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Register a new userPartial Network Identifiability: Theorem Proof and Evaluation
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Added by
Liang Ma
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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This is a technical report, containing all the theorem proofs and additional evaluations in paper Monitor Placement for Maximal Identifiability in Network Tomography by Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, Don Towsley, published in IEEE INFOCOM, 2014.
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This is a technical report, containing all the theorem proofs and additional evaluations in paper Network Capability in Localizing Node Failures via End-to-end Path Measurements by Liang Ma, Ting He, Ananthram Swami, Don Towsley, and Kin K. Leung, published in IEEE/ACM Transactions on Networking, vol. 25, no. 1, pp. 434-450, 2017.
This is a technical report, containing all the theorem proofs in paper Link Identifiability in Communication Networks with Two Monitors by Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley, published in IEEE Globecom, 2013.
This is a technical report, containing all the theorem proofs in paper On Optimal Monitor Placement for Localizing Node Failures via Network Tomography by Liang Ma, Ting He, Ananthram Swami, Don Towsley, and Kin K. Leung, published in IFIP WG 7.3 Performance, 2015.
This paper considers dynamic networks where vertices and edges represent manifest signals and causal dependencies among the signals, respectively. We address the problem of how to determine if the dynamics of a network can be identified when only partial vertices are measured and excited. A necessary condition for network identifiability is presented, where the analysis is performed based on identifying the dependency of a set of rational functions from excited vertices to measured ones. This condition is further characterised by using an edge-removal procedure on the associated bipartite graph. Moreover, on the basis of necessity analysis, we provide a necessary and sufficient condition for identifiability in circular networks.
Measuring and evaluating network resilience has become an important aspect since the network is vulnerable to both uncertain disturbances and malicious attacks. Networked systems are often composed of many dynamic components and change over time, which makes it difficult for existing methods to access the changeable situation of network resilience. This paper establishes a novel quantitative framework for evaluating network resilience using the Dynamic Bayesian Network. The proposed framework can be used to evaluate the networks multi-stage resilience processes when suffering various attacks and recoveries. First, we define the dynamic capacities of network components and establish the networks five core resilience capabilities to describe the resilient networking stages including preparation, resistance, adaptation, recovery, and evolution; the five core resilience capabilities consist of rapid response capability, sustained resistance capability, continuous running capability, rapid convergence capability, and dynamic evolution capability. Then, we employ a two-time slices approach based on the Dynamic Bayesian Network to quantify five crucial performances of network resilience based on core capabilities proposed above. The proposed approach can ensure the time continuity of resilience evaluation in time-varying networks. Finally, our proposed evaluation framework is applied to different attacks and recovery conditions in typical simulations and real-world network topology. Results and comparisons with extant studies indicate that the proposed method can achieve a more accurate and comprehensive evaluation and can be applied to network scenarios under various attack and recovery intensities.
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