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Multi-Resource Allocation for Network Slicing

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Date
2020
Link to item file
https://hal.archives-ouvertes.fr/hal-02008115
Dewey
Informatique générale
Sujet
5G slicing; OWA; multi-resource allocation
Journal issue
IEEE/ACM Transactions on Networking
Volume
28
Number
3
Publication date
2020
Article pages
1311-1324
Publisher
IEEE - Institute of Electrical and Electronics Engineers
DOI
http://dx.doi.org/10.1109/TNET.2020.2979667
URI
https://basepub.dauphine.fr/handle/123456789/21212
Collections
  • LAMSADE : Publications
Metadata
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Author
Fossati, Francesca
Moretti, Stefano
Perny, Patrice
Secci, Stefano
Type
Article accepté pour publication ou publié
Abstract (EN)
Among the novelties introduced by 5G networks, the formalization of the 'network slice' as a resource allocation unit is an important one. In legacy networks, resources such as link bandwidth, spectrum, computing capacity are allocated independently of each other. In 5G environments, a network slice is meant to directly serve end-to-end services, or verticals: behind a network slice demand, a tenant expresses the need to access a precise service type, under a fully qualified set of computing and network requirements. The resource allocation decision encompasses, therefore, a combination of different resources. In this paper, we address the problem of fairly sharing multiple resources between slices, in the critical situation in which the network does not have enough resources to fully satisfy slice demands. We model the problem as a multi-resource allocation problem, proposing a versatile optimization framework based on the Ordered Weighted Average (OWA) operator. We show how, adapting the OWA utility function, our framework can generalize classical single-resource allocation methods, existing multi-resource allocation solutions at the state of the art, and implement novel multi-resource allocation solutions. We compare analytically and by extensive simulations the different methods in terms of fairness and system efficiency. We conclude the paper adapting the proposed framework to Service Level Agreement (SLA)-driven services. Two algorithms, considering minimum capacity requirements and time-fairness are proposed and tested.

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