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Self-Organizing Neural Networks Integrated with Artificial Fish Swarm Algorithm for Energy-Efficient Cloud Resource Management

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Publicado en:International Journal of Advanced Computer Science and Applications vol. 16, no. 2 (2025)
Autor principal: PDF
Publicado:
Science and Information (SAI) Organization Limited
Materias:
Accuracy
Neural networks
Management methods
Cloud computing
Resource allocation
Resource scheduling
Workload
Power management
Algorithms
Resource management
Real time
Energy utilization
Workloads
Computer science
Optimization
Software services
Environmental impact
Energy consumption
Scheduling
Computer centers
Machine learning
Costs
Energy efficiency
Quality of service
Engineering
Acceso en línea:Citation/Abstract
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Resumen:Cloud computing's exponential expansion requires better resource management methods to solve the existing struggle between system performance and energy efficiency and functional scalability. Traditional resource management practices frequently lead systems in large-scale cloud environments to produce suboptimal results. This research presents a brand-new computational framework that unites Self-Organizing Neural Networks (SONN) with Artificial Fish Swarm Algorithm (AFSA) to enhance energy efficiency alongside optimized resource allocation and scheduling improvements. The SONN system groups workload information and automatically changes its structure to support fluctuating demand rates then the AFSA optimizes resource management through swarm-based intelligent protocols for high performance with scalable benefits. The SONN-AFSA model achieves substantial performance gains by analyzing real-world CPU usage statistics and memory usage behavior together with scheduling data from Google Cluster Data. The experimental findings show 20.83% lower energy utilization next to 98.8% prediction rates alongside 95% SLA maintenance and an outstanding 98% task execution rate. The proposed model delivers reliability outcomes superior to traditional approaches PSO and DRL and PSO-based neural networks which achieve accuracy rates above 88% and reach 92% accuracy. The adaptive platform delivers better power management to cloud computations yet preserves operational agility by adapting workload distributions. The learning ability of SONN joined with AFSA optimization segments produces superior resource direction capabilities which yield better service delivery quality. Research will proceed beyond its current scope to study real-time feedback structures as it evaluates multi-objective enhancement through large-scale dataset validation work to boost cloud computing sustainability across various platforms.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2025.01602105
Fuente:Advanced Technologies & Aerospace Database