Distribution of virtual machines with SVM-FFDM approach in cloud computing

Abstract

Virtual machine (VM) distribution in cloud computing plays a pivotal role in optimizing resource allocation and improving overall system performance. This study proposes a novel approach for efficient VM distribution using a combination of support vector machine (SVM) and the finest fit decreasing modifier (FFDM) algorithm. SVM is employed to classify and predict resource utilization patterns, ensuring that VMs are allocated based on predicted workloads. The FFDM algorithm, a modified version of the traditional first fit decreasing (FFD) algorithm, is then applied to optimize the packing of VMs onto physical servers by minimizing resource wastage and enhancing load balancing. By integrating machine learning techniques with optimization algorithms, the proposed approach achieves a more effective VM allocation strategy, leading to improved system efficiency, reduced energy consumption, and enhanced scalability in cloud environments. Simulation results demonstrate the superior performance of the SVM-FFDM method compared to traditional VM allocation techniques in terms of resource utilization and operational cost.