Hierarchical Scheduling in on-demand GPU-as-a-Service Systems. - Federica Filippini; Marco Lattuada 001; Arezoo Jahani; Michele Ciavotta; Danilo Ardagna; Edoardo Amaldi (SYNASC, 2020)

A Random Greedy based Design Time Tool for AI Applications Component Placement and Resource Selection in Computing Continua. - Hamta Sedghani; Federica Filippini; Danilo Ardagna (EDGE, 2021)

ANDREAS: Artificial intelligence traiNing scheDuler foR accElerAted resource clusterS. - Federica Filippini; Danilo Ardagna; Marco Lattuada 001; Edoardo Amaldi; Maciek Riedl; Katarzyna Materka; Pawel Skrzypek; Michele Ciavotta; Fabrizio Magugliani; Marco Cicala (FiCloud, 2021)

Performance Models for Distributed Deep Learning Training Jobs on Ray. - Federica Filippini; Boris Lublinsky; Maximilien de Bayser; Danilo Ardagna (SEAA, 2023)

FIGARO: reinForcement learnInG mAnagement acRoss the computing cOntinuum. - Federica Filippini; Riccardo Cavadini; Danilo Ardagna; Riccardo Lancellotti; Gabriele Russo Russo; Valeria Cardellini; Francesco Lo Presti (UCC, 2023)

SPACE4AI-R: a Runtime Management Tool for AI Applications Component Placement and Resource Scaling in Computing Continua. - Federica Filippini; Hamta Sedghani; Danilo Ardagna (UCC, 2023)

Runtime Management of Artificial Intelligence Applications for Smart Eyewears. - Abednego Wamuhindo Kambale; Hamta Sedghani; Federica Filippini; Giacomo Verticale; Danilo Ardagna (UCC, 2023)

OSCAR-P and aMLLibrary: Performance Profiling and Prediction of Computing Continua Applications. - Enrico Galimberti; Bruno Guindani; Federica Filippini; Hamta Sedghani; Danilo Ardagna; Germán Moltó; Miguel Caballer (ICPE (Companion), 2023)

A Path Relinking Method for the Joint Online Scheduling and Capacity Allocation of DL Training Workloads in GPU as a Service Systems. - Federica Filippini; Marco Lattuada 001; Michele Ciavotta; Arezoo Jahani; Danilo Ardagna; Edoardo Amaldi (IEEE Trans. Serv. Comput., 2023)

Greening AI: A Framework for Energy-Aware Resource Allocation of ML Training Jobs with Performance Guarantees. - Roberto Sala; Federica Filippini; Danilo Ardagna; Daniele Lezzi; Francesc Lordan; Patrick Thiem (AINA (5), 2024)

A Stochastic Approach for Scheduling AI Training Jobs in GPU-Based Systems. - Federica Filippini; Jonatha Anselmi; Danilo Ardagna; Bruno Gaujal (IEEE Trans. Cloud Comput., 2024)

SPACE4AI-D: A Design-Time Tool for AI Applications Resource Selection in Computing Continua. - Hamta Sedghani; Federica Filippini; Danilo Ardagna (IEEE Trans. Serv. Comput., 2024)

ML-Based Performance Modeling in Edge FaaS Systems. - Federica Filippini; Luca Cavenaghi; Nicolas Calmi; Marco Savi; Michele Ciavotta (ESOCC, 2025)

Multi-Agent Reinforcement Learning for Workload Distribution in FaaS-Edge Computing Systems. - Emanuele Petriglia; Federica Filippini; Michele Ciavotta; Marco Savi (IPDPS (Workshops), 2025)

OSCAR-P and aMLLibrary: Profiling and predicting the performance of FaaS-based applications in computing continua. - Roberto Sala; Bruno Guindani; Enrico Galimberti; Federica Filippini; Hamta Sedghani; Danilo Ardagna; Sebastián Risco; Germán Moltó; Miguel Caballer (J. Syst. Softw., 2025)

Decentralized Edge Workload Forecasting With Gossip Learning. - Alessandro Tundo; Federica Filippini; Francesco Regonesi; Michele Ciavotta; Marco Savi (IEEE Trans. Netw. Serv. Manag., 2025)