Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges
Not on the map. Why?
Abstract (arXiv)
Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesized to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Oct 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 4; 2023: 1; 2024: 1.
8 papers.
- 2022SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environmentscited by 11
- 2021PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computingcited by 8
- 2022Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature reviewcited by 6
- 2021MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systemscited by 4
- 2022DRAGON: Decentralized Fault Tolerance in Edge Federationscited by 2
- 2023Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiencycited by 1
- 2022MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environmentscited by 0
- 2024AI-Driven Innovations in Modern Cloud Computingcited by 0
- 2024AI-Driven Innovations in Modern Cloud Computing21 Oct 2024
- 2023Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency26 Apr 2023
- 2022DRAGON: Decentralized Fault Tolerance in Edge Federations16 Aug 2022
- 2022MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments21 May 2022
- 2022SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments21 May 2022
- 2022Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review29 Apr 2022
- 2021MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems14 Dec 2021
- 2021PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computing4 Dec 2021