Network Failures Research Articles

From Data to Knowledge: A Mini-Review on Molecular Network Modeling and Analysis for Therapeutic Target Discovery

Successful drug development is a risky and lengthy process that can take over ten years and consume billions of dollars. Target discovery is a critical stage of drug development for the identification of key molecules and pathways that can be targeted by novel therapeutics to find more effective treatments. Due to the rapid development in artificial intelligence and machine learning techniques over the past decade, computational approaches have now emerged as powerful tools to unravel complex interactions within biological systems to identify novel therapeutic targets. In particular, modeling and analysis of intracellular molecular networks play a pivotal role in target discovery by enabling researchers to efficiently and simultaneously navigate massive amounts of biological data to identify potential therapeutic targets. Such technologies can significantly accelerate the prolonged process of development of innovative therapies for complex diseases. Besides highlighting the findings of the recently introduced extreme signaling failures in intracellular molecular networks, here we briefly review various methods for modeling and analysis of intracellular molecular networks and discuss how they can be utilized to predict potential drug targets within such complex signaling systems. Overall, this review emphasizes the significance of modeling and analysis of molecular networks for fast-tracking and rapid discovery of novel therapeutic targets; to pave the way for the development of more effective treatments.

V-Recover: Virtual Machine Recovery When Live Migration Fails

Live migration is a critical technology used in cloud infrastructures to transfer running virtual machines (VMs). When live migration fails, as it often does, it is critical that any VMs in transit are not lost. There are two primary live migration techniques – pre-copy and post-copy. Pre-copy transfers a VM's memory to the destination before its virtual CPUs are transferred, whereas post-copy does the reverse. Both pre-copy and post-copy will lose the VM if the source machine fails during migration. Additionally, post-copy can lose the VM if the destination machine or network fail since the VM's memory and execution state are split across the source and destination machines. We present V-Recover, an approach to recover a VM when the source, destination, or network fails during live migration. V-Recover consists of two techniques: (1) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">forward incremental checkpointing</i> (FIC) mechanism to handle source machine failure during both pre-copy and post-copy, and (2) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reverse incremental checkpointing</i> (RIC) mechanism to handle destination or network failure during post-copy. We present the design, implementation, and evaluation of V-Recover in the KVM/QEMU virtualization platform. Our evaluations show that V-Recover effectively recovers a VM upon migration failure with acceptable overheads on migration metrics and application performance.

Fault diagnosis of 5G networks based on digital twin model

Fault diagnosis of 5G networks faces the challenges of heavy reliance on human experience and insufficient fault samples and relevant monitoring data. The digital twin technology can realize the interaction between virtual space and physical space through the fusion of model and data, providing a new paradigm for fault diagnosis. In this paper, we first propose a network digital twin model and apply it to 5G network diagnosis. We then use an improved Average Wasserstein GAN with Gradient Penalty (AWGAN-GP) method to discover and predict failures in the twin network. Finally, we use XGBoost algorithm to locate the faults in physical network in real time. Extensive simulation results show that the proposed approach can significantly increase fault prediction and diagnosis accuracy in the case of a small number of labeled failure samples in 5G networks.

Optimization Approaches for Demand-Side Management in the Smart Grid: A Systematic Mapping Study

Demand-side management in the smart grid often consists of optimizing energy-related objective functions, with respect to variables, in the presence of constraints expressing electrical consumption habits. These functions are often related to the user’s electricity invoice (cost) or to the peak energy consumption (peak-to-average energy ratio), which can cause electrical network failure on a large scale. However, the growth in energy demand, especially in emerging countries, is causing a serious energy crisis. This is why several studies focus on these optimization approaches. To our knowledge, no article aims to collect and analyze the results of research on peak-to-average energy consumption ratio and cost optimization using a systematic reproducible method. Our goal is to fill this gap by presenting a systematic mapping study on the subject, spanning the last decade (2013–2022). The methodology used first consisted of searching digital libraries according to a specific search string (104 relevant studies out of 684). The next step relied on an analysis of the works (classified using 13 criteria) according to 5 research questions linked to algorithmic trends, energy source, building type, optimization objectives and pricing schemes. Some main results are the predominance of the genetic algorithms heuristics, an insufficient focus on renewable energy and storage systems, a bias in favor of residential buildings and a preference for real-time pricing schemes. The main conclusions are related to the promising hybridization between the genetic algorithms and swarm optimization approaches, as well as a greater integration of user preferences in the optimization. Moreover, there is a need for accurate renewable and storage models, as well as for broadening the optimization scope to other objectives such as CO2 emissions or communications load.

Resource-efficient protection scheme for optical service units in fifth-generation fixed networks

One of the visions of fifth-generation fixed networks (F5G) is to provide a guaranteed reliable experience (GRE). Optical transport networks (OTNs) represent a primary candidate technology to offer high-quality bandwidth in F5G and hence to support GRE. Unfortunately, current OTN technologies provide large-bandwidth tunnels for carrying aggregated (typically, Ethernet) traffic, and corresponding protection techniques in OTNs consume a lot of resources. The emergence of a new technology called an optical service unit (OSU), which subdivides the payload block of traditional optical data units into smaller units, allows for the development of more flexible and resource-efficient protection schemes. In this paper, we propose a new resource-efficient protection scheme for OSU-based OTNs called OSU protection (OSU-P), with the aim to save network resources while providing individual customized protection for each single user. OSU-P only reserves small-bandwidth protection/backup connections for protected services; then, when a failure occurs, the protection bandwidth is adjusted to the bandwidth required by the service during the switching process. This scheme can save a significant amount of network resources, but it may encounter resource conflicts between the protected services and other preemptable services after a failure occurs. Hence, we also propose a deterministic protection algorithm and a preemptable service provisioning algorithm based on overlapping risk avoidance, which can ensure successful recovery switching of protected services and the least impact on preemptable unprotected services after a network failure occurs. Simulation results show that, compared with traditional 1+1 and 1:1 protection schemes, OSU-P can reduce spare resources allocated for protection by about 30% and 11%, respectively, and reduce the blocking rate by about 27% and 10%, respectively. The performance of the proposed algorithms in terms of the percentage of unsuccessful protection and affected preemptable services is also verified.

Hosting Capacity Assessment in Electrical Power Distribution Systems Using Genetic Algorithm

Environmental issues and the current global energy crisis serve as further motivators for the promotion of renewable energy sources. However, integrating these sources into existing power grids presents numerous challenges. As the connection capacity approaches its limits, it is imperative to employ innovative engineering methods to integrate distributed generation (DG) into resilient, self-healing smart grids of the future. One such tool is Hosting Capacity (HC) analysis, which is an emerging power system-planning tool used to position investments toward parts of the network that can absorb additional generation and promote efficient use of energy sources, avoiding overloading, inefficiencies, DG misallocations, and network failures. In this study, a technique for calculating the ideal HC in a power system is presented. The goal of this research is to develop a replicable optimization methodology for determining HC in smart distribution systems using a single objective constrained optimization problem solved through the use of genetic algorithm (GA). Detailed power system load and generation modeling and the use of advanced open-source research tool for load flow optimization improve the confidence in the proposed model. This research contributes to collective knowledge of the subject matter and establishes a reliable optimization methodology for determining HC in power systems.

Short-term economic dynamism as a policy tool to address supply shortages during crises

Abstract This paper investigates the role of short-term economic dynamism in responding to crisis induced supply shortages. We focus on the domestic manufacturing ramp-up of surgical masks, respirators, and their intermediary products in response to the COVID-19 pandemic. We develop a novel method for timely identification and validation of the evolving state of domestic manufacturing. To unpack the activities of domestic manufacturers and related institutions, we triangulate across 56 qualitative interviews, certifications, Thomasnet.com®, industry associations, and other public data. We find that while large manufacturers could rapidly scale up, onshore, or diversify production to enter into domestic production of critical medical supplies, these large manufacturers alone were insufficient to meet the spike in demand. In face of this shortage, small and medium enterprises (SME), who entered into mask and respirator production as de novo firms, spin-offs, and by diversifying, were important in increasing overall domestic capacity and serving markets unmet by large hospital distributors. These firms often had fewer competencies and resources compared to larger firms, and received less effective government support. Despite these disadvantages, a number of SMEs succeeded in entering into domestic production, and our interviews suggest this capacity could have been better integrated into the national response. We propose new theory for how and when federal and state governments should support short-term economic dynamism (firm entry into target products and/or markets) during crises to address supply shortages, and the types of market and network failures federal or state governments may be most effective at addressing.

Analytical traffic model of 6TiSCH using real-time in-band telemetry

In industrial environments, the IETF 6TiSCH stack is a protocol stack consisting of standardized layers (IEEE 802.15.4, TSCH, 6LoWPAN, RPL, CoAP) that enable reliable wireless communications. To efficiently manage the network, gathering insights is critical. This allows for detecting traffic bottlenecks, predicting energy consumption, checking duty cycle limits, adjusting network topology, and identifying interference. As such, detailed knowledge of the traffic quantity can be used to create a digital twin of the network to help prevent node or network failures by re-configuring network parameters. To that end, this paper presents an analytical model to calculate the transmitted and received bytes for every node in a 6TiSCH network, considering the complete 6TiSCH network stack. Real-time in-band network telemetry is used to limit monitoring overhead by piggybacking information onto transmitted data packets. Simulations demonstrate that our model provides accurate predictions with a median of 95 to 99% under various circumstances, such as topology changes and packet loss. The model can enhance network robustness and can be extended to predict the energy consumption of 6TiSCH devices easily.

Exploring the Power of GKE Networking: An In-depth Analysis ofKubernetes Node and Pod Communication in Google Kubernetes

This paper analyzes the communication between Kubernetes nodes and pods in Google Kubernetes Engine (GKE). Weexplore the structure and features of GKE networking, including communication between nodes, nodes, and pods, andpods themselves. We also consider the network security and performance aspects. In addition, we analyze commonnetworking issues such as latency, packet loss, and network failures and provide solutions. Through our experimentalresults, we demonstrate the effectiveness of our proposed solutions and offer insights for future research in this area.This paper provides valuable insights for developers and operators who use GKE and want to optimize their networkingconfiguration.

Fed-TSN: Joint Failure Probability-Based Federated Learning for Fault-Tolerant Time-Sensitive Networks

Industrial Internet of Things (IIoT) applications have diverse network session requirements. Certain critical applications, such as emergency alert relays, as well as industrial floor evacuation and surveillance systems, require fresh updates that can maintain the most recently delivered packets. This requires high reconfigurability to an extent where the system can measure the impact of an event and adapt the network accordingly. Recent research has demonstrated that network failures can undermine the sustainability of Industry 4.0 or Industrial IoT in general. In this paper, we design an intelligent Federated learning based Time-Sensitive Networking (Fed-TSN) controller framework to optimize the failure recovery. In industrial IoT scenarios, such as emergency evacuations on factory floors due to natural disasters, there can be multiple link failures with no disjoint paths which require a sustainable recovery solution. Accordingly, we consider multiple simultaneous link failures, both for networks with and without disjoint paths. The typically probabilistic network failures on a factory floor call for designing a mechanism that can search for routes with minimum joint failure probability (JFP). We formulate the JFP minimization problem as a non-linear integer program. We design a Software Defined Networking (SDN) controller that runs an application to produce near-optimal solutions for providing enhanced sustainability in a wide range of Industry 4.0 scenarios. We employ this non-linear integer program solution as input to our intelligent Fed-TSN fault recovery strategy that predicts the migration location based on the changes in the TSN gate schedule. We conduct simulations to quantify the improvements achieved with Fed-TSN compared to state-of-the-art approaches.

Cascading failures in interdependent directed networks under localized attacks

Cascading failures in interdependent directed networks under localized attacks

A Resource-Saving Shelter Selection Approach for Large-Scale Area Emergency

Most disaster evacuation approaches prioritize the best possible shelters for people based on their escape time. However, it is observed that lots of people continuously try to contact or travel to their relatives and friends after they safely arrived at shelters. This results in the occupation of communication and transportation resources and causes traffic congestion and internet blocking. Resources in disaster are insufficient and should be reserved for emergencies. This article proposed a resource-saving approach, resource-saving shelter selection approach (RSSA) in short, which determines shelters for people by considering their relationships and locations. In other words, people are gathered with whom they care to the same shelter to maximize the resource-saving for disaster relief. In addition, RSSA is designed standalone on mobile devices to cope with the inevitable network failures. The simulation results reveal that 23% of traveling costs are saved by RSSA compared to other approaches.

Automatic Spike Neural Technique for Slicing Bandwidth Estimated Virtual Buffer-Size in Network Environment

The Next-generation networks, such as 5G and 6G, need capacity and requirements for low latency, and high dependability. According to experts, one of the most important features of (5 and 6) G networks is network slicing. To enhance the Quality of Service (QoS), network operators may now operate many instances on the same infrastructure due to configuring able slicing QoS. Each virtualized network resource, such as connection bandwidth, buffer size, and computing functions, may have a varied number of virtualized network resources. Because network resources are limited, virtual resources of the slices must be carefully coordinated to meet the different QoS requirements of users and services. These networks may be modified to achieve QoS using Artificial Intelligence (AI) and machine learning (ML). Developing an intelligent decision-making system for network management and reducing network slice failures requires reconfigurable wireless network solutions with machine learning capabilities. Using Spiking Neural Network (SNN) and prediction, we have developed a 'Buffer-Size Management' model for controlling network load efficiency by managing the slice's buffer size. To analyze incoming traffic and predict the network slice buffer size; our proposed Buffer-Size Management model can intelligently choose the best amount of buffer size for each slice to reduce packet loss ratio, increase throughput to 95% and reduce network failure by about 97%.

0944 Telemedicine (TM) to provide outpatient care for sleep disorders in complex neurological patients in an academic Medical Center

Abstract Introduction Since the widespread adoption of telemedicine (TM) as a reimbursable option for the delivery of healthcare, (particularly clinic visits), the field of sleep medicine has been a beneficiary. In complex neurological patients, it was hypothesized that physical and cognitive constraints would limit the use of TM. This analysis was undertaken to evaluate patient care strategies in an academic neurology department based in a large metropolitan city Methods 2964 adult patients who opted for neurology clinic visits via video TM in the last 6 months were included from electronic medical record review at the University of Texas Health Sciences Center in Houston. Of these, 557 were seen in sleep medicine specialty. A successful visit was defined as a simultaneous connection of at least 2 parties for a minimum duration of time defined by institutional guidelines. Patients were analyzed in age subgroups (&amp;lt; 30, 31-50, 51-70 and &amp;gt;70 years of age) as well as race (Asian, Black, Hispanic, White and Others). Neurological diagnoses in these patients included stroke, epilepsy, movement disorders, cognitive disorders, neuromuscular conditions and less commonly multiple sclerosis Results Sleep medicine providers comprise of 2/53 (3.7%) of the department’s clinical providers, but accounted for 18.7% of TM visits. 97% (182/186) of new patients and 95% (354/371) of established patients had successful TM visits. Among general neurology patients, these numbers were 89% and 88% respectively. There was a statistically significant difference between successful visits in the sleep medicine and overall neurology patient groups (p value 0.0003 for new, 0.0001 for established patients). Among sleep medicine patients, percentage failures were 2.9, 3.3, 4.1 and 3.9 among the respective age groups. Percentage failures across the respective racial groups were 0, 6.1, 4.8, 4.2 and 1.9. Reasons cited for unsuccessful visits included camera, microphone and general hardware failures. Surprisingly network failure was not reported Conclusion The success of telemedicine clinic visits for the management of sleep disorders is not impacted by physical and cognitive impairment in patients with complex neurological conditions. Further analysis to include success of sleep diagnostics and treatment of sleep disorders (start-to-finish pathway), via telemedicine in this complex population is underway Support (if any) None

Electric vehicle charging path planning considering couped faults in distribution-transportation network

Once a distribution network failure occurs, it can spread to the traffic network through the coupling point, causing electric vehicles (EVs) to change their charging paths. To address this problem, this paper presents an EV charging path planning approach that considers coupled faults in the distribution-transportation network. First, the cascading failure model of the distribution-transportation network and the model for choosing charging stations are presented to transfer the information of coupling faults propagation and coupling points power interaction to the follow-up path planning scheme. Second, a time occupancy road resistance model that considers congested and unobstructed traffic states is proposed to calculate the road section travel time, based on the analysis results of the evolution process of road traffic flow queuing using traffic wave theory. For the speed and density parameters in the traffic wave model, values are calculated using the logistics speed-density model and the time occupancy model. Third, a multi-objective optimization function that integrates travel cost and coupling network operation state is determined from the perspective of hindering the propagation of coupling faults. The function is solved to recommend optimal charging paths using an improved A* searching algorithm. Finally, a 90-bus road network and three 33-bus distribution networks are selected as examples to verify the veracity and validity of the proposed model and method. The research results demonstrate that the proposed method can alleviate traffic congestion.

Machine learning quantum-chemical bond scission in thermosets under extreme deformation

Despite growing interest in polymers under extreme conditions, most atomistic molecular dynamics simulations cannot describe the bond scission events underlying failure modes in polymer networks undergoing large strains. In this work, we propose a physics-based machine learning approach that can detect and perform bond breaking with near quantum-chemical accuracy on-the-fly in atomistic simulations. Particularly, we demonstrate that by coarse-graining highly correlated neighboring bonds, the prediction accuracy can be dramatically improved. By comparing with existing quantum mechanics/molecular mechanics methods, our approach is approximately two orders of magnitude more efficient and exhibits improved sensitivity toward rare bond breaking events at low strain. The proposed bond breaking molecular dynamics scheme enables fast and accurate modeling of strain hardening and material failure in polymer networks and can accelerate the design of polymeric materials under extreme conditions.

A Cascading Failure Model of the Air Traffic Control Network Based on the Theory of Interdependent Networks

With the continuous development of information technology, a spontaneous interdependent network has formed within the air traffic control network. Due to the internal interdependence, any small, failed node may trigger a cascade failure of the entire system. The purpose of this study is to investigate the resilience of air traffic control networks. Based on air traffic management regulations, a new cascading failure model for air traffic control networks is proposed, which is based on the theory of interdependent networks. The model establishes a dual-layer dependency relationship between the control coordination network and the air route facility network, including control dependency and service dependency. Through experiments, targeted measures are proposed to improve the safety and reliability of air traffic control. This model introduces parameters such as control cost and node control capability, and reflects the resilience of the air traffic control network, based on the final number of failed nodes after the steady-state of the cascade failure, the network’s cascade failure rate, and the system’s load failure threshold. Simulation results show that enhancing the control capability and increasing the number of control positions can improve the control cost of the air traffic control network. The higher the control cost, the better the resilience of the air traffic control network. Improving the control capability of control nodes has a greater impact on the resilience of the air traffic control network, compared to increasing the number of control nodes. The degree attack on route nodes has a greater impact on the cascade failure of the air traffic control network, compared to random attacks and facility node degree attacks. The cascade failure model proposed in this paper provides a new method for guiding the air traffic control network to resist cascade failure attacks and enhance its resilience.

Key Factors Influencing Network Resilience in Dynamical Networks

There has been much recent research focusing on the resilience of networks, providing theoretical insights into the effective response of real-world systems systems to disasters. However, few studies have analyzed the factors that affect the resilience of networks. And the network operation process varies greatly so that the dynamic behavior of the network is a factor that has to be considered. To bridge these gaps, we analyze the factors affecting dynamic network resilience in terms of network dynamics. There are two main influencing factors: differentiation of failure probability, differentiation of impact. We build a generic resilience model for the network and validate these influencing factors by simulating them in different networks. By summarizing these factors, we point out constructive strategies. These strategies can help dynamic networks enhance network resilience, which is an important criterion for reducing network failures in real-world systems.

Qualitative Analysis on Costs and Benefits of Adopting a Cloud-Based Accounting Information System: A Case Study of Rural Banks in Ghana

The accounting and recording approach have been impacted by the expansion of the internet and the advancement of information technology. Cloud accounting is a result of the use of cloud computing technology. Cloud computing is growing in acceptance, and more companies are using cloud-based software to boost productivity and gain a number of other advantages. The purpose of this study was to learn more about the advantages and difficulties of implementing a cloud-based accounting information system. The study, which employed a qualitative approach, discovered that cloud accounting enhanced operations compared to using in-house software, increased accessibility of the banks' services, and sped up customer turnaround time. High maintenance costs, network failure, trouble deciphering reported error codes, insufficient technical help, and system failure during an update are a few of the difficulties.

ReLoRaWAN: Reliable data delivery in LoRaWAN networks with multiple gateways

LoRa has emerged as a promising technology to provide low-power and long-range communication for IoT devices. LoRaWAN networks build on top of the LoRa physical layer and adopt a centralized network architecture that supports one or more gateways connected with a large number of end devices. Although LoRa communication is resistant to channel noises, it still frequently suffers data delivery failures in LoRaWAN networks due to packet collisions of the concurrent transmissions. The delivery failures trigger the retransmission procedure in LoRaWAN protocol, which causes additional power consumption on end devices and further exacerbates packet collisions. To solve this issue, we investigate the reliable data delivery mechanism that utilizes with the packet reception of multiple gateways to recover the distorted payload. Firstly, we present a ReLoRaWAN framework, where the central server aggregates distorted packet payload from multi-gateway reception of the same packet and executes the corresponding data recovery operations. Then, we design a Tri-operation Integrated Data Recovery (TIDR) algorithm for recovering the distorted packet payload, which involves exclusive-OR based bitwise inversion operation, majority voting based bitwise inversion operation, and weighted bitwise decision operation. Finally, we implement a ReLoRaWAN testbed and conduct real-world experiments to evaluate the performance of our solution. Compared with existing works, the ReLoRaWAN greatly optimizes the quality of service and power consumption in the network. It improves the packet delivery ratio by 35% and reduces the average power consumption of the end device by 30%.