Comparison of network criticality, algebraic connectivity, and other graph metrics

Bigdeli, Alireza; Tizghadam, Ali; Leon-Garcia, Alberto

doi:10.1145/1610304.1610308

Cited by 44 publications

(18 citation statements)

References 10 publications

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“…Such metrics are algebraic connectivity [7], spectral gap [8], natural connectivity [9], weighted spectrum [10], network criticality [11], and effective graph resistance [12]. Moreover, there have been several studies to compare a subset of these metrics [9], [13], [14]. The results of these metrics show promising ability to describe the robustness of a given graph.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive comparison and accuracy of graph metrics in predicting network resilience

Alenazi

Sterbenz

2015

2015 11th International Conference on the Design of Reliable Communication Networks (DRCN)

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Graph robustness metrics have been used largely to study the behavior of communication networks in the presence of targeted attacks and random failures. Several researchers have proposed new graph metrics to better predict network resilience and survivability against such attacks. Most of these metrics have been compared to a few established graph metrics for evaluating the effectiveness of measuring network resilience. In this paper, we perform a comprehensive comparison of the most commonly used graph robustness metrics. First, we show how each metric is determined and calculate its values for baseline graphs. Using several types of random graphs, we study the accuracy of each robustness metric in predicting network resilience against centrality-based attacks. The results show three conclusions. First, our path diversity metric has the highest accuracy in predicting network resilience for structured baseline graphs. Second, the variance of node-betweenness centrality has mostly the best accuracy in predicting network resilience for Waxman random graphs. Third, path diversity, network criticality, and effective graph resistance have high accuracy in measuring network resilience for Gabriel graphs.

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Section: Introductionmentioning

confidence: 99%

Comprehensive comparison and accuracy of graph metrics in predicting network resilience

Alenazi

Sterbenz

2015

2015 11th International Conference on the Design of Reliable Communication Networks (DRCN)

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“…Graph Theory provides a large body of metrics such as connectivity, betweenness, distance or reliability polynomials [20,21], which are correlated to some forms of robustness. Yet, they do not simultaneously accommodate for both isolation and replication.…”

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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“…Note that FGG expands in two dimensions whereas ELG grows in one dimension. A more detailed comparison of network criticality and algebraic connectivity can be found in [20].…”

Section: A Network Criticality and Algebraic Connectivitymentioning

confidence: 99%

Autonomic traffic engineering for network robustness

Tizghadam

Leon-Garcia

2010

IEEE J. Select. Areas Commun.

Self Cite

103

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Abstract-The continuously increasing complexity of communication networks and the increasing diversity and unpredictability of traffic demand has led to a consensus view that the automation of the management process is inevitable. Currently, network and service management techniques are mostly manual, requiring human intervention, and leading to slow response times, high costs, and customer dissatisfaction. In this paper we present AutoNet, a self-organizing management system for core networks where robustness to environmental changes, namely traffic shifts, topology changes, and community of interest is viewed as critical. A framework to design robust control strategies for autonomic networks is proposed. The requirements of the network are translated to graph-theoretic metrics and the management system attempts to automatically evolve to a stable and robust control point by optimizing these metrics. The management approach is inspired by ideas from evolutionary science where a metric, network criticality, measures the survival value or robustness of a particular network configuration. In our system, network criticality is a measure of the robustness of the network to environmental changes. The control system is designed to direct the evolution of the system state in the direction of increasing robustness. As an application of our framework, we propose a traffic engineering method in which different paths are ranked based on their robustness measure, and the best path is selected to route the flow. The choice of the path is in the direction of preserving the robustness of the network to the unforeseen changes in topology and traffic demands. Furthermore, we develop a method for capacity assignment to optimize the robustness of the network.

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Comparison of network criticality, algebraic connectivity, and other graph metrics

Cited by 44 publications

References 10 publications

Comprehensive comparison and accuracy of graph metrics in predicting network resilience

Comprehensive comparison and accuracy of graph metrics in predicting network resilience

Evaluating robustness of cloud-based systems

Autonomic traffic engineering for network robustness

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