Measurement-Driven Design and Runtime Optimization in Edge Computing: Methodology and Tools

TL;DR

This paper introduces MECPerf, a measurement architecture for monitoring network performance in edge computing, enabling offline analysis and real-time optimization, validated through a European testbed and demonstrated with a client migration use case.

Contribution

It presents MECPerf, a novel system for collecting network measurements in edge computing environments for analysis and optimization, with validation and open data resources.

Findings

MECPerf effectively captures network metrics in real-time and offline.

Validation in a European testbed confirms system reliability.

Open data and Python tools facilitate widespread adoption.

Abstract

Edge computing is projected to become the dominant form of cloud computing in the future because of the significant advantages it brings to both users (less latency, higher throughput) and telecom operators (less Internet traffic, more local management). However, to fully unlock its potential at scale, system designers and automated optimization systems alike will have to monitor closely the dynamics of both processing and communication facilities. Especially the latter is often neglected in current systems since network performance in cloud computing plays only a minor role. In this paper, we propose the architecture of MECPerf, which is a solution to collect network measurements in a live edge computing domain, to be collected for offline provisioning analysis and simulations, or to be provided in real-time for on-line system optimization. MECPerf has been validated in a realistic testbed funded by the European Commission (Fed4Fire+), and we describe here a summary of the results, which are fully available as open data and through a Python library to expedite their utilization. This is demonstrated via a use case involving the optimization of a system parameter for migrating clients in a federated edge computing system adopting the GSMA platform operator concept.