Cloud-Network Slicing MANO Towards an Efficient IoT-Cloud Continuum

This work sets out by exploiting the NECOS Cloud-Network Slicing concept to form the Cloud-to-Things continuum. We adopt the Network-Slicing Management and Orchestration (NS-MANO) approach, a set of building blocks that integrates the NECOS platform to fill the gap caused by a lack of multi-domain N...

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Bibliographic Details
Published in:Journal of grid computing 2021-12, Vol.19 (4), Article 48
Main Authors: Maciel, Douglas B., Neto, Emidio P., Costa, Kevin B., Lima, Mathews P., Lopes, Vitor G., Neto, Augusto V., Silva, Felipe S. Dantas, Sampaio, Silvio C.
Format: Article
Language:English
Subjects:
Architecture
Computer Science
Cost analysis
Distributed processing
Domains
Harnesses
Management of Computing and Information Systems
Network slicing
Processor Architectures
Prototypes
Software
User Interfaces and Human Computer Interaction
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Summary:This work sets out by exploiting the NECOS Cloud-Network Slicing concept to form the Cloud-to-Things continuum. We adopt the Network-Slicing Management and Orchestration (NS-MANO) approach, a set of building blocks that integrates the NECOS platform to fill the gap caused by a lack of multi-domain Network-Slicing capability support. The NS-MANO harnesses Softwarization and Cloudification facilities to automatically provision elastic Network-Slice parts that span across the backhauling, fronthauling, and Radio Access Network (RAN) infrastructures of federated multi-domains. Additionally, NS-MANO binds all Network-Slice parts together into NECOS previously-orchestrated Cloud-Network Slice parts that form a full end-to-end Cloud-Network Slice instance. We designed a prototype atop a real-world testbed to check the NECOS/NS-MANO holistic architecture conformance, functional effectiveness, and performance impact. The findings suggest that the prototype offers an effective means of laying the foundations for an end-to-end Cloud-Network Slice lifecycle while keeping mobile users perceiving affordable quality over time. Additionally, we estimate the prototype cost through quantitative analysis on response times and signaling overhead along the experiment time.
ISSN:1570-7873
1572-9184
DOI:10.1007/s10723-021-09588-6