On the reliability of large-scale distributed systems – A topological view

He, Yuan; Ren, Hao; Yang, Baijian

doi:10.1016/j.comnet.2009.03.012

Cited by 10 publications

(6 citation statements)

References 28 publications

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“…The algorithms covered thus far exactly find cut vertices without localization. In this study, we omit the probabilistic cut vertex detection algorithms given in [8,10,11,14,16] and localization-based cut vertex detection algorithms given in [13,15].…”

Section: Related Workmentioning

confidence: 99%

“…The detection of cut vertices is an important research area for various types of networks [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]. Cut vertex detection is crucial in various application scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…To execute central algorithms [2], neighborhood lists of all nodes should be collected by the sink node which may not be an energy-efficient operation as shown in this work. Distributed algorithms can be either exact [3,4,5,6,7,9,12] or probabilistic [8,10,11,14,16]. Because our concern in this paper is exactly detecting cut vertices exactly, we omit other distributed algorithms that can only detect within a probability interval.…”

Section: Introductionmentioning

confidence: 99%

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An Energy-Efficient Distributed Cut Vertex Detection Algorithm for Wireless Sensor Networks

Dağdeviren

Akram

2013

The Computer Journal

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Maintaining connectivity is a very important objective of wireless sensor networks (WSNs) in successfully achieving data collection for applications. A cut vertex (node) is defined as a critical vertex whose removal disconnects a network component and partially disables data delivery. Hence, it is crucial that cut vertices be detected and treated with caution. In this paper, we propose an energy-efficient cut vertex detection algorithm for WSNs. Our algorithm uses a depth-first search approach and is completely distributed. It benefits from the radio multicast capabilities of sensor nodes and is the first algorithm with O(N) time complexity and O(N) sent message complexity, in which each message is O(log2(N)) bits. We show the operation of the algorithm, analyze it in detail, provide testbed experiments and extensive simulations. We compare our proposed algorithm with the other cut vertex detection algorithms and show that our algorithm saves up to 6.8 times more energy in less time.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Energy-Efficient Distributed Cut Vertex Detection Algorithm for Wireless Sensor Networks

Dağdeviren

Akram

2013

The Computer Journal

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“…Similarly, in computer networks, the penetration of a virus can be prevented by simply taking a few critical nodes offline, thus ensuring normal network functionality for the rest of the network [8]. Moreover, the effect of a few critical nodes on the overall network connectivity was highlighted in [9], where removal of only 4% of the nodes in a Peer to Peer Gnutella Network resulted in major fragmentation of the whole network [10]. Finally in a wired telecommunications network, the identification of critical nodes can aid in jamming the network by suppressing the communication between a few critical nodes in the network [11].…”

Section: Introductionmentioning

confidence: 99%

Optimization based spectral partitioning for node criticality assessment

Asif

Lestas

Qureshi

et al. 2016

Journal of Network and Computer Applications

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Timely identification of critical nodes is crucial for assessing network vulnerability and survivability. In this work, we propose a new distributed algorithm for identifying critical nodes in a network. The proposed approach is based on suboptimal solutions of two optimization problems, namely the algebraic connectivity minimization problem and a minmax network utility problem. The former attempts to address the topological aspect of node criticality whereas the latter attempts to address its connection-oriented nature. The suboptimal solution of the algebraic connectivity minimization problem is obtained through spectral partitioning considerations. This approach leads to a distributed solution which is computationally less expensive than other approaches that exist in the literature and is near optimal, in the sense that it is shown through simulations to approximate a lower bound which is obtained analytically. Despite the generality of the proposed approach, in this work we evaluate its performance on a wireless ad hoc network. We demonstrate through extensive simulations that the proposed solution is able to choose more critical nodes relative to other approaches, as it is observed that when these nodes are removed they lead to the highest degradation in network performance in terms of the achieved network throughput, the average network delay, the average network jitter and the number of dropped packets.

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“…Moreover, in [3] it was observed that removal of 4% of the nodes in a Peer to Peer Gnutella Network resulted in major fragmentation of the whole network. The node criticality problem in Peer to Peer and overlay networks was also addressed in [4]. Finally, in [5] it was shown that in a telecommunication network, the penetration of a virus can be prevented by removing a few critical nodes.…”

Section: Introductionmentioning

confidence: 99%

Combined Banzhaf & Diversity Index (CBDI) for critical node detection

Asif

Qureshi

Rajarajan

et al. 2016

Journal of Network and Computer Applications

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Critical node discovery plays a vital role in assessing the vulnerability of a computer network to malicious attacks and failures and provides a useful tool with which one can greatly improve network security and reliability. In this paper, we propose a new metric to characterize the criticality of a node in an arbitrary computer network which we refer to as the Combined Banzhaf & Diversity Index (CBDI). The metric utilizes a diversity index which is based on the variability of a node's attributes relative to its neighbours and the Banzhaf Power Index which characterizes the degree of participation of a node in forming shortest paths. The Banzhaf power index is inspired from the theory of voting games in game theory. The proposed metric is evaluated using analysis and simulations. The criticality of nodes in a network is assessed based on the degradation in network performance achieved when these nodes are removed. We use several performance metrics to evaluate network performance including the algebraic connectivity which is a spectral metric characterizing the connectivity robustness of the network. Extensive simulations in a number of network topologies indicate that the proposed CBDI index chooses more critical nodes which, when removed, degrade network performance to a greater extent than if critical nodes based on other criticality metrics were removed.

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On the reliability of large-scale distributed systems – A topological view

Abstract: Abstract

Cited by 10 publications

References 28 publications

An Energy-Efficient Distributed Cut Vertex Detection Algorithm for Wireless Sensor Networks

An Energy-Efficient Distributed Cut Vertex Detection Algorithm for Wireless Sensor Networks

Optimization based spectral partitioning for node criticality assessment

Combined Banzhaf & Diversity Index (CBDI) for critical node detection

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