Network survivability in large-scale regional failure scenarios

Bassiri, Bijan; Heydari, Shahram Shah

doi:10.1145/1557626.1557639

Cited by 28 publications

(20 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Geographically correlated failures have been considered in survivability and 45 vulnerability assessment of optical backbone networks with preplanning [5], [6] or reactive routing protocols [7]. In many prior works, the objective is to find the most vulnerable network area of a predefined size, under specific disaster models, e.g., under line disasters [8], circular disasters [8], [9], or general polygon disasters [10].…”

Section: Related Workmentioning

confidence: 99%

Survivability analysis of a two-tier infrastructure-based wireless network

2017

View full text Add to dashboard Cite

Network design and operation of a mobile network infrastructure, especially its base station systems, need to consider survivability as a fundamental requirement. To this end, quantifiable approaches to survivability analysis of such infrastructures are crucial. The objective of this paper is to propose a model for quantification of the survivability of wireless communication networks subject to massive failures, e.g., caused by natural disasters, common mode hardware and software failures, and security attacks. This means to analyze the transient behavior of the recovery phases. We use a Markov model approach, and apply this in a case study of a two-tier infrastructure-based wireless network. To take location information of base stations into consideration, the spatial average network performance is estimated by means of a stochastic geometry based approach. Further, in order to avoid state space explosion while addressing large networks, an approximate product-form analysis approach is also presented, where the two base stations tiers are decoupled such that their survivability analysis can be studied independently. The assumptions used in the proposed models, including Poisson point process (PPP) assumption and product-form decomposition assumption, are validated on real data. Numerical experiments are also performed to investigate the approximation accuracy and computational efficiency of the product-form analysis approach, as well as to examine the effect of different parameters on the network's survivability. The results show that 1 Lang Xie is the corresponding author with email address langxie 77@163.com. Preprint submitted to Journal of L A T E X TemplatesDecember 12, 2016 the approximate product-form approach is more scalable with reasonably good accuracy and hence may be more preferred for analysis of large size networks.

show abstract

Section: Related Workmentioning

confidence: 99%

Survivability analysis of a two-tier infrastructure-based wireless network

2017

View full text Add to dashboard Cite

show abstract

“…However, there is considerable interest on other various aspects of large-scale failures in current literature [11], [12]. Recently, netCSI [13], a combinatorial based algorithm is proposed to diagnose large-scale failures.…”

Section: Related Workmentioning

confidence: 99%

“…There is considerable interest in large-scale failures in the current literature [11], [12], but not much work focuses on diagnosing these clustered failures. Due to discrepancy in failure patterns, the performance of fault diagnosis techniques that are focused on independent failures [3], [10] degrades when applied to clustered failures.…”

mentioning

confidence: 99%

Adaptive Algorithms for Diagnosing Large-Scale Failures in Computer Networks

Tati

Swami

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

Abstract-In this paper, we propose an algorithm to efficiently diagnose large-scale clustered failures. The algorithm, Cluster-MAX-COVERAGE (CMC), is based on greedy approach. We address the challenge of determining faults with incomplete symptoms. CMC makes novel use of both positive and negative symptoms to output a hypothesis list with a low number of false negatives and false positives quickly. CMC requires reports from about half as many nodes as other existing algorithms to determine failures with 100% accuracy. Moreover, CMC accomplishes this gain significantly faster (sometimes by two orders of magnitude) than an algorithm that matches its accuracy. Furthermore, we propose an adaptive algorithm called Adaptive-MAX-COVERAGE (AMC) that performs efficiently during both kinds of failures, i.e., independent and clustered. During a series of failues that include both independent and clustered, AMC results in a reduced number of false negatives and false positives. [13] is proposed to localize largescale failures in networks. It is shown that by considering the failure patterns of large-scale outages, this algorithm can achieve higher accuracy than existing algorithms developed for independent failures [10]. However, the drawback of netCSI is that the run-time complexity of the algorithm increases exponentially with the increase in size of networks since it is a combinatorial approach. Keywords-FaultIn this paper, we propose a new algorithm called Cluster-MAX-COVERAGE (CMC) that diagnoses large-scale clustered failures. To identify the faulty network elements (i.e., network nodes, routers, and links) CMC utilizes a knowledge base of possible network paths and end-to-end symptom information. The observed end-to-end symptoms during failures include both negative symptoms, such as which source-destination pairs are disconnected, as well as positive symptoms, such as which source-destination pairs can still communicate. This information is reported to the network manager by a few selected nodes in the network called reporting nodes; a complete list of symptoms is not required. Using this information, CMC outputs a hypothesis list which consists of a set of network elements whose failures are consistent with the symptoms.To solve the issue of run-time complexity, CMC adopts a greedy approach when generating the hypothesis list of faulty network elements, as opposed to the combinatorial approach in netCSI. Our greedy approach is similar to a fault diagnosis algorithm called MAX-COVERAGE (MC) [10], which is developed to diagnose black holes or silent failures (independent failures) in IP networks. During clustered failures, the performance of MC degrades significantly-in particular it produces a prohibitively high number of false negatives (see Section V-C1). To overcome this limitation, CMC uses clusters of objects instead of single objects when forming the hypothesis list.The major contributions of CMC include:• Clustering models: To diagnose large-scale failures, CMC selects clusters of objects greedily based on c...

show abstract

“…This definition captures the aspect of correlated failures due to an attack by an intelligent adversary [15,45], as well as failures of large parts of the network infrastructure [30,4,44,9].…”

Section: Network Survivabilitymentioning

confidence: 99%

Path diversification for future internet end-to-end resilience and survivability

2013

View full text Add to dashboard Cite

Path Diversification is a new mechanism that can be used to select multiple paths between a given ingress and egress node pair using a quantified diversity measure to achieve maximum flow reliability. The path diversification mechanism is targeted at the end-to-end layer, but can be applied at any level for which a path discovery service is available. Path diversification also takes into account service requirements for low-latency or maximal reliability in selecting appropriate paths. Using this mechanism will allow future internetworking architectures to exploit naturally rich physical topologies to a far greater extent than is possible with shortest-path routing or equal-cost load balancing. We describe the path diversity metric and its application at various aggregation levels, and apply the path diversification process to 13 real-world network graphs as well as 4 synthetic topologies to asses the gain in flow reliability. Based on the analysis of flow reliability across a range of networks, we then extend our path diversity metric to create a compos- ite compensated total graph diversity metric that is representative of a particular topology's survivability with respect to distributed simultaneous link and node failures. We tune the accuracy of this metric having simulated the performance of each topology under a range of failure severities, and present the results. The topologies used are from nationalscale backbone networks with a variety of characteristics, which we characterize using standard graph-theoretic metrics. The end result is a compensated total graph diversity metric that accurately predicts the survivability of a given network topology.

show abstract

Network survivability in large-scale regional failure scenarios

Cited by 28 publications

References 3 publications

Survivability analysis of a two-tier infrastructure-based wireless network

Survivability analysis of a two-tier infrastructure-based wireless network

Adaptive Algorithms for Diagnosing Large-Scale Failures in Computer Networks

Path diversification for future internet end-to-end resilience and survivability

Contact Info

Product

Resources

About