On Reconfiguring 5G Network Slices

The virtual resources of 5G networks are expected to scale and support migration to other locations within the substrate. In this context, a configuration for 5G network slices details the instantaneous mapping of the virtual resources across all slices on the substrate, and a feasible configuration...

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Veröffentlicht in:IEEE journal on selected areas in communications 2020-07, Vol.38 (7), p.1542-1554
Hauptverfasser: Pozza, Matteo, Nicholson, Patrick K., Lugones, Diego F., Rao, Ashwin, Flinck, Hannu, Tarkoma, Sasu
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container_end_page 1554
container_issue 7
container_start_page 1542
container_title IEEE journal on selected areas in communications
container_volume 38
creator Pozza, Matteo
Nicholson, Patrick K.
Lugones, Diego F.
Rao, Ashwin
Flinck, Hannu
Tarkoma, Sasu
description The virtual resources of 5G networks are expected to scale and support migration to other locations within the substrate. In this context, a configuration for 5G network slices details the instantaneous mapping of the virtual resources across all slices on the substrate, and a feasible configuration satisfies the Service-Level Objectives (SLOs) without overloading the substrate. Reconfiguring a network from a given source configuration to the desired target configuration involves identifying an ordered sequence of feasible configurations from the source to the target. The proposed solutions for finding such a sequence are optimized for data centers and cannot be used as-is for reconfiguring 5G network slices. We present Matryoshka , our divide-and-conquer approach for finding a sequence of feasible configurations that can be used to reconfigure 5G network slices. Unlike previous approaches, Matryoshka also considers the bandwidth and latency constraints between the network functions of network slices. Evaluating Matryoshka required a dataset of pairs of source and target configurations. Because such a dataset is currently unavailable, we analyze proof of concept roll-outs, trends in standardization bodies, and research sources to compile an input dataset. On using Matryoshka on our dataset, we observe that it yields close-to-optimal reconfiguration sequences 10X faster than existing approaches.
doi_str_mv 10.1109/JSAC.2020.2986898
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subjects 5G mobile communication
Bandwidth
Configurations
Data centers
Datasets
Mapping
Network Function Virtualization (NFV)
Network latency
network reconfiguration
Network slicing
Reconfiguration
Standardization
Substrates
Virtual Machine (VM) migration
Virtual machining
Virtual networks
Wireless networks
title On Reconfiguring 5G Network Slices
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