Deep Learning Architectures in Emerging Cloud Computing Architectures: Recent Development, Challenges and Next Research Trend
| dc.contributor.author | Jauro, Fatsuma | |
| dc.contributor.author | Chiroma, Haruna | |
| dc.contributor.author | Gita, Abdulsalam Y. | |
| dc.date.accessioned | 2023-09-04T12:29:51Z | |
| dc.date.available | 2023-09-04T12:29:51Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | 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 in the emerging cloud computing literatures. However, no comprehensive literature review exists on the applications of deep learning approaches to solve complex problems in emerging cloud computing architectures. To fill this gap, we conducted a comprehensive literature survey on deep learning in emerging cloud computing architectures. The survey shows that deep learning algorithms in emerging cloud computing architectures are increasingly becoming an interesting research area for solving complex problems. We introduce a new taxonomy of deep learning techniques 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 | en_US |
| dc.description.sponsorship | ACE: Technology Enhanced Learning | en_US |
| dc.identifier.issn | 1568-4946 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/2116 | |
| dc.language.iso | en | en_US |
| dc.publisher | Applied Soft Computing, Elsevier | en_US |
| dc.subject | Convolutional Neural Network | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | Deep Reinforcement Learning | en_US |
| dc.subject | Edge Computing | en_US |
| dc.subject | Fog Computing | en_US |
| dc.subject | Emerging Cloud Computing | en_US |
| dc.subject | Serverless Computing | en_US |
| dc.subject | Mubarak Almutairi | en_US |
| dc.subject | ACE: Technology Enhanced Learning | en_US |
| dc.subject | ACETEL | en_US |
| dc.subject | National open university of Nigeria (NOUN) | en_US |
| dc.subject | Nigeria | en_US |
| dc.subject | Digital Development | en_US |
| dc.title | Deep Learning Architectures in Emerging Cloud Computing Architectures: Recent Development, Challenges and Next Research Trend | en_US |
| dc.type | Article | en_US |