A Novel Feature Extraction Model for Large-Scale Workload Prediction in Cloud Environment

Abstract

In an enterprise cloud environment, it is difficult to handle an extensive number of loads. Serving the request in very less time leads to resource allocation problem. It is better to have prior knowledge of the incoming loads to auto-scale the resources. A novel architecture is proposed for the better prediction of workloads in the cloud environment. The proposed feature extraction model considers three essentials for managing cloud resources, i.e., CPU, Disk, and Memory. The model with the very nominal error achieved an accuracy of 98.72%. The proposed model is contrasted with other conventional predictive models for validation. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.