DRL-based Service Migration for MEC Cloud-Native 5G and beyond Networks

被引:3
作者
Tsourdinis, Theodoros [1 ,2 ]
Makris, Nikos [1 ,3 ]
Fdida, Serge [2 ]
Korakis, Thanasis [1 ,3 ]
机构
[1] Univ Thessaly, Dept Elect & Comp Engn, Volos, Greece
[2] Sorbonne Univ, CNRS, LIP6, Paris, France
[3] CERTH, Ctr Res & Technol Hellas, Thessaloniki, Greece
来源
2023 IEEE 9TH INTERNATIONAL CONFERENCE ON NETWORK SOFTWARIZATION, NETSOFT | 2023年
基金
欧盟地平线“2020”;
关键词
Multi-access Edge Computing; Beyond; 5G; Cloud-Native network; AI/ML; OpenAirInterface; Kubernetes; VIRTUAL MACHINE;
D O I
10.1109/NetSoft57336.2023.10175417
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Multi-access Edge Computing (MEC) has been considered one of the most prominent enablers for low-latency access to services provided over the telecommunications network. Nevertheless, client mobility, as well as external factors which impact the communication channel can severely deteriorate the eventual user-perceived latency times. Such processes can be averted by migrating the provided services to other edges, while the end-user changes their base station association as they move within the serviced region. In this work, we start from an entirely virtualized cloud-native 5G network based on the OpenAirInterface platform and develop our architecture for providing seamless live migration of edge services. On top of this infrastructure, we employ a Deep Reinforcement Learning (DRL) approach that is able to proactively relocate services to new edges, subject to the user's multi-cell latency measurements and the workload status of the servers. We evaluate our scheme in a testbed setup by emulating mobility using realistic mobility patterns and workloads from real-world clusters. Our results denote that our scheme is capable sustain low-latency values for the end users, based on their mobility within the serviced region.
引用
收藏
页码:62 / 70
页数:9
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