A self-adaptive deep learning-based model to predict cloud workload

Abstract

Predicting cloud workload is a problematic issue for cloud providers. Recent research has led us to a significant improvement in workload prediction. Although self-adaptive systems have an imperative impact on lowering the number of cloud resources, those still have to be more accurate, detailed and accelerated. A new self-adaptive technique based on a deep learning model to optimize and decrease the use of cloud resources is proposed. It is also demonstrated how to prognosticate incoming workload and how to manage available resources. The PlanetLab dataset in this research is used. The obtained results have been compared to other relevant designs. According to these comparisons with the state-of-theart deep learning methods, our proposed model encompasses a better prediction efficiency and enhances productivity by 5%.