New event-driven sampling techniques for network reliability estimation

Abdullah Konak; Alice E. Smith; Sadan Kulturel-Konak

New event-driven sampling techniques for network reliability estimation

Abdullah Konak, Alice E. Smith, Sadan Kulturel-Konak

Division of Engineering, Business & Computing (Berks)

Research output: Contribution to journal › Conference article › peer-review

11 Scopus citations

Abstract

Exactly computing network reliability measures is an NP-hard problem. Therefore, Monte Carlo simulation has been frequently used by network designers to obtain accurate estimates. This paper focuses on simulation estimation of network reliability. Using a heap data structure, efficient implementation of a previous approach, dagger sampling, is proposed. Two new techniques, geometric sampling and block sampling, are developed to efficiently sample states of a network. These techniques are event-driven rather than time-driven, and are thus efficient for highly reliable networks. To test relative performance, computational experiments are carried out on various types of networks using the new procedures.

Original language	English (US)
Pages (from-to)	224-231
Number of pages	8
Journal	Proceedings - Winter Simulation Conference
Volume	1
State	Published - Dec 1 2004
Event	Proceedings of the 2004 Winter Simulation Conference - Washington, DC, United States Duration: Dec 5 2004 → Dec 8 2004

All Science Journal Classification (ASJC) codes

Software
Modeling and Simulation
Computer Science Applications

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title = "New event-driven sampling techniques for network reliability estimation",

abstract = "Exactly computing network reliability measures is an NP-hard problem. Therefore, Monte Carlo simulation has been frequently used by network designers to obtain accurate estimates. This paper focuses on simulation estimation of network reliability. Using a heap data structure, efficient implementation of a previous approach, dagger sampling, is proposed. Two new techniques, geometric sampling and block sampling, are developed to efficiently sample states of a network. These techniques are event-driven rather than time-driven, and are thus efficient for highly reliable networks. To test relative performance, computational experiments are carried out on various types of networks using the new procedures.",

author = "Abdullah Konak and Smith, {Alice E.} and Sadan Kulturel-Konak",

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AU - Konak, Abdullah

AU - Smith, Alice E.

AU - Kulturel-Konak, Sadan

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N2 - Exactly computing network reliability measures is an NP-hard problem. Therefore, Monte Carlo simulation has been frequently used by network designers to obtain accurate estimates. This paper focuses on simulation estimation of network reliability. Using a heap data structure, efficient implementation of a previous approach, dagger sampling, is proposed. Two new techniques, geometric sampling and block sampling, are developed to efficiently sample states of a network. These techniques are event-driven rather than time-driven, and are thus efficient for highly reliable networks. To test relative performance, computational experiments are carried out on various types of networks using the new procedures.

AB - Exactly computing network reliability measures is an NP-hard problem. Therefore, Monte Carlo simulation has been frequently used by network designers to obtain accurate estimates. This paper focuses on simulation estimation of network reliability. Using a heap data structure, efficient implementation of a previous approach, dagger sampling, is proposed. Two new techniques, geometric sampling and block sampling, are developed to efficiently sample states of a network. These techniques are event-driven rather than time-driven, and are thus efficient for highly reliable networks. To test relative performance, computational experiments are carried out on various types of networks using the new procedures.

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M3 - Conference article

AN - SCOPUS:17744366852

VL - 1

SP - 224

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JO - Proceedings - Winter Simulation Conference

JF - Proceedings - Winter Simulation Conference

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T2 - Proceedings of the 2004 Winter Simulation Conference

Y2 - 5 December 2004 through 8 December 2004

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