Leveraging Machine and Deep Learning Models for Load Balancing Strategies in Cloud Computing DOI Open Access

C Thilagavathy

Indian Journal of Science and Technology, Год журнала: 2024, Номер 17(45), С. 4722 - 4731

Опубликована: Дек. 14, 2024

Objectives: To evaluate the efficiency of task prediction and resource allocation for load balancing (LB) in cloud environment using combined approach like random Forest(RF) Particle Swarm optimization Convolutional Neural Networks (PSO-CNN) allocation. Methods: The ensemble present study uses Random Forest (RF), a machine learning (ML) model Optimization (PSO+CNN), bio-inspired algorithm Deep Learning (DL) employs PSO techniques to optimize CNN order address investigation algorithmic DL. results show that suggested outperforms other models CNN-LSTM(Long Short-term memory), CNN-GRU(Gated Recurrent Unit), –SVM(Support Vector Machine) increase performance efficacy systems. experiment is implemented Python assessed Google Cluster dataset accessible public. Findings: use ML DL are found be more efficient infrastructure than conventional methods. examines RF, hybrid RF-PSO-CNN models. accuracy, precision, F1. Score metrics were used assess classification recommended them with an accuracy 90% contrasted methods CNN-LSTM, CNN- GRU PSO-SVM. As result, both assessment consumption proposed performs effectively. Novelty: novel suggests LB Computing. predicted by RF assigned chosen CNN, thereby improving Most research any two or either predicting tasks scheduled which allocate. combination (RF) method, (PSO) (CNN) concurrently it effectiveness context. Keywords: Load Balancing (LB), Task scheduling, Resource allocation, (CNN),

Язык: Английский

A fast-flying particle swarm optimization for resolving constrained optimization and feature selection problems DOI
Ajit Kumar Mahapatra, Nibedan Panda, Madhumita Mahapatra

и другие.

Cluster Computing, Год журнала: 2024, Номер 28(2)

Опубликована: Ноя. 26, 2024

Язык: Английский

Процитировано

0
Hybrid modeling techniques for predicting chemical oxygen demand in wastewater treatment: a stacking ensemble learning approach with neural networks DOI

S Ramya,

S Srinath,

Pushpa Tuppad

и другие.

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(12)

Опубликована: Ноя. 27, 2024

Язык: Английский

Процитировано

0
Modulation optimization method for seven-level SHEPWM inverter based on EPSO algorithm DOI Creative Commons

Renzheng Wang,

Yuncheng Zhang, Huiling Chen

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 30, 2024

Selective Harmonic Elimination Pulse Width Modulation (SHEPWM) has excellent harmonic characteristics, but its nonlinear transcendental system of equations is difficult to be solved, and the practical application encounters a bottleneck. In this paper, modulation optimization method for seven-level SHEPWM inverter based on Evolutionary Particle Swarm Optimization (EPSO) algorithm proposed address problem, so that quickly converges global optimum solution. The EPSO incorporates population strategy in two phases improve diversity real time. initialization phase, initialized optimized using Opposition-Based Learning (OBL) quality initial population. iterative stage, we combine adaptive (PSO) algorithm, Tunicate Algorithm (TSA), Adaptive Gaussian Variation, Quasi-Opposition-Based (QOBL) other methods solve problem insufficient process searching optimal solution, break through local optimum, convergence speed accuracy algorithm. Experiments 19 benchmark functions show ability ahead TSA, INFO, MA (Mayfly Algorithm), EO (Equilibrium Optimizer) algorithms. solution about three times PSO, which achieves fast highly accurate convergence, with small error output inverter, better distortion rate than standard requirement.

Язык: Английский

Процитировано

0
Leveraging Machine and Deep Learning Models for Load Balancing Strategies in Cloud Computing DOI Open Access

C Thilagavathy

Indian Journal of Science and Technology, Год журнала: 2024, Номер 17(45), С. 4722 - 4731

Опубликована: Дек. 14, 2024

Objectives: To evaluate the efficiency of task prediction and resource allocation for load balancing (LB) in cloud environment using combined approach like random Forest(RF) Particle Swarm optimization Convolutional Neural Networks (PSO-CNN) allocation. Methods: The ensemble present study uses Random Forest (RF), a machine learning (ML) model Optimization (PSO+CNN), bio-inspired algorithm Deep Learning (DL) employs PSO techniques to optimize CNN order address investigation algorithmic DL. results show that suggested outperforms other models CNN-LSTM(Long Short-term memory), CNN-GRU(Gated Recurrent Unit), –SVM(Support Vector Machine) increase performance efficacy systems. experiment is implemented Python assessed Google Cluster dataset accessible public. Findings: use ML DL are found be more efficient infrastructure than conventional methods. examines RF, hybrid RF-PSO-CNN models. accuracy, precision, F1. Score metrics were used assess classification recommended them with an accuracy 90% contrasted methods CNN-LSTM, CNN- GRU PSO-SVM. As result, both assessment consumption proposed performs effectively. Novelty: novel suggests LB Computing. predicted by RF assigned chosen CNN, thereby improving Most research any two or either predicting tasks scheduled which allocate. combination (RF) method, (PSO) (CNN) concurrently it effectiveness context. Keywords: Load Balancing (LB), Task scheduling, Resource allocation, (CNN),

Язык: Английский

Процитировано

0