Maximizing k-Terminal Network Reliability in Some Sparse Graphs

Zhang, Zuyuan; Shao, Faming; Zhang, Nan; Niu, Yifeng

doi:10.1109/tnet.2020.3030819

Cited by 10 publications

(2 citation statements)

References 25 publications

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“…Traditionally, the literature on networked data systems has focused on maximizing link-level reliability to ensure the best possible service for connected devices (see references [7], [8], and [9]). Most research on network-wide reliability has consisted of evaluating graphbased connectivity metrics in light of possible failures that may occur in the network (e.g., k-terminal network reliability, as described in reference [10]). However, there has recently been a shift towards strict network-level reliability that takes into account user data traffic demand, particularly for nextgeneration cellular networks.…”

Section: Introductionmentioning

confidence: 99%

A Framework for the Evaluation of Network Reliability Under Periodic Demand

Maatouk¹,

Ayed²,

Shi³

et al. 2023

Preprint

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In this paper, we study network reliability in relation to a periodic time-dependent utility function that reflects the system's functional performance. When an anomaly occurs, the system incurs a loss of utility that depends on the anomaly's timing and duration. We analyze the long-term average utility loss by considering exponential anomalies' inter-arrival times and general distributions of maintenance duration. We show that the expected utility loss converges in probability to a simple form. We then extend our convergence results to more general distributions of anomalies' inter-arrival times and to particular families of nonperiodic utility functions. To validate our results, we use data gathered from a cellular network consisting of 660 base stations and serving over 20k users. We demonstrate the quasi-periodic nature of users' traffic and the exponential distribution of the anomalies' inter-arrival times, allowing us to apply our results and provide reliability scores for the network. We also discuss the convergence speed of the long-term average utility loss, the interplay between the different network's parameters, and the impact of non-stationarity on our convergence results

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Section: Introductionmentioning

confidence: 99%

A Framework for the Evaluation of Network Reliability Under Periodic Demand

Maatouk¹,

Ayed²,

Shi³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…For example, Horner and Widener [34] simulated the failure of partial road network and test how these damages affect the victims' access to relief goods. Zhang et al [35] studied the optimal distribution of k terminal vertices to maximize k terminal network reliability in sparse graphs, in which the edges are subject to failure. In these studies which consider edge failure, many authors assume that the network links have homogeneous or heterogeneous failure probabilities and this kind of assumptions implies that the interruption of the link is caused by its own failure [36].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Model to Improve Network Reliability Level under Limited Budget by Considering Selection of Facilities and Total Service Distance in Rescue Operations

Wang

Filip

2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Sudden disasters may damage facilities, transportation networks and other critical infrastructures, delay rescue and bring huge losses. Facility selection and reliable transportation network play an important role in emergency rescue. In this paper, the reliability level between two points in a network is defined from the point of view of minimal edge cut and path, respectively, and the equivalence of these two definitions is proven. Based on this, a multi-objective optimization model is proposed. The first goal of the model is to minimize the total service distance, and the second goal is to maximize the network reliability level. The original model is transformed into a model with three objectives, and the three objectives are combined into one objective by the method of weighting. The model is applied to a case, and the results are analyzed to verify the effectiveness of the model.

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