Endurance: A new robustness measure for complex networks under multiple failure scenarios

Manzano, Marc; Calle, Eusebi; Torres-Padrosa, Victor; Segovia, Juan; Harle, D.A.

doi:10.1016/j.comnet.2013.08.011

Cited by 27 publications

(13 citation statements)

References 31 publications

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“…Efforts have been carried out to study how to define new measurements. Manzano, for example, introduced a measure called “endurance” and quantified the level of robustness supported by a specific topology under different types of multiple failure scenarios [ 55 ]. Van Mieghem proposed an R -value, ranging from 0 to 1, to assess the robustness of any network [ 56 ].…”

Section: Case Study Analysis and Discussionmentioning

confidence: 99%

A Complex Network Theory-Based Modeling Framework for Unmanned Aerial Vehicle Swarms

Wang

Zhang

et al. 2018

Sensors

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Unmanned aerial vehicle (UAV) swarms is an emerging technology that will significantly expand the application areas and open up new possibilities for UAVs, while also presenting new requirements for the robustness and reliability of the UAV swarming system. However, its complex and dynamic characteristics make it extremely challenging and uncertain to model such a system. In this study, to reach a full understanding of the swarming system, a modeling framework based on complex network theory is presented. First, the scope of work is identified from the point of view of control algorithms considering the dynamics and research novelty of the development of UAV swarming control strategy and three control structures consisting of three interdependent network layers are proposed. Second, three algorithms that systematically build the modeling framework considering all characteristics of the system are also developed. Finally, some network measurements are introduced by adjusting the fundamental ones into the UAV swarming system. The proposed framework is applied to a case study to illustrate the visualization models and estimate the statistical characteristics of the proposed networks with static and dynamic topology analysis. Furthermore, a simple demonstration of the robustness evaluation of the network is also presented. The networks obtained from this framework can be used to further analyze the robustness or reliability of a UAV swarming system in a high-confrontation battlefield environment the effect of cascading failure in ad-hoc network on system.

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Section: Case Study Analysis and Discussionmentioning

confidence: 99%

A Complex Network Theory-Based Modeling Framework for Unmanned Aerial Vehicle Swarms

Wang

Zhang

et al. 2018

Sensors

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“…In contrast to the R-value, n places greater importance on perturbations affecting low percentages of elements in a network. n is normalized to the interval [0, 1], where n = 1 denotes the non-existence of robustness, whereas n = 0 is correlated to the maximum possible degree of robustness [26]. The last functional metric is R*-value which is the R-value computed via a normalized eigenvector or principal component (PC).…”

Section: Functional Metricsmentioning

confidence: 99%

Robustness Comparison of 15 Real Telecommunication Networks: Structural and Centrality Measurements

2016

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Multiple failures can have catastrophic consequences on the normal operation of telecommunication networks. In this sense, guaranteeing network robustness to avoid users and services being disconnected is essential. A wide range of metrics have been proposed for measuring network robustness. In this paper the taxonomy of robustness metrics in telecommunication networks has been extended and a classification of multiple failures scenarios has been made. Moreover, a structural and centrality robustness comparison of 15 real telecommunication networks experiencing multiple failures was carried out. Through this analysis the topological properties which are common for grouping networks with similar robustness are able to be identified.

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“…Although this more of a theoretical framework, the method remains similar: removing elements and measuring the evolution of some key properties. Endurance [24] and elasticity [25] are other attempt to understand robustness in terms of failures and how the propagate through a graph. By contrast with these work, our approach permits to finely tuned the propagation of failure for each component.…”

Section: Related Workmentioning

confidence: 99%

Evaluating robustness of cloud-based systems

et al. 2015

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Various services are now available in the Cloud, ranging from turnkey databases and application servers to high-level services such as continuous integration or source version control. To stand out of this diversity, robustness of service compositions is an important selling argument, but which remains difficult to understand and estimate as it does not only depend on services but also on the underlying platform and infrastructure. Yet, choosing a specific service composition may fail to deliver the expected robustness, but reverting early choices may jeopardise the success of any Cloud project. Inspired by existing models used in Biology to quantify the robustness of ecosystems, we show how to tailor them to obtain early indicators of robustness for cloud-based deployments. This technique helps identify weakest parts in the overall architecture and in turn mitigates the risk of having to revert key architectural choices. We illustrate our approach by comparing the robustness of four alternative deployments of the SensApp application, which includes a MongoDB database, four REST services and a graphical web-front end.

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Endurance: A new robustness measure for complex networks under multiple failure scenarios

Cited by 27 publications

References 31 publications

A Complex Network Theory-Based Modeling Framework for Unmanned Aerial Vehicle Swarms

A Complex Network Theory-Based Modeling Framework for Unmanned Aerial Vehicle Swarms

Robustness Comparison of 15 Real Telecommunication Networks: Structural and Centrality Measurements

Evaluating robustness of cloud-based systems

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