Estimating the Response Time of a Cloud Computing System with the Help of Neural Networks

Q3 Engineering

Advances in Systems Science and Applications Pub Date : 2020-09-30 DOI:10.25728/ASSA.2020.20.3.926

A. Gorbunova, V. Vishnevsky

引用次数: 3

Abstract

The article presents a new approach to assessing the average response time of a cloud computing system and its dispersion. A fork-join system or a system with request splitting was chosen as a queuing model, and artificial neural networks were used as a method for estimating a variable of interest. The analysis showed that the estimates obtained were more accurate than those previously known. Besides, the proposed approach allows expanding the analysis of the cloud system to the case of a model with a non-Poisson input stream and non-exponential service time, as well as obtaining estimates for a larger number of performance indicators of the cloud system, which was not previously possible.

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利用神经网络估计云计算系统的响应时间

本文提出了一种评估云计算系统平均响应时间及其离散度的新方法。选择分叉连接系统或请求分离系统作为排队模型，并使用人工神经网络作为估计感兴趣变量的方法。分析表明，获得的估计比以前已知的估计更准确。此外，所提出的方法允许将云系统的分析扩展到具有非泊松输入流和非指数服务时间的模型的情况，以及获得云系统的大量性能指标的估计，这在以前是不可能的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advances in Systems Science and Applications Engineering-Engineering (all)

CiteScore

1.20

自引率

0.00%

发文量

期刊介绍： Advances in Systems Science and Applications (ASSA) is an international peer-reviewed open-source online academic journal. Its scope covers all major aspects of systems (and processes) analysis, modeling, simulation, and control, ranging from theoretical and methodological developments to a large variety of application areas. Survey articles and innovative results are also welcome. ASSA is aimed at the audience of scientists, engineers and researchers working in the framework of these problems. ASSA should be a platform on which researchers will be able to communicate and discuss both their specialized issues and interdisciplinary problems of systems analysis and its applications in science and industry, including data science, artificial intelligence, material science, manufacturing, transportation, power and energy, ecology, corporate management, public governance, finance, and many others.