An Ant Colony Optimization Algorithm for Virtual Machine Placement in Cloud Computing.

Abstract

The cloud computing (CC) environment is defined as the separation of workload, computing characteristics, and more. In the future, companies will manage workload or supply requests by allocating resources to computers to schedule tasks. , network or server. In this research, a new planning model is proposed as a hybrid of ant colony optimization techniques (ACO+). This technique increases the ant population to reduce the search space and allows the ACO technique to identify extensive paths accordingly. The Ant Colony Optimization (ACO) algorithm works by selecting the best virtual machine at the shortest possible distance from a point to a straight line. Use point-to-line spacing. The best virtual machine is selected from this. The submitted ACO+ implements an efficient method for identifying the best VMs that consume the least energy and improves fundamental tools such as overall response time for proper resource VM placement and optimization tasks. The results of this coupling simulation show that the submitted “Anti-Colony-Optimization-Plus” shows effective performance compared to other algorithms. Simulation Results: The Ant Colony Optimization Plus algorithm achieves effective results with a minimum energy consumption of 72%. This is superior to all comparison algorithms like K-Means clustering and Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), etc.