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New Algorithm for Predicting the States of a Computer Network Using Multivalued Dependencies

New Algorithm for Predicting the States of a Computer Network Using Multivalued Dependencies The algorithm that allows based on the analysis of monitoring historical data to predict the state of the network has been proposed. It is aimed to evaluate the state of the network using the service level indicators Service Level Objectives, SLO. The algorithm is based on the search for statistically stable multivalued dependencies. For forecasting, combinations of arguments are calculated, obtained by imposing masks, on the basis of which, in turn, statistical patterns are formed. To search for repeating patterns a history data size is determined. Each stage of the algorithm is illustrated with pseudocode. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Control and Computer Sciences Springer Journals

New Algorithm for Predicting the States of a Computer Network Using Multivalued Dependencies

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References (31)

Publisher
Springer Journals
Copyright
Copyright © Allerton Press, Inc. 2023. ISSN 0146-4116, Automatic Control and Computer Sciences, 2023, Vol. 57, No. 1, pp. 48–60. © Allerton Press, Inc., 2023.
ISSN
0146-4116
eISSN
1558-108X
DOI
10.3103/s0146411623010091
Publisher site
See Article on Publisher Site

Abstract

The algorithm that allows based on the analysis of monitoring historical data to predict the state of the network has been proposed. It is aimed to evaluate the state of the network using the service level indicators Service Level Objectives, SLO. The algorithm is based on the search for statistically stable multivalued dependencies. For forecasting, combinations of arguments are calculated, obtained by imposing masks, on the basis of which, in turn, statistical patterns are formed. To search for repeating patterns a history data size is determined. Each stage of the algorithm is illustrated with pseudocode.

Journal

Automatic Control and Computer SciencesSpringer Journals

Published: Feb 1, 2023

Keywords: quality of service; forecasting algorithm; sequential analysis; pattern; multivalued dependency; network traffic

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