Using virtualized multimedia tools for video conferencing solution integrated in teaching and learning environment

Khan, Asia

doi:10.1080/09720529.2021.2014137

Cited by 2 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conventional load-balancing strategies may not be as effective in cloud workloads due to their dynamic nature. Still, hybrid metaheuristics are designed to adjust to shifting circumstances and may respond quickly to workload variations [27,28]. The method seeks to optimize scheduling, resource utilization, and VM throughput by fusing the Artificial Bee Colony Algorithm (ABC) with the Qlearning algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Load Balancing in Cloud Computing: Optimized RL-Based Clustering with Multi-Objective Optimized Task Scheduling

Khan

2024

Processes

Self Cite

View full text Add to dashboard Cite

Dynamic load balancing in cloud computing is crucial for efficiently distributing workloads across available resources, ensuring optimal performance. This research introduces a novel dynamic load-balancing approach that leverages a deep learning model combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to calculate load values for each virtual machine (VM). The methodology aims to enhance cloud performance by optimizing task scheduling and stress distribution. The proposed model employs a dynamic clustering mechanism based on computed loads to categorize VMs into overloaded and underloaded clusters. To improve clustering efficiency, the approach integrates Reinforcement Learning (RL) with a sophisticated Hybrid Lyrebird Falcon Optimization (HLFO) algorithm. HLFO merges the Lyrebird Optimization Algorithm (LOA) and Falcon Optimization Algorithm (FOA), enhancing the effectiveness of load balancing. A Multi-Objective Hybrid Optimization model is introduced to optimize task scheduling while considering Quality of Service (QoS) parameters, including makespan minimization, energy consumption reduction, balanced CPU utilization, efficient memory usage, and task prioritization. The implementation, conducted in Python and CloudSim, demonstrates the model’s ability to effectively allocate work between virtual machines (VMs) and physical machines (PMs), resulting in improved resource utilization, shortened makespan, enhanced CPU usage, and rigorous assessments affirming its efficacy. This research addresses the complexity of dynamic load balancing in cloud environments by combining deep learning, reinforcement learning, and hybrid optimization techniques, offering a comprehensive solution to optimize cloud performance under varying workloads and resource conditions.

show abstract

Section: Literature Reviewmentioning

confidence: 99%