Danilo Ardagna

Associate Professor

Politecnico di Milano

Italy

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Danilo Ardagna is an Associate Professor at Politecnico di Milano, Italy. His research areas include efficient parameter tuning for virtual screening applications, AI resource allocation using game-theoretical approaches, and scheduling of AI training jobs in GPU-based systems. He has published recent articles on cloud application architecture and performance prediction for deep learning workloads.

Articles (16)

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2025

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2021

Collaborators (4)

Giacomo Verticale

Associate Professor

Politecnico di Milano

ITALY

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GIANLUCA PALERMO

Associate Professor

Politecnico di Milano

ITALY

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Elisabetta Di Nitto

Full Professor

Politecnico di Milano

ITALY

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Riccardo Lancellotti

Associate Professor

University of Modena and Reggio Emilia

ITALY

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Danilo Ardagna

Research Interests

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About

Articles (16)

Efficient parameter tuning for a structure-based virtual screening HPC application

AI Applications Resource Allocation in Computing Continuum: A Stackelberg Game Approach

A Robust Game Approach for On Spot Price Cloud Markets in Microservice-Based Applications

Integrating Bayesian Optimization and Machine Learning for the Optimal Configuration of Cloud Systems

SPACE4AI-D: A Design-Time Tool for AI Applications Resource Selection in Computing Continua

A Stochastic Approach for Scheduling AI Training Jobs in GPU-Based Systems

Architectural Design of Cloud Applications: A Performance-Aware Cost Minimization Approach

A Stackelberg Game Approach for Managing AI Sensing Tasks in Mobile Crowdsensing

Performance prediction of deep learning applications training in GPU as a service systems

A Path Relinking Method for the Joint Online Scheduling and Capacity Allocation of DL Training Workloads in GPU as a Service Systems

Optimal Resource Allocation of Cloud-Based Spark Applications

A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications

An incentive mechanism based on a Stackelberg game for mobile crowdsensing systems with budget constraint

Optimizing Quality-Aware Big Data Applications in the Cloud

Fixed-point Iteration Approach to Spark Scalable Performance Modeling and Evaluation

Predicting the performance of big data applications on the cloud

Collaborators (4)

Giacomo Verticale

GIANLUCA PALERMO

Elisabetta Di Nitto

Riccardo Lancellotti

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