On/off process modeling of IP network failures

Kuusela, Pirkko; Norros, Ilkka

doi:10.1109/dsn.2010.5544427

Cited by 21 publications

(23 citation statements)

References 9 publications

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“…Then, it is possible to find the analytical bound for the risk measures. In practice, network failures arrive according to a Poisson process [31]. This is a common assumption taken while the mathematical modelling of failures is performed.…”

Section: Unprotected Casementioning

confidence: 99%

“…The relevant bibliography is reviewed and commented on in the context of risk engineering in [29]. Extensive studies of failure and recovery times related to operational networks have been performed and reported for the Sprint network [30], the Finnish research network [31], and the Norwegian university network [32]. Generally, a typical approach in numerical studies is to assume that the failures arise due to the homogeneous Poisson process.…”

Section: Related Workmentioning

confidence: 99%

“…Modelling of downtimes (repair or recovery times) is more controversial. While the simplest approach also uses exponential times, recovery times in real networks appear to be log-normal [34] or Pareto-like-but always having mean value [31,33].…”

Section: Related Workmentioning

confidence: 99%

“…where l represents a link length expressed in kilometres (l max is the largest link length in a networks), and k R and m are the basic distribution parameters retrieved from [31]. Each service has its own parameters necessary to find the exact value of the penalty.…”

Section: Numerical Studiesmentioning

confidence: 99%

“…In the basic case, according to the most commonly assumed conditions, both distributions for link/node failure times/downtimes are exponential. Their rates were taken from [31]. We use the following function to find the failure/repair rates for links:…”

Section: Numerical Studiesmentioning

confidence: 99%

See 4 more Smart Citations

Effective Risk Assessment in Resilient Communication Networks

2016

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The paper discusses business impact analysis in the context of resilient communication networks. It is based on the total (aggregated) penalty that may be paid by an operator when the services (identified with transport demands) provided are interrupted due to network failures. The level of penalty is expressed as a commonly accepted business risk measure, Value-at-Risk (VaR). First, the main concern over VaR, namely the theoretical lack of subadditivity, is discussed. The study shows that, in practice, disadvantages do not appear in resilient network design, and VaR can be used without the need to apply more complex and less informative measures. Second, a method for calculating the upper bound of the total penalty is presented. The assessment is performed for unprotected and protected services with a broad variety of compensation policies used to translate technical loss to monetarily expressed penalty. The proposed bounds are experimentally shown to be effective in comparison with alternative calculation methods, and also in the case when some of the assumptions taken during the modelling stage are not met.

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Section: Unprotected Casementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Numerical Studiesmentioning

confidence: 99%

Section: Numerical Studiesmentioning

confidence: 99%

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Effective Risk Assessment in Resilient Communication Networks

2016

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Resilience

Metsälä¹

2012

Mobile Backhaul

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Managed Dependability in Interacting Systems

Heegaard

Helvik

Nencioni

et al. 2016

Springer Series in Reliability Engineering

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A digital ICT infrastructure must be considered as a system of systems in itself, but also in interaction with other critical infrastructures such as water distributions, transportation (e.g. Intelligent Transport Systems), and Smart Power Grid control. These systems are characterised by self-organisation, autonomous subsystems, continuous evolution, scalability and sustainability, providing both economic and social value. Services delivered involve a chain of stakeholders that share the responsibility, providing robust and secure services with stable and good performance.One crucial challenge for the different operation/control centers of the stakeholders is to manage dependability during normal operation, which may be characterised by many failures of minor consequence. In seeking to optimise the utilisation of the available resources with respect to dependability, new functionality is added with the intension to help assist in obtaining situational awareness, and for some parts enable autonomous operation. This new functionality adds complexity, such that the complexity of the (sub)systems and their operation will increase. As a consequence of adding a complex system to handle complexity, the frequency and severity of the consequences of such events may increase. Furthermore, as a side-effect of this, the preparedness will be reduced for restoration of services after a major event (that might involves several stakeholders), such as common software breakdown, security attacks, or natural disaster. This chapter addresses the dependability challenges related to the above mentioned system changes. It is important to understand how adding complexity to handle complexity will influence the risks, both with respect to the consequences and the probabilities. In order to increase insight, a dependability modelling approach is taken, where the goal is to combine and extend the existing modelling approaches in a novel way. The objective is to quantify different strategies for management of dePoul E Heegaard, Bjarne E. Helvik, Gianfranco Nencioni, Jonas Wäfler Norwegian University of Science and Technology, Department of Telematics, Trondheim, Norway, e-mail: {firstname.lastname}@item.ntnu.no 1 2 Poul E Heegaard, Bjarne E Helvik, Gianfranco Nencioni, Jonas Wäfler pendability in interacting systems. Two comprehensive system examples are used to illustrate the approach. A Software Defined Networking example addresses the effect of moving control functionality from being distributed and embedded with the primary function, to be separated and (virtually) centralised. To demonstrate and discuss the consequences of adding more functionality both in the distributed entities serving the primary function, and centralised in the control centre, a Smart Grid system example is studied.

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On/off process modeling of IP network failures

Cited by 21 publications

References 9 publications

Effective Risk Assessment in Resilient Communication Networks

Effective Risk Assessment in Resilient Communication Networks

Resilience

Managed Dependability in Interacting Systems

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