Clayman, S;
Neto, A;
Verdi, F;
Correa, S;
Sampaio, S;
Sakelariou, I;
Mamatas, L;
... Serrat, J; + view all
(2021)
The NECOS Approach to End-to-End Cloud-Network Slicing as a Service.
IEEE Communications Magazine
, 59
(3)
pp. 91-97.
10.1109/MCOM.001.2000702.
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Abstract
Cloud-network slicing is a promising approach to serve vertical industries delivering their services over multiple administrative and technological domains. However, there are numerous open challenges to provide end-to-end slices due to complex business and engineering requirements from service and resource providers. This article presents a reference architecture for the cloud-network slicing concept and the practical realization of the slice-as-a-service paradigm, which are key results from the Novel Enablers in Cloud Slicing (NECOS) project. The NECOS platform has been designed to consider modularity, separation of concerns, and multi-domain dynamic operation as prime attributes. The architecture comprises a set of interworking components to automatically create, manage, and decommission end-to-end cloud-network slice instances in a lightweight manner. NECOS orchestrates slices at runtime, spanning across core/edge data centers and wired/wireless network infrastructures. The novelties of the multi-domain NECOS platform are validated through three proof-of-concept experiments: (i) a touristic content delivery service slice deployment featuring on-demand virtual infrastructure management across three countries on different continents to meet particular slice requirements; (ii) intelligent slice elasticity driven by machine learning techniques; and (iii) market-place-based resource discovery capabilities.
| Type: | Article |
|---|---|
| Title: | The NECOS Approach to End-to-End Cloud-Network Slicing as a Service |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/MCOM.001.2000702 |
| Publisher version: | https://doi.org/10.1109/MCOM.001.2000702 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Network slicing, Cloud computing, Data centers, Runtime, Machine learning, Elasticity |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10135302 |
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