Machine learning model design for high performance cloud computing & load balancing resiliency: An innovative approach

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine learning model design for high performance cloud computing & load balancing resiliency: An innovative approach

Popis výsledku v původním jazyce

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&apos;s existing non-machine learning based design models. (C) 2022 The Author(s)

Název v anglickém jazyce

Machine learning model design for high performance cloud computing & load balancing resiliency: An innovative approach

Popis výsledku anglicky

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&apos;s existing non-machine learning based design models. (C) 2022 The Author(s)

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of King Saud University - Computer and Information Sciences

ISSN

1319-1578

e-ISSN

1319-1578

Svazek periodika

Volume 34

Číslo periodika v rámci svazku

Issue 10

Stát vydavatele periodika

SA - Království Saúdská Arábie

Počet stran výsledku

19

Strana od-do

"9991 "- 10009

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85140585768