Allocation and Migration of Virtual Machines Using Machine Learning

Talwani, Suruchi; Alhazmi, Khaled; Singla, Jimmy; Alyamani, Hasan J.; Bashir, Ali Kashif

doi:10.32604/cmc.2022.020473

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…e SVM was used with automatic recursive feature elimination, obtaining an accuracy of 96%. Another study wherein the SVM has been inducted for dyslexia detection on an eye tracking feature is reported in [20][21][22][23][24]. An accuracy of 80% on a dataset size of 97 is achieved in this work.…”

Section: Related Workmentioning

confidence: 92%

An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia

Aḥmad

Rehman

Hassan

et al. 2022

Computational Intelligence and Neuroscience

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Dyslexia is among the most common neurological disorders in children. Detection of dyslexia therefore remains an important pursuit for the research works across various domains which is illustrated by the plethora of work presented in diverse scientific articles. The work presented herein attempted to utilize the potential of a unified gaming test of subjects (dyslexia/controls) in tandem with principal components derived from data to detect dyslexia. The work aims to build a machine learning model for dyslexia detection using comprehensive gaming test data. We have attempted to explore the potential of various kernel functions of the Support Vector Machine (SVM) on different number of principal components to reduce the computational complexity. A detection accuracy of 92% is obtained from the radial basis function with 5 components, and the highest detection accuracy obtained from the radial basis function with 3 components is 93%. On the contrary, the Artificial Neural Network(ANN) shows an added advantage with minimal number of hyperparameters with 3 components for obtaining an accuracy of 95%. The comparison of the proposed method with some of the existing works shows efficacy of this method for dyslexia detection.

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Section: Related Workmentioning

confidence: 92%

An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia

Aḥmad

Rehman

Hassan

et al. 2022

Computational Intelligence and Neuroscience

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“…For instance, neural networks can enable traffic prediction and pattern recognition. Load balancing is an active cloud computing research area, with recent works focusing on the optimization, automation, and integration of machine learning [61].…”

Section: Load Balancingmentioning

confidence: 99%

Adaptive Threshold Tuning-based Load Balancing (ATTLB) for Cost Minimization in Cloud Computing

Khoshaim

2024

IJACSA

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Cloud computing has revolutionized the ondemand resource provisioning through virtualization. However, dynamic pricing of cloud resources presents cost management challenges. Load balancing is critical for cloud efficiency; however, current algorithms use static thresholds and are unable to adapt to fluctuating prices. This study proposes a novel Dynamic Threshold Tuning (ATTLB) algorithm that optimizes the CPU and memory thresholds of a load balancer based on real-time pricing. The ATTLB algorithm has a pricing monitor to track spot prices; a VM profiler to record capacities; a threshold optimizer to tune thresholds based on pricing, capacity, and SLAs; and a load dispatcher to assign requests to VMs using the optimized thresholds. Extensive simulations compare ATTLB with weighted round-robin (WRR), ant colony optimization (ACO), and least connection-based load balancing (LCLB) algorithms using the CloudSim toolkit. The results demonstrate the ability of ATTLB to reduce total costs by over 35% and improve SLA violations by 41% compared with prior techniques for cloud load balancing. Adaptive threshold tuning provides robustness against dynamic pricing and demand changes. ATTLB balances cost, performance, and utilization through realtime threshold adaptation.

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“…R={1,2,3,4}, S={ 5,6,7,8}, U={9,10,0,0},W={11,12,0,0}. Therefore, nest is denoted as {1,2,3,4} {5,6,7,8} {9,10,0,0} {11,12,0,0} [36]. CS is a metaheuristic algorithm which is designed considering the behaviour of the cuckoos.…”

Section: Optimization Using the Cuckoo Search (Cs)mentioning

confidence: 99%

An Energy Efficient VM Selection Using Updated Dragonfly Algorithm in Cloud Computing

Prashar

Thakur

2023

Preprint

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Cloud computing is popular among industries, academia, and government to supply reliable and scalable computational power. High speed networks in cloud data centers connect Virtual machines with Physical Machines. Virtualization assists the cloud service providers to manage resources effectively but unoptimized and inefficient services degrade the performance of the system. The scheduling architecture of cloud computing includes Physical Machines (PMs), Virtual Machines (VMs) and the allocation and migration policy of the VMs over the PMs. The overutilized PMs get a few migrations and this paper introduces a novel behaviour of VM selection from overutilized PM using Swarm intelligence. The evaluation of the proposed algorithm architecture is compared with other state of art optimization algorithm from the same series. The evaluation has been done on the base of Quality of Service (QoS) parameters such as SLA-Violation, energy consumption against various load variation scenario to support elasticity. The proposed work has outcasted other techniques with significant margin in terms of QoS and the illustrations are discussed in the result.

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Allocation and Migration of Virtual Machines Using Machine Learning

Cited by 9 publications

References 35 publications

An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia

An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia

Adaptive Threshold Tuning-based Load Balancing (ATTLB) for Cost Minimization in Cloud Computing

An Energy Efficient VM Selection Using Updated Dragonfly Algorithm in Cloud Computing

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