Surfing the SWAVES: Lifecycle-aware Service Placement in MEC

TL;DR

This paper introduces SWAVES, a lifecycle-aware service placement method in MEC networks that proactively deploys VNFs based on user movement predictions, significantly reducing unsuccessful packets.

Contribution

SWAVES is a novel proactive service placement approach that accounts for user mobility and resource constraints to improve MEC performance.

Findings

SWAVES reduces unsuccessful packet ratio by orders of magnitude.

Proactive VNF deployment improves latency and availability.

The approach effectively balances resource usage and user mobility predictions.

Abstract

In Multi-access Edge Computing (MEC) networks, users covered by a mobile network can exploit edge clouds (ECs), computational resources located at the network's edge, to execute virtual network functions (VNFs). ECs are particularly useful when deploying VNFs with strict delay and availability requirements. As users roam in the network and get handed over between cells, deployed VNFs must follow users to retain the benefits of edge computing. Yet, having VNFs ready at the closest EC can be challenging: (i) ECs are not usually powerful enough to store and run any combination of VNFs simultaneously; (ii) if a VNF is not available at the needed EC, a series of time-consuming operations has to be performed before the VNF becomes operational. These limitations can be addressed by proactively starting VNFs instances at (likely) future locations, balancing better latency properties against higher resource usage. Such proactive deployment does need forecasting of user movements, but these will be imperfect, creating yet another tradeoff. We present our approach to this service provisioning problem, SWAVES. When compared on the ratio of users' unsuccessful packets, SWAVES improves such metric by orders of magnitude with respect to other proposed heuristic.