Dynamic Scheduling and Optimal Reconfiguration of UPF Placement in 5G Networks

Leyva-Pupo, I., Cervelló-Pastor, C., Anagnostopoulos, C. and Pezaros, D. P. (2020) Dynamic Scheduling and Optimal Reconfiguration of UPF Placement in 5G Networks. In: 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM ’20), Alicante, Spain, 16-20 Nov 2020, pp. 103-111. ISBN 9781450381178 (doi: 10.1145/3416010.3423221)

Abstract

Multi-access Edge Computing (MEC) is a key technology in the road to 5G and beyond networks. Significant reductions in both latency and backhaul traffic can be achieved by placing server applications, and network functions at the network edge. However, this implies new challenges for their dynamic placement and management. In this paper, we tackle the problem of dynamic placement reconfiguration of 5G User Plane Functions (UPFs) in a MEC ecosystem to adapt to changes in user locations while ensuring QoS and network operator expenditures reduction. In this vein, an Integer Linear Programming (ILP) solution is proposed to determine the optimal UPF placement configuration (e.g., number of UPFs and user-UPF mapping) by considering several cost components along with service requirements. Moreover, a scheduling technique based on Optimal Stopping Theory (OST) is presented to decide the optimal reconfiguration time according to instantaneous values of latency violations and established QoS thresholds. Extensive simulation results demonstrate their effectiveness, achieving significant improvements in metrics such as number of re-computation events, reconfiguration costs, and number of latency violations over time.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Anagnostopoulos, Dr Christos and Pezaros, Professor Dimitrios
• Authors: Leyva-Pupo, I., Cervelló-Pastor, C., Anagnostopoulos, C., and Pezaros, D. P.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450381178
• Copyright Holders: Copyright © 2020 Association for Computing Machinery
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

Related URLs

• Organisation