RIPPLE: Lifecycle-aware Embedding of Service Function Chains in Multi-access Edge Computing

TL;DR

RIPPLE is a lifecycle-aware embedding method for service function chains in edge computing that proactively manages VNF deployment considering lifecycle and connectivity uncertainties to improve QoS.

Contribution

The paper introduces RIPPLE, a novel approach that jointly considers lifecycle dynamics and connectivity forecasts for efficient SFC embedding in MEC.

Findings

RIPPLE reduces service interruptions compared to traditional methods.

It effectively manages lifecycle and connectivity uncertainties.

Performs close to ideal solutions assuming instant lifecycle operations.

Abstract

In Multi-access Edge Computing networks, services can be deployed on nearby edge clouds (EC) as service function chains (SFCs) to meet strict quality of service (QoS) requirements. As users move, frequent SFC reconfigurations are required, but these are non-trivial: SFCs can serve users only when all required virtual network functions (VNFs) are available, and VNFs undergo time-consuming lifecycle operations before becoming operational. We show that ignoring lifecycle dynamics oversimplifies deployment, jeopardizes QoS, and must be avoided in practical SFC management. To address this, forecasts of user connectivity can be leveraged to proactively deploy VNFs and reconfigure SFCs. But forecasts are inherently imperfect, requiring lifecycle and connectivity uncertainty to be jointly considered. We present RIPPLE, a lifecycle-aware SFC embedding approach to deploy VNFs at the right time and location, reducing service interruptions. We show that RIPPLE closes the gap with solutions that unrealistically assume instantaneous lifecycle, even under realistic lifecycle constraints.