scispace - formally typeset
Search or ask a question

Which network is inherently more robust about link failure or node failure due to redundancy in the network? 

Answers from top 7 papers

More filters
Papers (7)Insight
Proceedings ArticleDOI
Michael Menth, Rüdiger Martin 
27 Dec 2005
84 Citations
In this paper we propose the use of multi-topology (MT) routing for network resilience against link and node failures.
In practice, different networks may be interdependent such that the failure in one network may result in the failure in another network.
Furthermore link failures in the network can be s-dependent.
Open accessBook ChapterDOI
27 May 2002
39 Citations
Resilience against single link failures can be built into the network by providing backup paths, which are used when an edge failure occurs on a primary path.

Related Questions

What are the key factors that influence the accuracy of Graph Neural Networks in failure prediction?5 answersThe accuracy of Graph Neural Networks (GNNs) in failure prediction is influenced by several key factors. Firstly, the instability of trained models, where individual node predictions vary due to random factors, can impact the reliability of predictions. Additionally, the size of the training set, model hyperparameters, and node properties play crucial roles in determining the stability of predictions, with larger training sets and optimized hyperparameters contributing to improved model performance and reduced instability. Moreover, the use of physics-informed loss functions in GNN-based models can enhance accuracy by directly following physical equations, leading to more interpretable results and efficient simulations of cascading failures in power systems.
What are the potential benefits and drawbacks of implementing link failure rerouting based on SDN?5 answersImplementing link failure rerouting based on SDN offers several potential benefits. It allows for the quick recovery of network links in the event of failures, reducing packet loss and improving network performance. SDN-based rerouting mechanisms can also optimize network bandwidth by efficiently sharing traffic among different providers. Additionally, SDN enables the decoupling of control and data planes, providing better network management and flexibility. However, there are also drawbacks to consider. Implementing SDN-based link failure rerouting may require additional memory demand per switch, which can impact network speed and processing efficiency. Furthermore, the selection of backup paths and rerouting strategies must consider factors such as path criticality and residual path capacity to avoid post-recovery congestion and ensure service availability.
Are neural networks robust againts noise?5 answersNeural networks have been shown to be robust against noise in various applications. For example, a proposed neural network architecture achieved decent noise robustness when faced with input data with white noise, even outperforming humans in recognizing very noisy images. Another study applied the localized stochastic sensitivity (LSS) approach to improve the robustness of recurrent neural networks (RNNs) in time series forecasting tasks, reducing the impact of input noises on the network's performance. Additionally, incorporating noisy and noise-free images in the training set of a convolutional neural network (CNN) has been shown to increase the network's generalization and classification accuracy when processing noisy data. These findings suggest that neural networks can exhibit robustness against noise, making them suitable for real-world applications.
What is the network effect? node and link?5 answersThe network effect refers to the increase in value or benefits that a product or service provides as more people use it. It can be categorized into direct and indirect network effects. Direct network effects occur when the consumption benefits of a product increase for everyone on the same network as the number of consumers on that network increases. Indirect network effects arise when an increase in the number of users of a particular hardware product leads to an increased demand for compatible software, resulting in more software varieties being supplied. In the context of social networks, the network effect can also refer to the prediction of undiscovered links in the network based on node similarity. Node similarity-based algorithms, such as the LP and Katz indexes, have been found to have high prediction accuracy in social network link prediction.
Why network breakage happened?5 answersNetwork breakage can occur due to various reasons. In the case of the power blackout on the east side of North America, it was caused by a computer error that led to the shutdown of a generating plant, which in turn put a strain on high-voltage power lines that failed when they came in contact with overgrown trees. In the context of multicast routing in wireless networks, link breakage can limit performance, but network coding can help alleviate this issue by encoding data packets and using a disjoint-path tree for routing, which improves packet delivery ratio and multicast capacity. In the study of isopropanol crystallization, a breakage of the hydrogen-bond network was found to be a precursor step for crystallization. In micro drilling processes, the breakage of drill bits can occur due to the small drill diameter, and on-line detection methods based on neural networks have been developed to effectively detect drill breakage. In the case of major telephone network crashes in the US, poorly designed "local rules" governing network response to unexpected events partially caused the crashes.
How can we design a network that is resilient to failure?5 answersRobust network design, which aims to guarantee network availability under various failure scenarios while optimizing performance/cost objectives, has received significant attention. Existing approaches often rely on model-based mixed-integer optimization that is hard to scale or employ deep learning to solve specific engineering problems yet with limited generalizability. In this paper, we show that failure evaluation provides a common kernel to improve the tractability and scalability of existing solutions. By providing a neural network function approximation of this common kernel using graph attention networks, we develop a unified learning-based framework, FERN, for scalable Failure Evaluation and Robust Network design. Many real-world systems interact with one another through dependency links, which reduces the system robustness. In this work, we develop a theoretical framework for analyzing the robustness of interdependent directed networks under localized attacks. In this paper, we present algorithms for designing networks that are robust to node failures with minimal or limited number of links. The interdependence between communication networks and power grids is a critical issue to take into account when designing and operating both systems. Failures in one network may cause further failures in the other network and vice versa. Therefore, a robust "interdependency network" is needed.

See what other people are reading

What is the role of artificial intelligence in integration of storage system with microgrids?
5 answers
How does the MININET simulator compare to real-world network performance?
4 answers
What are the drawbacks of relying on a smartwatch for daily tasks and communication?
5 answers
How does the MININET simulator compare to real-world network performance?
4 answers
What are the legal and ethical considerations in backup and disaster recovery planning for personal data in vm?
9 answers
Why isn't nuclear energy a renewable energy source?
5 answers
Nuclear energy is not considered a renewable energy source due to the issue of nuclear waste generated by the nuclear fission reaction, as highlighted in Context_3. Despite its potential to reduce carbon emissions and contribute to energy stability, the management of nuclear waste remains a significant concern. Additionally, the integration of nuclear power plants with renewable energy sources like solar and wind is seen as a solution to meet energy demands sustainably. To be classified as renewable, an energy source should have minimal environmental impact and be inexhaustible, which is not the case with nuclear energy due to the finite nature of uranium resources and the challenges associated with nuclear waste disposal. However, advancements in nuclear fuel materials and processes, such as uranium extraction from seawater and optimal utilization in molten salt reactors, aim to enhance the sustainability of nuclear energy.
Why road passability is important during flood?
5 answers
Road passability during floods is crucial due to its direct impact on accessibility for emergency response and community resilience. Flooded roads can disrupt travel, hinder access to critical facilities, and impede evacuation routes, affecting property values and emergency response times. Assessing the loss of physical accessibility to health facilities post-disaster highlights the importance of road networks in disaster response and mitigation efforts. Understanding the dynamics of vehicles moving through floodwaters is essential for ensuring safety, as watercourses intersect roadways at various points, potentially causing traffic disturbances during heavy rain events. A model-based framework can assess the time-varying accessibility of roadway networks during extreme flood events, aiding in emergency response and transportation systems planning.
What are the current trends in the integration of hybrid energy sources for electric vehicle charging stations (EVCS)?
5 answers
Current trends in the integration of hybrid energy sources for electric vehicle charging stations (EVCS) focus on optimizing renewable energy utilization and enhancing charging infrastructure. The use of solar and wind power, along with energy storage systems, in hybrid renewable energy-based EVCS ensures sustainable and uninterrupted charging. Coordinated EV scheduling, incorporating vehicle-to-grid (V2G) technology, aids in managing energy demand efficiently and reducing grid congestion. Common DC bus charging infrastructure, integrating solar PV and fuel cells, enables fast charging and seamless transition during grid availability/unavailability modes. Additionally, the adoption of renewable energy sources like photovoltaic and wind for EVCS not only promotes eco-friendliness but also reduces grid dependency and enhances cost-effectiveness.
How the RL framework help solve service overlay planning problem in manet scenarios?
5 answers
The RL framework plays a crucial role in addressing service overlay planning problems in MANET scenarios by optimizing decision-making processes. NeuroPlan, a deep RL approach, utilizes a graph neural network for state encoding and a two-stage hybrid approach to efficiently solve network planning problems. Additionally, the Flexible Routing Decision (FRD) framework proposes a novel routing decision-making process using multiple metric types to support application-level QoS requirements in MANETs. By leveraging RL methods, such as actor-critic reinforcement learning, these frameworks enhance the efficiency of path discovery and routing decisions while considering dynamic network conditions and diverse QoS requirements in MANET environments. This integration of RL techniques in service overlay planning contributes to cost reduction and improved performance in MANET scenarios.
How does the integration of parameter optimization and topology optimization impact the efficiency and effectiveness of engineering design?
5 answers
The integration of parameter optimization (PO) and topology optimization (TO) can significantly enhance the efficiency and effectiveness of engineering design. By combining PO and TO through a novel hybrid optimization (HO) method, geometric features can be inherited in iterative optimization, leading to faster convergence and improved design solutions. This integration allows for simultaneous optimization of topology and layout parameters, as demonstrated in the optimal design of a permanent magnet synchronous machine, resulting in enhanced torque properties. Additionally, the use of sensitivity analysis and response surface methods in an integrated structure optimization approach aids in filtering key parameters for further optimal design, reducing deformation, mass, and stress in structures like a Delta robot arm.
Why QR code is needed in smart parking?
5 answers
QR codes are essential in smart parking systems for various reasons. They are utilized for assigning parking spaces in real-time, authorizing vehicles, and facilitating spot selection based on availability. QR codes integrated with microcontroller technology and website-based applications help monitor parking space availability in real-time, optimizing parking area utilization. Additionally, QR codes play a crucial role in enhancing security within parking systems by providing a means of vehicle identification and access control. They enable features like car plate recognition and serve as a backup in case primary identification methods fail, ensuring a comprehensive security system for users. Overall, QR codes streamline parking operations, enhance user experience, and contribute to efficient parking management in smart parking solutions.