Antonio Calagna

Education

M.Sc. Degree in Communications and Computer Network Engineering cum laude in July 2021, both from Politecnico di Torino . From October 2021
B.Sc. Degree in Electronic Engineering in July 2019 and M.Sc. Degree in Communications and Computer Network Engineering cum laude in July 2021, both from Politecnico di Torino . From October 2021

Antonio Calagna was born in Palermo (Italy) on October 27th, 1998. He got his B.Sc. Degree in Electronic Engineering in July 2019 and M.Sc. Degree in Communications and Computer Network Engineering cum laude in July 2021, both from Politecnico di Torino . From October 2021 he’s a PhD student in Electrical, Electronics and Communications Engineering under the supervision of Professor Carla Fabiana Chiasserini and Professor Paolo Giaccone. He is a visiting Ph.D. student at the Institute for Intelligent Networked Systems at Northeastern University under the supervision of Professor Tommaso Melodia and co-supervision of Professor Salvatore D’Oro. His gained contributions address the pivotal issue of time- and mission-critical services placement at the edge of the network and the urgency of ensuring a satisfying quality of experience to mobile end users. He thus investigate microservice migration as a key solution to ensure proximity of such microservices to mobile end users while preserving service continuity.

Publications

2026

Journals & Magazines

A. Calagna, S. Maxenti, L. Bonati, S. D'Oro, T. Melodia, and C. Chiasserini. “CORMO-RAN: Energy Efficiency at the Near-RT RIC via Lossless Migration of O-RAN xApps.” IEEE Transactions on Mobile Computing (2026)Journal

DOI Link

Open Radio Access Network (RAN) is a key paradigm to attain unprecedented flexibility of the RAN via disaggregation and Artificial Intelligence (AI)-based applications called xApps. In dense areas with many active RAN nodes, compute resources are engineered to support potentially hundreds of xApps monitoring and controlling the RAN to achieve operator’s intents. However, such resources might become underutilized during low-traffic periods, where most cells are sleeping and, given the reduced RAN complexity, only a few xApps are needed for its control. In this paper, we propose CORMO-RAN, a data-driven orchestrator that dynamically activates compute nodes based on xApp load to save energy, and performs lossless migration of xApps from nodes to be turned off to active ones while ensuring xApp availability during migration. CORMORAN tackles the trade-off among service availability, scalability, and energy consumption while (i) preserving xApps’ internal state to prevent RAN performance degradation during migration; (ii) accounting for xApp diversity in state size and timing constraints; and (iii) implementing several migration strategies and providing guidelines on best strategies to use based on resource availability and requirements. We prototype CORMORAN as an rApp, and experimentally evaluate it on an ORAN private 5G testbed hosted on a Red Hat OpenShift cluster with commercial radio units. Results demonstrate that CORMORAN is effective in minimizing energy consumption of the RAN Intelligent Controller (RIC) cluster, yielding up to 64% energy saving when compared to existing approaches.