Load balancing is essential in cloud computing (CC) to manage the increasing load on servers efficiently. This article proposes a load balancing strategy utilizing constraint measures to distribute the load evenly amo...
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Load balancing is essential in cloud computing (CC) to manage the increasing load on servers efficiently. This article proposes a load balancing strategy utilizing constraint measures to distribute the load evenly amongst the servers while minimizing power consumption. Firstly, the capacity and load of every Virtual Machine (VM) is evaluated, and tasks are assigned using the african vultures algorithm (AVA) when the load exceeds a predefined threshold. This approach aim is to minimize energy consumption, makespan, and data center usage. Additionally, a load balancing method computes critical features for each VM and assesses their load, followed by calculating selection factors for tasks. Tasks with superior selection factors are assigned to VMs. The proposed Efficient Load Balancing in Cloud Computing under african vultures algorithm (ELB-CC-AVA) demonstrates better performance in cloud environments, achieving lower makespan by 32.82%, 30.47%, and 25.32%, along with higher resource utilization rates of 38.22%, 40.21%, and 25.46% compared to the existing methods.
This study presents a novel method, termed RBAVO-DE (Relief Binary africanvultures Optimization based on Differential Evolution), aimed at addressing the Gene Selection (GS) challenge in high-dimensional RNA-Seq data...
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This study presents a novel method, termed RBAVO-DE (Relief Binary africanvultures Optimization based on Differential Evolution), aimed at addressing the Gene Selection (GS) challenge in high-dimensional RNA-Seq data, specifically the rnaseqv2 lluminaHiSeq rnaseqv2 un edu Level 3 RSEM genes normalized dataset, which contains over 20,000 genes. RNA Sequencing (RNA-Seq) is a transformative approach that enables the comprehensive quantification and characterization of gene expressions, surpassing the capabilities of micro-array technologies by offering a more detailed view of RNA-Seq gene expression data. Quantitative gene expression analysis can be pivotal in identifying genes that differentiate normal from malignant tissues. However, managing these high-dimensional dense matrix data presents significant challenges. The RBAVO-DE algorithm is designed to meticulously select the most informative genes from a dataset comprising more than 20,000 genes and assess their relevance across twenty-two cancer datasets. To determine the effectiveness of the selected genes, this study employs the Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) classifiers. Compared to binary versions of widely recognized meta-heuristic algorithms, RBAVO-DE demonstrates superior performance. According to Wilcoxon's rank-sum test, with a 5% significance level, RBAVO-DE achieves up to 100% classification accuracy and reduces the feature size by up to 98% in most of the twenty-two cancer datasets examined. This advancement underscores the potential of RBAVO-DE to enhance the precision of gene selection for cancer research, thereby facilitating more accurate and efficient identification of key genetic markers.
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