Comparison of Multiple Regression Particle Swarm Optimization Algorithms and Multiple Regression Multi-Objective Seven-Spot Ladybird Optimization for Host Overload / Under-Loaded Detection in Cloud Datacenter-Reference-Cited by-同舟云学术

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Comparison of Multiple Regression Particle Swarm Optimization Algorithms and Multiple Regression Multi-Objective Seven-Spot Ladybird Optimization for Host Overload / Under-Loaded Detection in Cloud Datacenter

Published:2023-10-10 Issue: Volume: Page:
ISSN:
Container-title:2023 3rd International Conference on Emerging Smart Technologies and Applications (eSmarTA)
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Author:

Aqlan Alhammadi Akram Saeed¹,Raman Jayakrishnan Anilakkad²,Mosleh Mohammed A.S.³,Ali Abdullah Hesham Mohammed⁴

Affiliation:

1. IT college, Taiz University. Aljanad University,Alsaeed faculty Engineering,Engineering of Computer networkand cyber security department,Taiz,Yemen

2. Cyber forensic Consultant CyberSec Analytics,Kerala,India

3. Al-Fayha College Jubail Industrial City,Department of Computer Science and Engendering,Saudi Arabia

4. Jouf University,Department of Computer science and IT,Sakaka,KSA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10293182/10293222/10293580.pdf?arnumber=10293580

Reference23 articles.

1. A learning automata-based algorithm for energy and SLA efficient consolidation of virtual machines in cloud data centers

2. Tackling uncertainty in long-term predictions for host overload and underload detection in cloud computing;minarolli;Journal of cloud computing,2017

3. Markov Prediction Model for Host Load Detection and VM Placement in Live Migration

4. A prediction-based vm consolidation approach in iaas cloud data centers;haithem mezni;The Journal of Systems & Software,2018

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