Surfing the SWAVES: Lifecycle-aware Service Placement in MEC

2025-06-13

AI Summary

To address service disruptions and latency violations in multi-access edge computing (MEC) caused by frequent virtual network function (VNF) migrations due to user mobility, constrained edge resources, and high VNF cold-start latency, this paper proposes a lifecycle-aware proactive VNF placement mechanism. We formulate the first joint model integrating VNF lifetime dynamics with user mobility trajectories, and design an adaptive placement strategy that jointly optimizes latency constraints, resource consumption, and prediction uncertainty. Building upon this, we develop SWAVES—a heuristic algorithm integrating mobility prediction, resource-aware optimization, and VNF startup cost modeling. Experimental results demonstrate that our approach reduces packet failure rates by several orders of magnitude compared to state-of-the-art heuristics, significantly enhancing service continuity and achieving robust millisecond-level latency guarantees.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Optimizing VNF placement in MEC for user mobility
• Balancing latency and resource usage in edge clouds
• Improving service availability with proactive VNF deployment

Innovation

• Proactive VNF placement at future locations
• Lifecycle-aware service migration in MEC
• Movement forecasting for optimized resource usage