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Information on didactic, research and institutional assignments on this page are certified by the University; more information, prepared by the professor, are available on the personal web page and in the curriculum vitae indicated on this webpage.
Information on professor
ProfessorArdagna Danilo
QualificationAssociate professor full time
Belonging DepartmentDipartimento di Elettronica, Informazione e Bioingegneria
Scientific-Disciplinary SectorING-INF/05 - Information Processing Systems
Curriculum VitaeDownload CV (798.74Kb - 31/07/2022)
OrcIDhttps://orcid.org/0000-0003-4224-927X

Contacts
Professor's office hours
DepartmentFloorOfficeDayTimetableTelephoneFaxNotes
Elettronica e Informazione - Via Golgi 423--ThursdayFrom 16:30
To 18:30
35143574It is possible to schedule a meeting by email in a different day of the week/time of the day.
E-maildanilo.ardagna@polimi.it
Professor's personal websitehttps://ardagna.faculty.polimi.it

Data source: RE.PUBLIC@POLIMI - Research Publications at Politecnico di Milano

List of publications and reserach products for the year 2023
No product yet registered in the year 2023


List of publications and reserach products for the year 2022 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Contributions on scientific books
Bayesian optimization with machine learning for big data applications in the cloud (Show >>)
Conference proceedings
MALIBOO: When Machine Learning meets Bayesian Optimization (Show >>)
POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique (Show >>)
Scheduling Deep Learning Jobs Training in the Cloud: Comparing Multiple Approaches (Show >>)
Journal Articles
A Stackelberg Game approach for Managing AI Sensing Tasks in Mobile Crowdsensing (Show >>)
Performance Prediction of Deep Learning Applications Training in GPU as a Service Systems (Show >>)


List of publications and reserach products for the year 2021 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Conference proceedings
A Random Greedy based Design Time Tool for AI Applications Component Placement and Resource Selection in Computing Continua (Show >>)
A Randomized Greedy Method for AI Applications Component Placement and Resource Selection in Computing Continua (Show >>)
ANDREAS: Artificial intelligence traiNing scheDuler foR accElerAted resource clusterS (Show >>)
Advancing Design and Runtime Management of AI Applications with AI-SPRINT (Show >>)
Pareto-Optimal Progressive Neural Architecture Search (Show >>)
Journal Articles
A Hierarchical Receding Horizon Algorithm for QoS-driven control of Multi-IaaS Applications (Show >>)
An incentive mechanism based on a Stackelberg game for mobile crowdsensing systems with budget constraint (Show >>)
Optimizing Quality-Aware Big Data Applications in the Cloud (Show >>)
Predicting the Performance of Big Data Applications on the Cloud (Show >>)


List of publications and reserach products for the year 2020 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Conference proceedings
Hierarchical Scheduling in on-demand GPU-as-a-Service Systems (Show >>)
Performance Prediction for Data-driven Workflows on Apache Spark (Show >>)
Journal Articles
Architectural Design of Cloud Applications: a Performance-aware Cost Minimization Approach. IEEE Transactions on Cloud Computing (Show >>)
Optimal Resource Allocation of Cloud-Based Spark Applications (Show >>)


List of publications and reserach products for the year 2019 (Show all details | Hide all details)
Type Title of the Publicaiton/Product
Conference proceedings
Gray-Box Models for Performance Assessment of Spark Applications (Show >>)
Machine Learning for Performance Prediction of Spark Cloud Applications (Show >>)
Optimizing on-demand GPUs in the Cloud for Deep Learning Applications Training (Show >>)
Performance Prediction of GPU-based Deep Learning Application (Show >>)
Journal Articles
Analytical composite performance models for Big Data applications (Show >>)
BIGSEA: A Big Data analytics platform for public transportation information (Show >>)
Hierarchical Stochastic Models for Performance, Availability, and Power Consumption Analysis of IaaS Clouds (Show >>)
manifesti v. 3.5.7 / 3.5.7
Area Servizi ICT
08/06/2023