Hybrid Bio-Inspired Optimization-based Cloud Resource Demand Prediction using Improved Support Vector Machine

• Published in: International journal of advanced computer science & applications 2024, Vol.15 (1)
• Main Authors: Sanjay, Nisha, Sreedharan, Sasikumaran
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Classification; Cloud computing; Consumption; Feature selection; Genetic algorithms; Machine learning; Neural networks; Particle swarm optimization; Support vector machines
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2024.0150177

In order to furnish diverse resource requirements in cloud computing, numerous resources are integrated into a data centre. How to deliver resources in a timely and accurate manner to meet user expectations is a significant concern. However, the resource demands of users fluctuate greatly and frequently change regularly. It's possible that the resource provision won't happen on time. Furthermore, because some physical resources are shut down to save energy, there may occasionally not be enough of them to meet user requests. Therefore, it's critical to offer resource provision proactively to ensure positive user involvement using cloud computing. To enable resource provision in advance, it is essential to accurately estimate future resource demands. Using machine learning techniques, we offer a unique approach in this study that tries to identify key features, accelerating the forecast of cloud resource consumption. Finding the classification method with the greatest fit and maximum classification accuracy is crucial when predicting cloud resource consumption. The attribute selection method is used to decrease the dataset. The categorization process is then given the reduced data. The hybrid attribute selection method used in the investigation, which combines the bio-inspired algorithm genetic algorithm, the pulse-coupled neural network, and the particle swarm optimization algorithm, improves classification accuracy. The accuracy of prediction employing this technique is examined using a variety of performance criteria. When it comes to predicting the demand for cloud resources, the experimental results show that the suggested machine learning method performs more effectively than traditional machine learning models.