Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend

The challenges of the conventional cloud computing paradigms motivated the emergence of the next generation cloud computing architectures. The emerging cloud computing architectures generate voluminous amount of data that are beyond the capability of the shallow intelligent algorithms to process. Deep learning algorithms, with their ability to process large-scale datasets, have recently started gaining tremendous attentions from researchers to solve problem in the emerging cloud computing architectures. However, no comprehensive literature review exists on the applications of deep learning architectures to solve complex problems in emerging cloud computing architectures. To fill this gap, we conducted a comprehensive literature survey on the applications of deep learning architectures in emerging cloud computing architectures. The survey shows that the adoption of deep learning architectures in emerging cloud computing architectures are increasingly becoming an interesting research area. We introduce a new taxonomy of deep learning architectures for emerging cloud computing architectures and provide deep insights into the current state-of-the-art active research works on deep learning to solve complex problems in emerging cloud computing architectures. The synthesis and analysis of the articles as well as their limitation are presented. A lot of challenges were identified in the literature and new future research directions to solve the identified challenges are presented. We believed that this article can serve as a reference guide to new researchers and an update for expert researchers to explore and develop more deep learning applications in the emerging cloud computing architectures. (C) 2020 Elsevier B.V. All rights reserved.