Machine learning model design for high performance cloud computing & load balancing resiliency: An innovative approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10252045" target="_blank" >RIV/61989100:27240/22:10252045 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1319157822003524?pes=vor" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1319157822003524?pes=vor</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jksuci.2022.10.001" target="_blank" >10.1016/j.jksuci.2022.10.001</a>
Alternative languages
Result language
angličtina
Original language name
Machine learning model design for high performance cloud computing & load balancing resiliency: An innovative approach
Original language description
High performance computing is now a major area where business and computing technologies need resilient high performance to meet business continuity and real-time needs. However, many top-level business and technology organizations are still in the process of improving high performance and traffic resiliency to ensure the availability of the system at all times. Machine learning is an important advancement of computer technology that helps in decision making by prediction and classification mechanism based on historical data. In this paper, we propose and integrate the concept of high-performance computing with artificial intelligence machine learning techniques in cloud platforms. The networking and computing performance data are used to validate, predict and classify the traffic and performance patterns and ensure system performance and continuous traffic flow resiliency decisions. The proposed integrated design approach has been analyzed on different step actions and decisions based on machine learning regression and classification models, which auto-correct the performance of the system at real run time instances. Our machine learning integrated design simulated results show its traffic resilience performs proactively 38.15% faster with respect to the failure point recovery along with 7.5% business cost savings as compared to today's existing non-machine learning based design models. (C) 2022 The Author(s)
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of King Saud University - Computer and Information Sciences
ISSN
1319-1578
e-ISSN
1319-1578
Volume of the periodical
Volume 34
Issue of the periodical within the volume
Issue 10
Country of publishing house
SA - THE KINGDOM OF SAUDI ARABIA
Number of pages
19
Pages from-to
"9991 "- 10009
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85140585768