The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Next generation cloud computing: New trends and research directions
Open accessAbstract
The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems. Distributed cloud infrastructure will make use of the network edge in the future.Two tier applications will be replaced by new multi-tier cloud architectures.Next generation cloud computing impacts both societal and scientific avenues.A new marketplace will need to be developed for resources at the network edge.Security and sustainability are key to architecting future cloud systems.
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Cites
The case for vm-based cloudlets in mobile computing
Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009) · IEEE Pervasive Computing · doi:10.1109/mprv.2009.82 · Cited by 3,660
Big Data computing and clouds: trends and future directions
Assunção, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A. S., & Buyya, R. (2015) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2014.08.003 · Cited by 901
Self-citationLarge-scale deep unsupervised learning using graphics processors
Raina, R., Madhavan, A., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553486 · Cited by 742 · cited 1×
“The availability of hardware accelerators, such as GPUs, in cloud environments has reduced the computing time for machine learning algorithms on large volumes of data [81, 82, 83].”
BackgroundComputing at the mobile edge: designing elastic android applications for computation offloading
Orsini, G., Bade, D., & Lamersdorf, W. (2015) · 2015 8th IFIP Wireless and Mobile Networking Conference (WMNC) · doi:10.1109/wmnc.2015.10 · Cited by 112
The journey of qos-aware autonomic cloud computing
Singh, S., Chana, I., & Singh, M. (2017) · IT Professional · doi:10.1109/mitp.2017.26 · Cited by 43
Cited by
Resource Management in Fog/Edge Computing: A Survey on Architectures, Infrastructure, and Algorithms
Hong, C.-H., & Varghese, B. (2020) · ACM Computing Surveys · doi:10.1145/3326066 · Cited by 461
Self-citationKim, H., Choi, H., Kang, H., An, J., Yeom, S., & Hong, T. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.110755 · Cited by 367
Houssein, E. H., Gad, A. G., Wazery, Y. M., & Suganthan, P. N. (2021) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2021.100841 · Cited by 355
The Digitisation of Agriculture: a Survey of Research Activities on Smart Farming
Bacco, M., Barsocchi, P., Ferro, E., Gotta, A., & Ruggeri, M. (2019) · Array · doi:10.1016/j.array.2019.100009 · Cited by 346
A Survey on Edge and Edge-Cloud Computing Assisted Cyber-Physical Systems
Cao, K., Hu, S., Shi, Y., Colombo, A., Karnouskos, S., & Li, X. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3073066 · Cited by 303
Metadata
- Authors
- Varghese, B., & Buyya, R.
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2018-02
- Volume
- 79
- Pages
- 849-861
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W2737889100
- Cited by
- 857
- References
- 5
- FWCI
- 160.46
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
