Active redundancy allocation for a k-out-of-n:F system of dependent components

Bueno, Vanderlei da Costa; Carmo, Iran Martins do

doi:10.1016/j.ejor.2005.09.012

Cited by 45 publications

(17 citation statements)

References 11 publications

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“…This topic has posed many interesting problems to which many researchers have devoted themselves in the past decades; see, for example, Boland, El-Neweihi, and Proschan (1992); Shaked and Shanthikumar (1992); Singh and Misra (1994); Singh and Singh (1997); Mi (1998); Ramirez-Marquez and Coit (2004) ;Yalaoui, Chu, and Châtelet (2005); da Costa Bueno (2005); Valdés and Zequeira (2006); da Costa Bueno and do Carmo (2007); Hu and Wang (2009) ;Valdés, Arango, and Zequeira (2010) ;Brito, Zequeira, and Valdès (2011);Misra, Dhariyal, and Gupta (2009) ;Misra, Misra, and Dhariyal (2011a,b); Zhao, Chan, and Ng (2012) ;Zhao, Chan, Li, and Ng (2013a,b); Laniado and Lillo (2014) and the references therein. In general, there are two types of redundancies called active redundancy and standby redundancy commonly used in reliability engineering and system security.…”

Section: Introductionmentioning

confidence: 99%

Redundancy allocation at component level versus system level

Zhao

Zhang

2015

European Journal of Operational Research

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Section: Introductionmentioning

confidence: 99%

Redundancy allocation at component level versus system level

Zhao

Zhang

2015

European Journal of Operational Research

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“…In [31], GA integrated with fuzzy theory was suggested to solve the RAP of series-parallel CS homogeneous systems where one type of elements can only be replaced with the same type of elements. In [32], a hybrid intelligent algorithm based on GA, neural networks, and fuzzy theory was proposed to solve the RAP of systems that combine CS and active redundancy technique (redundant elements are configured in parallel to primary system elements without using switching mechanisms [6], [33]). Later on, a multiobjective version of GA was applied to optimize the similar combined system [34].…”

Section: Introductionmentioning

confidence: 99%

Optimal Backup Distribution in 1-out-of-<inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> Cold Standby Systems

Levitin

Xing

2015

IEEE Trans. Syst. Man Cybern, Syst.

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This paper considers nonrepairable 1-out-of-N: G cold standby (CS) systems subject to uneven backup actions as well as dynamic backup and retrieval times. In such systems, only one element is online and operates with the rest of the elements waiting in the unpowered CS mode. The operating element performs data backup actions when certain fractions of the mission task are accomplished. The backup actions facilitate data recovery in case of an operating element failure, which allows an activated standby element to take over the task through data retrieval. Backup distribution can have a nonmonotonic effect on mission reliability, time, and cost, leading to the optimal backup distribution problem. In this paper, we first suggest a numerical method to model and evaluate mission reliability, expected time, and cost simultaneously for the considered CS systems with uneven backup actions and dynamic backup and retrieval times. Based on the suggested evaluation method and genetic algorithm, the optimal backup distribution problem is then formulated and solved with the objective to minimize the expected mission cost subject to meeting certain levels of mission reliability and expected mission time. Examples show that the proposed methodology can facilitate a tradeoff study between mission reliability, and time and cost, which assists in the optimal decisionmaking for the backup policy used in the practical design of CS systems.

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“…When some components are broken down, the dependence of working components was characterized by Yun et. al [15] and Bueno & Carmo [7].…”

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confidence: 96%

Optimal Updating Time Using Theory of Reliability

Xiong

Korostelev

et al. 2008

2008 14th IEEE International Conference on Parallel and Distributed Systems

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In this paper, we use reliability theory to determine the answer of an important question about how often we need to update a piece of secure information. For example, one may need to determine an appropriate time for updating a new password. To generalize, we compute an efficient updating time for a dependable secure computing (DSC) system which stores secret information in a reliable and secure fashion among n servers. The secret information can be reconstructed by successfully connecting to any k servers (k ≤ n), but cannot be revealed by using (k − 1) or fewer servers. Generally, the secret information has to be updated in the system in order to prevent adversaries from learning secret information by breaking into k servers in a DSC system. There are serval proposed procedures to update secret information, but few has been able to determine what an appropriate updating time on a DSC system is. To achieve the most reliable way to safeguard the secret information, we developed an interesting approach to compute the updating time on a DSC system from the reliability point of view.

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Active redundancy allocation for a k-out-of-n:F system of dependent components

Cited by 45 publications

References 11 publications

Redundancy allocation at component level versus system level

Redundancy allocation at component level versus system level

Optimal Backup Distribution in 1-out-of-<inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> Cold Standby Systems

Optimal Updating Time Using Theory of Reliability

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