Solving the response time variability problem by means of a genetic algorithm

García‐Villoria, Alberto; Pastor, Rafael

doi:10.1016/j.ejor.2009.05.024

Cited by 17 publications

(8 citation statements)

References 26 publications

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“…5. Once the rate at which components should be checked has been determined, we use the second GA, as detailed in [28], to generate a schedule in which the determined number of voter checks are distributed as evenly as possible per schedule period. We refer to the second GA as the inner GA in Fig.…”

Section: Scheduling Voter Checksmentioning

confidence: 99%

Scheduling configuration memory error checks to improve the reliability of FPGA‐based systems

Nguyen

Cetin²,

Diessel³

2018

IET Computers & Digital Techniques

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Field-programmable gate arrays are susceptible to radiation-induced single event upsets. These are commonly dealt with using triple modular redundancy (TMR) and module-based configuration memory error recovery (MER). By triplicating components and voting on their outputs, TMR helps localise configuration memory errors, and by reconfiguring faulty components, MER swiftly corrects them. However, the order in which TMR voters are checked inevitably impacts the overall system reliability. In this study, the authors outline an approach for computing the reliability of TMR-MER systems that consist of finitely many components. They demonstrate that system reliability is improved when the more vulnerable components are checked more frequently than when they are checked in round-robin order. They propose a genetic algorithm for finding a voter checking schedule that maximises the reliability of TMR-MER systems. Results indicate that the mean time to failure (MTTF) of these systems can be increased by up to 400% when variable-rate voter checking (VRVC) is used instead of round robin. They show that VRVC achieves 15-23% increase in MTTF with a 10× reduction in checking frequency to reduce system power. They also found that VRVC detects errors 44% faster on average than round robin. Nomenclature Symbol Definition N number of TMR components in the system Ck component k, k = 1, …, N O kn Ck is observed for the nth time by checking its voter(s) Δt o time period between successive voter observations (assumed to be constant for a given system setting) Δt dk time period between two consecutive observations of Ck Δt rk time period to recover a faulty module of Ck Δt k total time period over which Ck can fail Δt di j time period between successive observations of Ci and Cj Δt d′i j average time period between two consecutive observations of Ci in the interval between two consecutive observations of Cj IET Comput. Digit. Tech.

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Section: Scheduling Voter Checksmentioning

confidence: 99%

Scheduling configuration memory error checks to improve the reliability of FPGA‐based systems

Nguyen

Cetin²,

Diessel³

2018

IET Computers & Digital Techniques

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“…Genetic algorithms have been applied successfully in many scientific and engineering fields (for an overview see Goldberg (1989) the monographs by Michalewicz (1996) or Gen and Cheng (2000), the references in these papers, and more recent articles (Aryanezhad and Hemati, 2008;Ibánez et al, 2009;Martínez et al, 2009;García-Villoria and Pastor, 2010;Omaraa and Arafa, 2010;Agustı´n-Blas et al, 2012;Gálvez et al, 2012), and other related papers (Jiang et al, 2009;Wilamowski, 2010;Li et al, 2011aLi et al, , 2011bWang et al, 2011;Duan and Xu, 2012;Xing et al, 2013). To overcome certain drawbacks, such as low computation efficiency, premature and slow convergence to the global minimum, many variations of GAs have been developed.…”

Section: Internet-of-thingsmentioning

confidence: 99%

BSP‐GA: A new Genetic Algorithm for System Optimization and Excellent Schema Selection

Jin

Wilamowska-Korsak

et al. 2014

Syst. Res.

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The significance of Internet‐of‐Things to Supply Chain Management has been dramatically increasing. The performance of supply chain based on Internet‐of‐Things is largely dependent on its optimization. Genetic algorithms (GAs) are important intelligent methods for complex system optimization problems, but they have some internal drawbacks such as premature and slow convergence to the global optimum. In this paper, we present a new schema protection based GA (BSP‐GA). First, we propose three principles for selecting excellent schema based on the schema theory; second, we propose the concept of K‐intensive effect synthesis operator, and we give a general five‐intensive effect synthesis operator and its proof; third, we give the selection process of excellent schema through an example, and further we give the implementation steps of BSP‐GA. The performance of BSP‐GA has been compared with simple GA by using two carefully chosen benchmark problems. It has been observed that BSP‐GA can yield the global optimum more efficiently than commonly used simple GA. Furthermore, a theorem is presented to guarantee the convergence of BSP‐GA. Copyright © 2014 John Wiley & Sons, Ltd.

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“…We also compare the results of our heuristic with the best results obtained by the GA of García-Villoria and Pastor (2010c). In this scenario, a set of 740 test instances is used instead.…”

Section: Comparison Vs Metaheuristicsmentioning

confidence: 99%

“…Seven new heuristics are also given by Corominas et al (2009). Metaheuristics for the RTVP were recently proposed in García-Villoria and Pastor (2010a, 2010b, 2010c and these include an electromagnetism-like mechanism (EM) algorithm, a psychoclonal algorithm and a genetic algorithm (GA) respectively.…”

Section: Introductionmentioning

confidence: 99%

An adaptive search for the response time variability problem

Salhi

García‐Villoria

2012

Journal of the Operational Research Society

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Abstract. The Response Time Variability Problem (RTVP) is an NP-hard combinatorial scheduling problem which has recently been reported and formalised in the literature. This problem has a wide range of real-world applications in mixed-model assembly lines, multithreaded computer systems, broadcast of commercial videotapes and others. The RTVP arises whenever products, clients or jobs need to be sequenced in such a way that the variability in the time between the points at which they receive the necessary resources is minimised. We propose a greedy but adaptive heuristic that avoids being trapped into a poor solution by incorporating a look ahead strategy suitable for this particular scheduling problem. The proposed heuristic outperforms the best existing methods, while being much faster and easier to understand and to implement.

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Solving the response time variability problem by means of a genetic algorithm

Cited by 17 publications

References 26 publications

Scheduling configuration memory error checks to improve the reliability of FPGA‐based systems

Scheduling configuration memory error checks to improve the reliability of FPGA‐based systems

BSP‐GA: A new Genetic Algorithm for System Optimization and Excellent Schema Selection

An adaptive search for the response time variability problem

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