Risk-awareness in resilient networks design: Value-at-risk is enough

Chołda, Piotr; Guzik, Piotr; Rusek, Krzysztof

doi:10.1109/netwks.2014.6958530

Cited by 6 publications

(14 citation statements)

References 26 publications

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“…The simplest to interpret is the mean penalty per interval, known as risk exposure (RE): it is the average amount of penalties that reduce the profit. It is also coherent [4], a useful feature in the context of convex optimization [17]. Nevertheless, it does not grasp the variability of the impact or extreme values (i.e., two totally different distributions may have the same mean value [31]).…”

Section: Risk Assessmentmentioning

confidence: 99%

Energy-efficiency versus resilience: risk awareness view on dimensioning of optical networks with a sleep mode

Chołda

Jaglarz

2015

Photon Netw Commun

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This article proposes to solve the trade-off between energy-efficiency and resilience with a focus on business mechanisms. Risk engineering is used as a foundation. Financial impact (penalty) quantification with various compensation policies is applied, and business-relevant risk measures are used during the risk assessment. Then, risk mitigation strategies are evaluated to select the appropriate risk response. The approach is presented in networks with energy profiles supporting a sleep mode. An effective heuristic is used to assign flows, and it is shown that the energy-efficiency performance is substantially independent of the recovery methods selected for risk mitigation. It is also demonstrated that backup resources can be switched off in the normal state without having a considerable impact from a financial viewpoint.

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Section: Risk Assessmentmentioning

confidence: 99%

Energy-efficiency versus resilience: risk awareness view on dimensioning of optical networks with a sleep mode

Chołda

Jaglarz

2015

Photon Netw Commun

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“…This contribution can be treated as an extension of the two previously published papers [6,7]. The new contribution can be summarized as follows: (1) The first paper [6] elaborates on the issue of lack of subadditivity of VaR, and shows that this risk measure can be successfully used in communications since lack of subadditivity is not a concern in practice.…”

Section: Related Workmentioning

confidence: 99%

“…The new contribution can be summarized as follows: (1) The first paper [6] elaborates on the issue of lack of subadditivity of VaR, and shows that this risk measure can be successfully used in communications since lack of subadditivity is not a concern in practice. Here, we extend the set of the numerical studies to confirm this statement, especially by broadening the set of investigated distributions and taking into account node failures.…”

Section: Related Workmentioning

confidence: 99%

“…3.3 below), the derivative measures were proposed. However, although new methods for their efficient assessment have recently appeared [51,52], in many cases their values are still at least one level of magnitude higher [6]. Thus, they cannot be treated as an efficient basis for investments and network design decisions.…”

Section: Probabilistic Risk Measuresmentioning

confidence: 99%

“…Secondly, businesses may be more interested in the variability of loss rather than its mean value, which does not usually capture changes in network behaviour. Here, we use our previous works on the shift of interest in resilient networks design [5][6][7] as a basis. There, we discuss using business-related risk measures, such as Value-at-Risk (VaR).…”

Section: Introductionmentioning

confidence: 99%

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Effective Risk Assessment in Resilient Communication Networks

2016

Self Cite

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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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