage and number of nodes. The attackers in WSN attack the particular node which has a low energy level and converts this node into malicious node. The formation of malicious node is the primary reason for link failures between nodes. This paper proposes an efficient methodology to detect the malicious nodes in WSN using feed forward back propagation neural network classifier. This classifier differentiates the malicious node from trusty node based on the extracted features of the test node. The performance of the proposed malicious node detection system is analysed in terms of detection rate, packet delivery ratio (PDR) and latency. The experimental results are compared with state-of-art methods.

TFR: A Novel Approach for Clock Synchronization Fault Recovery in Precision Time Protocol (PTP) Networks

Accurate and precise clock synchronization is one of the fundamental requirements for various applications, such as telecommunication systems, measurement and control systems, and smart grid systems. Precision time protocol (PTP) was designed and specified in IEEE 1588 to meet that requirement. PTP provides a mechanism for synchronizing the clocks in a PTP system to a high degree of accuracy and precision based on exchange synchronization messages through a master–slave hierarchy. The best master clock (BMC) algorithm is currently used to establish the master–slave hierarchy for PTP. However, the BMC algorithm does not provide a fast recovery mechanism in case of master failures. The accuracy and precision of the PTP clocks could be deteriorated by the occurrence of failure in the network (link or node failure). These fault occurrences will affect network performance and reliability, and cause clock time drifting of the PTP nodes. In this paper, we present a novel approach, called timing fault recovery (TFR), to significantly reduce clock time drifting in PTP systems. TFR detects the fault occurrence in the network and recovers it by using a handshake mechanism with a short duration. Therefore, the TFR approach provides clock stability and constancy and increases the reliability and the availability of PTP systems. The performance of TFR has been analyzed and compared to that of the standard PTP. Various simulations were conducted to validate the performance analysis. The results show that, for our sample network, the TFR approach reduces clock drifting by 90% in comparison to the standard PTP, thus providing better clock firmness and synchronization accuracy for PTP clocks.

Rapid recovery from link failures in software-defined networks

Carrier-grade networks (CGNs) can leverage the network programmability of software-defined networking (SDN) to ensure fast recovery and high availability. However, for the successful adoption of SDN, the failure recovery requirement must be addressed. Local detouring is a popular approach for faster recovery rather than path-based end-to-end recovery. For fast local recovery, alternate paths must be preinstalled for each individual flow on the link, which in some cases results in storing thousands of alternate path flow rules. Furthermore, the dependence on the controller for dynamic per-flow detouring may delay the recovery. In this paper, we propose local immediate (LIm) and immediate controller dependent (ICoD) recovery mechanisms to address the limitations of OpenFlow-based link recovery approaches. Our proposed mechanisms considerably reduce the alternate path flow rules by aggregating the disrupted flows using virtual local area network (VLAN) tagging. The proposed algorithms achieve recovery within 3 ms and 20 ms, respectively and satisfy the strict 50 ms recovery requirement of CGNs. LIm and ICoD also reduce the alternate path flow storage requirement by up to 99%. Simulation results reveal that the flow-aggregation also reduces the effort of the controller and minimizes the alternate path installation traffic.

A Novel Dedicated Route Protection Scheme for Survivability of Link Failure in Elastic Optical Networks

The spectrally efficient transportation of the high bit rate(s) data is achievable by the Elastic optical networks (EONs). However, in the EONs, owing to the failure occurrence even in an individual simple element, different service(s) maybe interrupted. Hence, it is imperative that the schemes for survivability be developed so that the issues due to the possible failure(s) can be overcome. In the current work, in view of survivability of the link failure(s) in the EONs, we propose the Spectrum Continuity and Contiguity Established DRP (SCC-E-DRP) algorithm which is a novel dedicated route protection (DRP) scheme that attempts to avoid the problem of trap topology during its exploration for a pair of link disjoint path. Further, to evaluate the link disjoint paths, we resort to the use of the SCC Established Shortest Route (SCC-E-SR) algorithm which is a modified Dijkstra’s algorithm based scheme that selects the path(s) pair(s) based on the end-toend SCC. We conduct extensive simulations considering realistic network topologies, and compare the performance of the SCCE-DRP scheme with the existing techniques. The obtained results show that, compared to the existing schemes, the SCC-E-DRP scheme achieves better results in terms of blocking probability.

Elastic Versus WDM Networks With Dedicated Multicast Protection

We consider the routing and spectrum allocation (RSA) problem of protecting all-optical multicast sessions against a single link failure in elastic optical networks (EONs). A tree is derived to support a multicast session and can be considered as a set of paths originating from a common source. We use a scheme to protect each of the paths by an arc-disjoint backup path in the case of any link failure. For this problem, we provide a node-arc integer linear programming (ILP) formulation and propose a heuristic algorithm for this protection scheme. We evaluate the improvement achieved by the flexible grids adopted in EONs over the fixed-grid wavelength-division multiplexing (WDM) networks. The evaluation is done by considering static modeling based on ILP and heuristics as well as dynamic simulations. We consider two approaches for heuristic algorithms: one is a two-step (TS) approach that computes the routing trees then allocates spectrum. The other uses the spectrum window plane (SWP) and constructs auxiliary graphs for the relevant range of spectrum to jointly solve the RSA problem. We evaluate the performances of the proposed algorithms under static and dynamic traffic models. Based on the studied scenarios, we observe consistent improvement in efficiency of flexible grid EONs over fixed-grid WDM networks. Moreover, we demonstrate that the network size (likely because of the availability of more alternative routes) can significantly affect the benefit in efficiency achieved by the SWP-based approaches over the TS approaches.

Loop-Free Convergence With Unordered Updates

This paper studies the feasibility of minimizing convergence delay and forwarding disruption without carrying any additional bits in the IP header, to provide high availability despite link failures in traditional IP networks. Previously proposed mechanisms achieve two of these three objectives by trading off the other objective. For instance, the ordered forwarding information base updates approach may prolong the convergence delay, whereas the SafeGuard scheme requires carrying the path cost in the IP header. As a better alternative, we propose a scheme called fast convergence with fast reroute (FCFR), which combines the features of IP fast rerouting and interface-specific forwarding. We show that FCFR can achieve minimal convergence delay, while ensuring loop-free delivery during convergence, after a single non-partitioning failure in an IP network, without altering the IP header format, making it amenable for immediate deployment.