Novel NFV aware network service for intelligent network slicing based on squatting-kicking model
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
ProjectEVOLUCION HACIA REDES Y SERVICIOS AUTO-GESTIONADOS PARA EL 5G DEL FUTURO (AEI-PID2019-108713RB-C51)
Future networks starting from 5G will depend on network slicing to meet a wide range of network services (NSs) with various quality of service (QoS) requirements. With the powerful Network Function Virtualization (NFV) technology available, network slices can be rapidly deployed and centrally managed, giving rise to simplified management, high resource utilization, and cost-efficiency. This is achieved by realizing NSs on general-purpose hardware, hence, replacing traditional middleboxes. However, realizing fast deployment of end-to-end network slices still requires intelligent resource allocation algorithms to efficiently use the network resources and ensure QoS among different slice categories during congestion cases. This is especially important at the links of the network because of the scarcity of their resources. Consequently, this paper proposes a paradigm based on NFV architecture aimed at providing the massive computational capacity required in the NSs and supporting the resource allocation strategy proposed for multiple slice networks based on resources utilization optimization using a proposed and analyzed Squatting-Kicking model (SKM). SKM is a suitable algorithm for dynamically allocating network resources to different priority slices along paths and improving resource utilization under congested scenarios. Simulation results show that the proposed service deployment algorithm achieves 100% in terms of both overall resource utilization and admission for higher priority slices in some scenarios in bandwidth-constrained contexts, which cannot be achieved by other existing schemes due to priority constraints.
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CitationHamouda, A.; Hesselbach, X.; Piney, J. Novel NFV aware network service for intelligent network slicing based on squatting-kicking model. "IEEE access", 15 Desembre 2020, vol. 8, p. 223041-223068.
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