Event Of Network Failure Research Articles

Safety analysis and dynamic risk assessment of community power distribution network using Bayesian network

Safety analysis and risk assessment of Community Power Distribution Network (CPDN) are the key links of safe operation of distribution network system. Reliability indices based risk assessment is the standard method for power distribution network. However, reliability indices are not adequate for the CPDN's intrinsic safety, which is represented by the states of physical components and network's failure events. Meanwhile, the reliability indices is designed for long-term risk assessment and cannot refer to the operation scenarios of CPDN. Therefore, dynamic risk assessment by considering the intrinsic safety of CPDN is a significant and challenge issue. This study proposes a novel safety and dynamic risk assessment framework of CPDN using Bayesian Network (BN). First, based on the standard reliability indices, the failure rate of physical components in CPDN and the failure events are creatively introduced into risk assessment, then a novel risk indices hierarchy is obtained. This indices hierarchy can quantitatively present the dynamic risk of CPDN, with considering the reliability indices and the features of intrinsic safety. Second, a dynamic risk analysis method is proposed, which is based on Fault Tree (FT) and Bayesian Network (BN). A real CPDN is used as a case study to demonstrate the feasibility and effectiveness of the proposed method. The result of the case study suggests that the proposed framework could adequately assess the risk of CPDN and the derived rational safety actions can reduce the risk effectively.

Interactive decision support methodology for near real-time response to failure events in a water distribution network

Abstract The present study proposes a new interactive methodology and an interactive tool for the response to water network failure events facilitating near real-time decision-making. The proposed methodology considers: (i) a structured yet flexible approach supporting and guiding the operator throughout the entire response process to water network failure events, while allowing the operator to have a final say; (ii) a novel interaction with the operator in near real time via the proposed tool (e.g. allowing operators to propose different ‘what-if’ scenarios without being hydraulic experts); (iii) the provision of automatically generated advice (e.g. optimal response solutions and assessed end-impacts) – although optimal response solutions not identified in near real time yet; and (iv) improved impact assessment using realistic impact indicators that cover different aspects of the event – which are consistently calculated for every proposed response solution (to facilitate easy comparison between different response solutions). The new methodology was applied on a semi-real case study. The results obtained demonstrated the potential of the new response methodology and its application through the interactive tool to improve water utilities' current practice. This was accomplished through supporting/guiding operators in the identification of effective response solutions with low end-impact on the consumers and low cost for the utility.

Rapid Restoration Techniques for Software-Defined Networks

There is increasing demand in modern day business applications for communication networks to be robust and reliable due to the complexity and critical nature of such applications. As such, data delivery is expected to be reliable and secure even in the harshest of environments. Software-Defined Networking (SDN) is gaining traction as a promising approach for designing network architectures which are robust and flexible. One reason for this is that separating the data plane from the control plane, increases the controller’s ability to configure the network rapidly. When network failure events occur, the network manager may trade-off the optimality of the achieved network reconfiguration with the responsivenss of the reconfiguration process. Responsiveness may be favoured when the network resources are under stress and the failure rate is high. We contribute SDN recovery methods that leverage information about the structure of the network to expedite network restoration when a link failure occurs. They operate by detecting community-like structures in the network topology and then they find alternative paths which have low operation and installation costs using this information. Extensive simulations are conducted to evaluate the proposed SDN recovery methods using open-source simulation tools. They provide evidence that the proposed approaches lead to performance gains when an alternative path is required among a set of candidate paths.

A Spatial Statistic Based Risk Assessment Approach to Prioritize the Pipeline Inspection of the Pipeline Network

The identification of high risk regions is an important aim of risk-based inspections (RBIs) in pipeline networks. As the most vital part of risk-based inspections, risk assessment makes a significant contribution to achieving this aim. Accurate assessment can target high risk inspected regions so that limited resources can mitigate considerable risks in the face of increased spatial distribution of a pipeline network. However, the existing approaches for risk assessment face grave challenges due to a lack of sufficient data and an assessment’s vulnerability to human biases and errors. This paper attempts to tackle those challenges through spatial statistics, which is used to estimate the uncertainty of risk based on a dataset of locations of pipeline network failure events without having to acquire additional data. The consequence of risk in each inspected region is measured by the total cost caused by the failure events that have occurred in the region, which is also calculated in the assessment. Then, the risks of the different inspected regions are obtained by integrating the uncertainty and consequences. Finally, the feasibility of our approach is validated in a case study. Our results in the case study demonstrate that uncertainty is less instructive for prioritizing pipeline inspections than the consequences of risk due to the low significant difference in risk uncertainty in different regions. Our results also have implications for understanding the correlation between the spatial location and consequences of risk.

Averaged one-dependence estimators on edge devices for smart pregnancy data analysis

The development of edge computing has allowed the Internet of Things (IoT) to reach higher levels of operational efficiency, knowledge generation, and decision-making through a better relationship between applications and their users. The principal idea is the processing of large amounts of data close to the network edge, where these data are generated. Hence, in this paper, the use of a machine learning technique, known as averaged one-dependence estimators, is proposed for real-time pregnancy data analysis from IoT devices and gateways. This statistical technique is useful for decentralized pre-processing of data and its intermediate storage, reducing the amount of data to be transferred to the cloud and ensuring operability, even in an event of network failure. The results show that this technique presents accurate results with a low computational cost and could be a useful tool to better take advantage of the potential of IoT solutions in healthcare.

Mobile Network Failure Event Detection and Forecasting With Multiple User Activity Data Sets

As the demand for mobile network services increases, immediate detection and forecasting of network failure events have become important problems for service providers. Several event detection approaches have been proposed to tackle these problems by utilizing social data. However, these approaches have not tried to solve event detection and forecasting problems from multiple data sets, such as web access logs and search queries. In this paper, we propose a machine learning approach that incorporates multiple user activity data into detecting and forecasting failure events. Our approach is based on a two-level procedure. First, we introduce a novel feature construction method that treats both the imbalanced label problem and the data sparsity problem of user activity data. Second, we propose a model ensemble method that combines outputs of supervised and unsupervised learning models for each data set and gives accurate predictions of network service outage. We demonstrate the effectiveness of the proposed models by extensive experiments with real-world failure events occurred at a network service provider in Japan and three user activity data sets.

A new cooperative communication algorithm for improving connectivity in the event of network failure in VANETs

Communication in Vehicular Ad-Hoc Networks (VANETs) is frequently disrupted by high mobility of nodes, obstacles, and interference-prone links. The communication link between nodes can be maintained using cooperative communication techniques where nodes collaborate with one another to increase the network performance. Implementation of cooperative communication, which requires helpful nodes to collaborate with others, is a difficult task in VANETs due to their inherent characteristics. Selection of a cooperative node which will help during the retransmission process is the most crucial work in the cooperative communication process considering the dynamic nature of nodes. In this study, we formulate a game-theoretic relay selection protocol for MAC-level retransmission, where every node plans to maximize their received incentives, which is determined by an incentive function. Therefore, cooperative neighboring nodes form tree structures to the destination node from transmitter node by using the proposed algorithm, NGOMA. The distributed incentive function stores promiscuous packets in nodes and forms a tree architecture with cooperative nodes to relay messages to the recipient node from the source node in times of packet-loss events. The network formed by the NGOMA protocol by the source and destination node with relay nodes, converges into a Nash network. Numerical results show that NGOMA, which allows nodes to cooperatively store and transmit lost packets to the recipients, reduces the MAC-level retransmission delay by 63% compared to a non-cooperative function, which does not cooperatively relay messages.

An empirical analysis of intra- and inter-datacenter network failures for geo-distributed services

As cloud services continue to grow, a key requirement is delivering an 'always-on' experience to end users. Of the several factors affecting service availability, network failures in the hosting datacenters have received little attention. This paper presents a preliminary analysis of intra-datacenter and inter-datacenter network failures from a service perspective. We describe an empirical study analyzing and correlating network failure events over an year across multiple datacenters in a service provider. Our broader goal is to outline steps leveraging existing network mechanisms to improve end-to-end service availability.

Analysis of major failures in Europe’s power grid

Power grids are prone to failure. Time series of reliability measures such as total power loss or energy not supplied can give significant account of the underlying dynamical behavior of these systems, specially when the resulting probability distributions present remarkable features such as an algebraic tail, usually considered the footprint of self-organization and the existence of critical points. In this paper, 7 years (from 2002 to 2008) of Europe’s transport of electricity network failure events have been analyzed and the best fit for this empirical data probability distribution is presented. With the actual span of available data and although there exists a moderate support for the power-law model, the relatively small amount of events contained in the function’s tail suggests that causal factors other than self-organization or a critical state might be significantly ruling these systems’ dynamics.

An Overlay Architecture for High-Quality VoIP Streams

The cost savings and novel features associated with voice over IP (VoIP) are driving its adoption by service providers. Unfortunately, the Internet's best effort service model provides no quality of service guarantees. Because low latency and jitter are the key requirements for supporting high-quality interactive conversations, VoIP applications use UDP to transfer data, thereby subjecting themselves to quality degradations caused by packet loss and network failures. In this paper, we describe an architecture to improve the performance of such VoIP applications. Two protocols are used for localized packet loss recovery and rapid rerouting in the event of network failures. The protocols are deployed on the nodes of an application-level overlay network and require no changes to the underlying infrastructure. Experimental results indicate that the architecture and protocols can be combined to yield voice quality on par with the public switched telephone network