A discrete stress–strength interference model based on universal generating function

A new approach was proposed to estimate the reliability of a machine component when the probability density functions of stress and strength can not be exactly determined or only finite experiment data of stress and strength are available. The conventional universal generating function was introduced and then it was extended to represent the discrete interval-valued random variable. The experimental data of stress and strength were formulated as two discrete interval-valued random variables. Based on the extended universal generating function, a discrete interval-valued stress-strength interference model was proposed. An approach was proposed to solve the proposed stress-strength interference model and it can be used to calculate the upper and lower bounds of the component reliability. An example was given to demonstrate the proposed approach. It is showed that the proposed approach is suitable to the reliability estimation of a machine component when only finite experimental data of stress and strength can be obtained.

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Backward Recursive Method for Structural Reliability Calculation Based on the Universal Generating Function

IEEE Transactions on Reliability ◽

10.1109/tr.2020.3047415 ◽

2021 ◽

pp. 1-10

Author(s):

Jinyu Zhou ◽

Liyang Xie ◽

Wenqin Han

Keyword(s):

Generating Function ◽

Structural Reliability ◽

Recursive Method ◽

Reliability Calculation ◽

Universal Generating Function

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A Fast Optimization Method for Reliability and Performance of Cloud Services Composition Application

Journal of Applied Mathematics ◽

10.1155/2013/407267 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Zhao Wu ◽

Naixue Xiong ◽

Yannong Huang ◽

Qiong Gu ◽

Chunyang Hu ◽

...

Keyword(s):

Cloud Computing ◽

State Space ◽

Generating Function ◽

Performance Optimization ◽

Optimization Method ◽

Cloud Services ◽

Services Composition ◽

Fast Optimization ◽

And Performance ◽

Universal Generating Function

At present the cloud computing is one of the newest trends of distributed computation, which is propelling another important revolution of software industry. The cloud services composition is one of the key techniques in software development. The optimization for reliability and performance of cloud services composition application, which is a typical stochastic optimization problem, is confronted with severe challenges due to its randomness and long transaction, as well as the characteristics of the cloud computing resources such as openness and dynamic. The traditional reliability and performance optimization techniques, for example, Markov model and state space analysis and so forth, have some defects such as being too time consuming and easy to cause state space explosion and unsatisfied the assumptions of component execution independence. To overcome these defects, we propose a fast optimization method for reliability and performance of cloud services composition application based on universal generating function and genetic algorithm in this paper. At first, a reliability and performance model for cloud service composition application based on the multiple state system theory is presented. Then the reliability and performance definition based on universal generating function is proposed. Based on this, a fast reliability and performance optimization algorithm is presented. In the end, the illustrative examples are given.

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