Link Failure Research Articles

TIVC: An Efficient Local Search Algorithm for Minimum Vertex Cover in Large Graphs.

The minimum vertex cover (MVC) problem is a canonical NP-hard combinatorial optimization problem aiming to find the smallest set of vertices such that every edge has at least one endpoint in the set. This problem has extensive applications in cybersecurity, scheduling, and monitoring link failures in wireless sensor networks (WSNs). Numerous local search algorithms have been proposed to obtain "good" vertex coverage. However, due to the NP-hard nature, it is challenging to efficiently solve the MVC problem, especially on large graphs. In this paper, we propose an efficient local search algorithm for MVC called TIVC, which is based on two main ideas: a 3-improvements (TI) framework with a tiny perturbation and edge selection strategy. We conducted experiments on real-world large instances of a massive graph benchmark. Compared with three state-of-the-art MVC algorithms, TIVC shows superior performance in accuracy and possesses a remarkable ability to identify significantly smaller vertex covers on many graphs.

Protection Techniques using Resource Delayed Release for SDN-based OTN over WDM Networks

The availability and reliability of optical backbone links are very important to ensure the efficient operation of the Internet. To address the issue of data loss due to optical link failures, there is a need for an optimal recovery strategy so that the traffic can be rerouted on a backup path to the destination. This paper builds on top of our prior research efforts (Yavary Mehr et al., 2022; Zhou et al., 2017) which introduced the concept of Resource Delayed Release (RDR) by adding a new state called ”idle state” which begins when the channel has completed carrying its services so that the next request can be carried immediately instead of waiting for a new channel to be established. While RDR improves the network performance by reducing the service provisioning time and blocking probability, it does not handle link failures which are quite common in optical networks. Therefore, enhancing RDR with protection strategies will make the network more reliable and thus we investigate this topic in this work.In this paper, we evaluate four different protection methods for single link failure recovery in WDM networks (Path Protection (PP), Partial Path Protection (PPP), Segment Protection (SegP) and Link Protection (LP)) with two different routing approaches namely Shortest Path (SPath) and Greedy (G) algorithm under uniform and non-uniform traffic generated using real traffic traces collected from a local Internet Service Provider (ISP). Special attention while evaluating these protection strategies was paid to the optimization of the amount of remaining bandwidth. The performance evaluation of the network under uniform and non-uniform traffic was done over the NSFNet and COST239 topologies by employing the metrics of link and network utilization, Blocking Probability (BP), Bandwidth Blocking Probability (BBP), Recovery Time (RT) and Service Provisioning Time (SPT). Our results show that the PPP method performs the best in terms of reducing BP, BBP, and SPT compared with PP, LP, and SegP in all three topologies while utilizing RDR.

Vertex and Link Residual Closeness of Graphs of Given Fractional Matching Number

Vertex and link residual closeness are two new measures of network vulnerability due to the failure of individual vertices and links, respectively. We identify those graphs with maximum vertex/link residual closeness over all [Formula: see text]-vertex connected graphs with fixed fractional matching number.

Efficient Distance and Connectivity Based Traffic Density Stable Routing Protocol for Vehicular Ad Hoc Networks

Vehicle ad-hoc networks (VANETs) have become a prominent research topic in recent years due to rapid dynamic topology, high vehicle mobility, frequent link failures and significant delay constraints. The mobility of the vehicle nodes increases, and the overhead of control traffic due to the high dynamics of the VANET. Efficient routing algorithms are necessary for VANETs to ensure reliable transmission (VANETs). This work proposes a Traffic Density Stable Routing Protocol based on Connection- and Distance (TDSRP-DC) to avoid data packet collisions at intersections and an adaptive routing schedule based on the selection at every instant. Our approach is based on vehicle-to-vehicle communication. Ground vehicles identify the most appropriate next junction and transfer the data packet to the receiver to find the optimal multi-hop route. It relies on transferring the data between all vehicles to estimate real-time traffic fluctuations sporadically. Network formation, neighbor realization, fitness value prediction, and routing methodology are the conceptual process needed in this research. Valid parameters for finding the optimum path in the conceptual model include node distance, node speed, node azimuth, link stability, and link reliability. The suggested solution’s simulation was performed in comparison to traditional algorithms. Compared to existing TFOR algorithms on complex traffic, the proposed TDSRP-DC provides tremendous improvements based on packet delivery rate (10%) and performance (35%). The demonstrated results and comparisons illustrate the proposed routing protocol’s significance with enhanced quality of service performance.

Multiagent Q-Learning-Based Mobility Management for Multi-Connectivity in mmWAVE Cellular Systems.

Effective mobility management is crucial for efficient operation of next-generation cellular systems in the millimeter wave (mmWave) band. Massive multiple-input-multiple-output (MIMO) systems are seen as necessary to overcome the significant path losses in this band, but the highly directional beam makes the channels more susceptible to radio link failures due to blockages. To meet stringent capacity and reliability requirements, multi-connectivity has attracted significant attention. This paper proposes a multiagent distributed Q learning-based mobility management scheme for multi-connectivity in mmWave cellular systems. A hierarchical structure is adopted to address the model complexity and speed up the learning process. The performance is assessed using a realistic measurement data set collected from Wireless Insite in an urban area and compared with independent Q learning and a heuristic scheme in terms of handover probability and spectral efficiency.

Efficient Routing in VANETs Using MRRP Algorithm

Designing a reliable routing protocol for Vehicular Ad hoc Network (VANET) poses considerable challenges due to certain unique challenges inherently present in Vehicular Ad hoc Network (VANET) topology. Some of them are needed for vehicles acting as nodes having to abide by traffic rules, uncertain inter-vehicular speed variations that may affect link stability etc. Designing a routing protocol capable of dealing with multiple limiting conditions such as long congestion periods, link failures and handoffs is a challenging task, where most of the existing multipath routing protocol shows poor performance. In this paper, the proposed Multipath Route Restoration Protocol (MRRP)is aimed at providing a robust communication channel in case of link failure between nodes. This is realized by focusing on better route maintenance for the protocol. In a wireless network, a routing protocol determines the particular ways in which routers connect. In a wireless network, as the number of hops in a wireless communication path increases, various signal factors such as interference and path loss degrade the network performance. however, sending data over a longer distance will reduce throughput. Furthermore, link stability is substantially impacted by the unpredictable movement of vehicles. Multipath routing is regarded as a potential solution to improve packet delivery and end-to-end delay in VANETs.

Design of an In-Band Control Plane for Automatic Bootstrapping and Fast Failure Recovery in P4 Networks

In this paper, we present the design of an in-band control plane system for P4 networks. The proposed in-band control plane performs automatic switch bootstrapping, network status monitoring, topology discovery and fast failover. The authentication and registration of newly connected switches are managed by the controller without manual intervention. The system can recover from any single link or single node failure in a short period of time. The recovery control paths for each single link and single node failure are prepared in advance. We propose an algorithm that takes recovery time into account for preplanning the recovery paths. In addition to handling any single failure, the proposed control plane is survivable even if there are multiple failures in the network. We implement an emulated network to evaluate the proposed control plane system. The experimental results show that the system successfully provides all the target functions.

Failure Detector Based on Vehicle Movement Prediction in Vehicular Ad-Hoc Networks

Vehicular ad hoc network (VANETs) improves road safety and efficiency by organizing vehicles and infrastructure to provide a platform for application deployment. The availability of vehicles and infrastructure is critical to the operation of applications. Accurate failure detector (FD) has been one of the fundamental components for maintaining high availability in VANETs. However, it is hard to find the vehicle failure accurately and timely due to the dynamic nature of VANETs caused by the high mobility of vehicles and communications link failures. Therefore, it is important to achieve an accurate FD which can cope with the high mobility of VANETs. In this paper, we propose a dead reckoning based FD, called DR-FD. It can predict the mobility of vehicle accurately and avoid the impact of link failures on the detection results by the cooperation between vehicles. Experimental results are provided to confirm that the proposed DR-FD method can achieve at most 20% reduction in detection time, 30% improvement in mistake rate and 20% improvement in overhead.

Path Diversity and Survivability for the HyperX Datacenter Topology

Network survivability has been recognized as an issue of a major importance in terms of security, stability and prosperity. This paper studies fundamental properties of HyperX, an emerging topology for connecting supercomputing and datacenter networks. We focus on the establishment of paths with guaranteed survivability, allowing path availability even upon a restricted number of link failures. We first examine the availability of disjoint paths connecting a pair of input nodes. Disjoint paths guarantee path existence even upon a bounded number of link failures. We explore the inherent tradeoff between allowing slightly longer paths and the ability to extend available sets of mutually disjoint paths. Second, we study the availability of paths in a HyperX topology that already observed link failures. Such failures can increase the length of available paths or even eliminate connectivity between network parts. We also compare basic properties of shortest paths in HyperX to other datacenter topologies. Last, we provide an evaluation to illustrate path availability along with the potential impact of failures.

InFaRR: In-Network Fast ReRouting

InFaRR (In-network Fast ReRouting) is an algorithm for fast rerouting in programmable data planes. Implemented in P4, InFaRR is free of additional headers and network state heartbeats. InFaRR has four essential features not jointly found in other recovery mechanisms: Loop prevention, Pushback, Recognition and Restoration and Return to the main route. Tests in standard Fat-Tree and AB Fat-Tree topologies with failures in different scenarios showed positive results when compared to state-of-the-art algorithms in the literature. In scenarios in which the other algorithms were able to recover, InFaRR presented fewer hops to bypass the failure when the Pushback, Loop Prevention and Recognition and Restoration mechanisms were used. In scenarios with multiple failures, InFaRR successfully rerouted where the others algorithms in some cases looped. The mechanism for returning to the main route allows to verify failures in remote links, making it possible to return to the main route without intervention from the control plane. Several evaluations were done comparing the results of InFaRR with the state-of-the art mechanisms, showing the capability of our fast rerouting algorithm in dealing with failures at the line-rate.

Minimum-Weight Link-Disjoint Paths With a Bounded Number of Shared Nodes

Network protection has drawn a certain interest in network optimization. One of the most effective and widely used methods to protect networks from failures is to establish backup paths for working paths. For example, we find k node-disjoint paths between a source and a sink with one working path and k-1 backup paths. However, the demand for full protection of a network is somewhat too restrictive and there may not exist k node-disjoint paths in the network due to the limitation of geographical environments. On the other hand, the occurrence probability of node failures is usually much less than that of link failures in real-world models. To save network resources, we turn to establish link-disjoint paths allowing a few shared nodes. We study the problem of finding k link-disjoint paths between a source and a sink under the constraint that the number of nodes shared by at least r paths is at most δ, minimizing the total link weight. First, we systematically study the computational complexity of the problem with respect to three parameters k, r, and δ. Then, we build an integer linear programming for the general model and design a polynomial-time algorithm for the case that k=r by using the techniques of augmenting paths and splitting nodes. Finally, we carry out experimentations on synthetic and real networks that show the effectiveness of our algorithms in practice.

A Fault‐tolerant model for tuple space coordination in distributed environments

SummaryIn distributed systems, tuple space is one of the coordination models that significantly maximizes system performance against failure due to its space and time decoupling features. With the growing popularity of distributed computing and increasing complexity in the network, host and link failure occurs frequently, resulting in poor system performance. This article proposes a fault‐tolerant model named Tuple Space Replication (TSR) for tuple space coordination in distributed environments. The model introduces a multi‐agent system that consists of multiple hosts. Each host in a multi‐agent system comprises an agent space with a tuple space for coordination. In this model, we introduce three novel fault‐tolerant algorithms for tuple space primitives to provide coordination among hosts with tolerance to multiple links and hosts failure. The first algorithm is given for out() operation to insert tuples in the tuple space. The second algorithm is presented for rdp() operation to read any tuple from the tuple space. The third algorithm is given for inp() operation to delete or withdraw tuples from the tuple space. These algorithms use less number of messages to ensure consistency in the system. The message complexity of the proposed algorithms is analyzed and found O(n) for out(), O(1) for rdp(), and O(n) for inp() operations which is comparable and better than existing works, where n is the number of hosts. The testbed experiment reveals that the proposed TSR model gives performance improvement up to 88%, 70.94%, and 63.80% for out(), rdp(), and inp() operations compared to existing models such as FT‐SHE, LBTS, DEPSPACE, and E‐DEPSPACE.

Optimized design of filterless horseshoe networks exploiting point-to-multipoint coherent transceivers

The horseshoe topology is widely used to realize metro-aggregation networks, since it provides a natural fit to the hub-and-spoke traffic pattern present in the majority of these deployments, while enabling survivability against single link and hub failures. A filterless architecture can also be adopted to further reduce capital expenditure (CapEx) by replacing active elements, such as reconfigurable add/drop multiplexers, with simpler and passive splitters/combiners. Such an architecture can effectively host coherent-based point-to-multipoint (P2MP) transceivers enabled by digital subcarrier multiplexing (DSCM). Importantly, by carefully optimizing the deployment of amplifiers (location and gain) and splitters/combiners (type), it may be possible to reduce the total number of optical amplifiers required, further decreasing CapEx. This paper proposes an integer linear programming framework to optimize metro-aggregation filterless horseshoe networks, taking into account the specific requirements of DSCM-based P2MP coherent transceivers. The results indicate that a considerable reduction in amplifier count is possible while ensuring that end-to-end performance thresholds are met, which include the minimum required input power at the receivers, a maximum subcarrier input power difference at the hub’s receivers, and the minimum optical-signal-to-noise ratio.

A robust distributed observer design for Lipschitz nonlinear systems with time-varying switching topology

This paper deals with state estimation for a class of Lipschitz nonlinear systems under a time-varying disconnected communication network. A distributed observer consists of some local observers that are connected to each other through a communication network. We consider a situation where a communication network does not remain connected all the time, and the network may be caused by intermittent communication link failure. Moreover, each local observer has access to a local measurement, which may be insufficient to ensure the system’s observability, but the collection of all measurements in the network ensures observability. In this condition, the purpose is to design a distributed observer where the estimated state vectors of all local observers converge to the state vector of the system asymptotically, while local observers exchange estimated state vectors through a communication network and use their local measurements. According to theoretical analysis, a nonlinear and a robust nonlinear distributed observer exist when in addition to the union of all communication topologies being strongly connected during a time interval, the component of each communication graph is also strongly connected during each subinterval. The existence conditions of the distributed observers are derived in terms of a set of linear matrix inequalities (LMIs). Finally, the effectiveness of the presented method is numerically verified using some simulation examples.

Scalable Deep Reinforcement Learning-Based Online Routing for Multi-Type Service Requirements

Emerging applications raise critical QoS requirements for the Internet. The improvements in flow classification technologies, software-defined networks (SDN), and programmable network devices make it possible to fast identify users' requirements and control the routing for fine-grained traffic flows. Meanwhile, the problem of optimizing the forwarding paths for traffic flows with multiple QoS requirements in an online fashion is not addressed sufficiently. To address the problem, we propose DRL-OR-S, a highly scalable online routing algorithm using multi-agent deep reinforcement learning. DRL-OR-S adopts a comprehensive reward function, an efficient learning algorithm, and a novel deep neural network structure to learn appropriate routing strategies for different types of flow requirements. In order to enhance the generalization and scalability, we propose a novel graph-based actor-critic network architecture and a carefully designed input state for DRL-OR-S. To accelerate the training process and guarantee reliability, we further introduce an NN-simulator for efficient offline training and a safe learning mechanism to avoid unsafe routes during the online routing process. We implement DRL-OR-S under SDN architecture and conduct Mininet-based experiments using real network topologies and traffic traces. The results validate that DRL-OR-S can well satisfy the requirements of latency-sensitive, throughput-sensitive, latency-throughput-sensitive, and latency-loss-sensitive flows at the same time, while exhibiting great adaptiveness and reliability under the scenarios of link failure, traffic change, unseen large topology and partial deployment.

FlexDATE: Flexible and Disturbance-Aware Traffic Engineering With Reinforcement Learning in Software-Defined Networks

Traffic Engineering (TE) is an important network operation that routes/reroutes flows based on network topology and traffic demands to optimize network performance. Recently, new emerging applications pose challenges to TE with dynamic network conditions, where frequent routing updates are required to maintain good network performance with Software-Defined Networking (SDN). However, flow rerouting operations could lead to considerable Quality of Service (QoS) degradation and service disruption, which is often neglected by existing TE solutions. In this paper, we apply a new QoS metric named network disturbance to measure the negative impact of flow rerouting operations performed by TE. To achieve near-optimal load balancing performance and mitigate network disturbance together in dynamic network scenarios, we propose a flexible and disturbance-aware TE solution called that combines Reinforcement Learning (RL) and Linear Programming (LP). Specifically, leverages RL to intelligently identify flexible numbers of critical flows for each traffic matrix and reroutes these critical flows based on LP optimization to improve network performance with low disturbance. Empowered by a customized actor-critic architecture coupled with Graph Neural Networks (GNNs), can generalize well to unseen traffic scenarios and remain resilient to single link failures. Extensive simulations are conducted on five real-world network topologies to evaluate with real and synthetic traffic traces. The results show that can achieve the performance target (i.e., 90% of optimal performance) in 99% of network scenarios and effectively mitigate the average and maximum network disturbance by up to 9.1% and 38.6%, respectively, compared to state-of-the-art TE solutions.

Traffic predictive-based flow splitting rerouting scheme for link failures in software-defined networks

This paper presents a traffic rerouting approach to keep a satisfactory QoS (Quality of Service) in virtualized network infrastructures (VPN, Cloud, etc.), supervised by an SDN (Software-Defined Networking) controller dealing with high traffic fluctuations. Traffic fluctuations are usually caused by link failures or link congestion and result in high packet loss, long delay times, and high jitter. These metrics are critical in computer network's QoS assessing. In our strategy, we combine traffic prediction in the SDN controller with flow splitting in the data plane. Simulations reveal that this strategy provides more satisfying values of the QoS assessment metrics compared to the literature.

Dual Protection Routing Trees on Graphs

In IP networks, packet forwarding is destination-based and hop-by-hop, and routes are built as needed. Kwong et al. introduced a protection routing in which packet delivery to the destination node can proceed uninterrupted in the event of any single node or link failure. He then showed that “whether there is a protection routing to the destination” is NP-complete. Tapolcai found that two completely independent spanning trees, abbreviated as CISTs, can be used to configure the protection routing. In this paper, we proposed dual protection routing trees, denoted as dual-PRTs to replace CISTs, which are less restrictive than CISTs. Next, we proposed a transformation algorithm that uses dual-PRTs to configure the protection routing. Taking complete graphs Kn, complete bipartite graphs Km,n, hypercubes Qn, and locally twisted cubes LTQn as examples, we provided a recursive method to construct dual-PRTs on them. This article showed that there are no two CISTs on K3,3, Q3, and LTQ3, but there exist dual-PRTs that can be used to configure the protection routing. As shown in the performance evaluation of simulation results, for both Qn and LTQn, we get the average path length of protection routing configured by dual-PRTs is shorter than that by two CISTs.

Metascheduling Using Discrete Particle Swarm Optimization for Fault Tolerance in Time-Triggered IoT-WSN

In time-triggered systems, adaptation is performed by using metascheduling approaches to ensure temporal predictability. Wireless sensor networks (WSNs) are growing to be used as a reliable, an energy-efficient, and a scalable network infrastructure for numerous Internet of Things (IoT) applications. Because of run-time changes because of failure events, a metascheduling system is required to address the changes by precomputing schedules for several context events at design time. Therefore, this paper proposes a metascheduler that solves a new scheduling problem for each adaptation scenario in IoT-WSN using our offline algorithm named discrete particle swarm optimization for reliable task allocation (DPSO-TA), resulting in a multi-schedule graph that combines the repeated schedules. In this work, a time-triggered IoT-WSN metascheduler is proposed that takes into considerations the node or link failures that are common in WSN. Single and two failure events are considered, where a graph for all feasible schedules is formed at the network design time. Such a large number of schedules causes a state space explosion problem, which is managed using a re-convergence method. Different application model sizes and network topologies are used to assess the proposed metascheduler under various failure event scenarios. The results show a reduction in the size of the multi-schedule graph by applying inputs with different deadline values and different number of tasks. Furthermore, the validity suggested by the proposed metascheduler, which is the ratio of valid to invalid schedules, is improved when compared to algorithms that schedule tasks to hosts with the shortest completion time or algorithms that select hosts for a set of sorted tasks based on energy consumed and task arrival time.

RAFT Consensus Reliability in Wireless Networks: Probabilistic Analysis

The centralized system becomes less efficient, secure, and resilient as the network size and heterogeneity increase due to its inherent single point of failure issues. Distributed consensus mechanisms characterized by decentralization, autonomy, parallelism and fault-tolerance can meet the increasing demands of safety and security in critical interconnected systems. This paper establishes a Node and Link probabilistic failure model in the presence of node and communication link failures for a representative crash fault tolerant distributed consensus protocol: RAFT. The analytical results in terms of the probability density function and the mean value of consensus reliability are derived. Two important reliability performance indicators, Reliability Gain and Tolerance Gain are proposed to indicate the linear relationship between the consensus reliability and two basic parameters, i.e. the joint failure rate and the maximum number of tolerant faulty nodes, which provide the theoretical guidance for quickly deploying a RAFT system. The special case of a distributed consensus network with already a certain number of failures and its adverse impact are evaluated. The Markov probabilistic models, definitions of Reliability Gain and Tolerance Gain, and the analysis methods proposed in this paper can be extended to other consensus mechanisms.