On Reconfiguring 5G Network Slices

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http://hdl.handle.net/10138/321106

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Pozza , M , Nicholson , P , Lugones , D , Rao , A , Flinck , H & Tarkoma , S 2020 , ' On Reconfiguring 5G Network Slices ' , IEEE Journal on Selected Areas in Communications , vol. 38 , no. 7 , pp. 1542-1554 . https://doi.org/10.1109/JSAC.2020.2986898

Title: On Reconfiguring 5G Network Slices
Author: Pozza, Matteo; Nicholson, Patrick; Lugones, Diego; Rao, Ashwin; Flinck, Hannu; Tarkoma, Sasu
Contributor: University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
Date: 2020-07
Language: eng
Number of pages: 13
Belongs to series: IEEE Journal on Selected Areas in Communications
ISSN: 0733-8716
URI: http://hdl.handle.net/10138/321106
Abstract: 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.
Subject: 113 Computer and information sciences
5G mobile communication
Substrates
Data centers
Bandwidth
Network slicing
Virtual machining
network slicing
Network Function Virtualization (NFV)
Virtual Machine (VM) migration
network reconfiguration
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