Virtualized cloud optimizer for predicting cloudlets

Abstract

Virtualization is one of the important factors in cloud computing to select the cloud service and model. Different cloud applications are providing services at the platform level and it varies depending upon application services. Load balancing is a major issue while accessing the resources. Cloudlet is the device or machine or computing tool to access the resources when it is required. An optimizer is required to predict the user profile based on active users and cloud services. In this research, the proposed method model identifies a solution for efficient load balancing by considering factors such as processing time, and response time to reduce carbon footprint in the cloud computing environment The Proposed algorithm is based on the genetic algorithm Ant colony optimization which uses path cost and threshold. The major components are the User Base, Datacenter selector, Virtual Machine (VM) selector and allocator and Efficiency analyzer. In this work, we provide virtualized Cloud Optimizer for selecting the cloud services and ranking the Cloudlets. The experiments are done by using CloudSim and the dataset is selected from the UCI repository.