Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2307.08115

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

Chiara Caiazza·Claudio Cicconetti·Valerio Luconi·Alessio Vecchio
View original source ↗Read source document ↗Export metadata ↓

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.

Explore related subjects

cs.NI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chiara Caiazza, Claudio Cicconetti, Valerio Luconi, Alessio Vecchio. 2023-07-16. Measurement-Driven Design and Runtime Optimization in Edge Computing: Methodology and Tools. https://doi.org/10.1016/j.comnet.2021.108140

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach

The placement of base station (BS) is a fundamental determinant of coverage and capacity of urban wireless networks. Yet large-scale BS deployment optimization remains challenging due to its dependency on site-specific radio propagation and user spatial distributions, both of which are unfortunately difficult to obtain prior to deployment. To overcome this barrier, we propose an intelligent BS deployment framework that integrates a geographic data-informed wireless network digital twin (DT) with deep reinforcement learning (DRL), enabling sample-free macro BS deployment optimization from solely open geographic data, without on-site measurements, real user trajectories, or exhaustive ray tracing. The proposed DT incorporates a sample-free radio map prediction model with hybrid input representation to achieve kilometer-scale signal strength estimation in milliseconds, complemented by a diffusion-based generative model for trajectory synthesis to collectively characterize channel and user distributions. Leveraging the DT as a virtual training environment, we formulate BS deployment as a multi-step Markov decision process (MDP) and solve it via a spatially structured DRL algorithm. A local search process and a Wasserstein distance-based deployment buffer are further incorporated to efficiently explore the large combinatorial solution space. Experimental results in real-world urban scenarios demonstrate that the geographic data-informed DT attains accuracy comparable to 100-sample-based prediction, and the intelligent BS deployment framework achieves up to 98.9% of the idealized benchmark performance while reducing optimization overhead by over 99%.

cs.NI↗
arXiv2026

The Price of the Golden 6G Band: Evaluation of Beam Management Effort in FR3

Frequency Range 3 (FR3), 7.125-24.25 GHz, regarded as the "golden band" for 6G networks, has less challenging propagation characteristics than FR2 while offering much wider bandwidth for high data rate applications than FR1. Reusing existing FR1 infrastructure for FR3 network deployments requires gNodeBs (gNBs) to employ antenna arrays and perform beam management, which has proven challenging at FR2. In this paper, we extensively study and characterize the beam management effort in an FR3 urban network, in terms of: beam alignment sensitivity, number of directional link opportunities, gNB handover and beam switch rates, and beam steering distance. Our results show that achieving a high and stable mobile throughput requires significant beam management effort across FR3 bands. While the beam tracking requirements are less stringent at the lower frequencies due to wider beams, the beam switching rate to a non-adjacent beam is relatively comparable at FR3 and FR2.

cs.NI↗
arXiv2026

A Mechanical Antenna for Improving Capacity Fairness in Dynamic Multi-Station Scenarios

While indoor Internet of Things (IoT) and sensor networks increasingly rely on Wi-Fi access points (APs) to collect high-bandwidth data streams from multiple devices, conventional APs rely on static antenna deployments, whose fixed orientations are often suboptimal in dynamic propagation environments. To overcome this limitation, this paper proposes a mechanical Wi-Fi antenna control system that adaptively optimizes its 3D antenna orientation for dynamic multi-station scenarios. The proposed system autonomously actuates its physical antennas in response to perceived radio environments by combining state-specific black-box optimizers and capacity-based environment change detection. The evaluation results show that the proposed system improves channel capacity under dynamic station combinations, avoids unnecessary re-optimization under transient blockages, and triggers re-optimization after sustained environmental changes such as continuous blockage and device relocation.

cs.NI↗