Danilo Ardagna

5.1k total citations · 1 hit paper
140 papers, 3.1k citations indexed

About

Danilo Ardagna is a scholar working on Computer Networks and Communications, Information Systems and Artificial Intelligence. According to data from OpenAlex, Danilo Ardagna has authored 140 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 107 papers in Computer Networks and Communications, 104 papers in Information Systems and 30 papers in Artificial Intelligence. Recurrent topics in Danilo Ardagna's work include Cloud Computing and Resource Management (83 papers), Software System Performance and Reliability (46 papers) and IoT and Edge/Fog Computing (38 papers). Danilo Ardagna is often cited by papers focused on Cloud Computing and Resource Management (83 papers), Software System Performance and Reliability (46 papers) and IoT and Edge/Fog Computing (38 papers). Danilo Ardagna collaborates with scholars based in Italy, United States and Iran. Danilo Ardagna's co-authors include Barbara Pernici, Barbara Panicucci, Li Zhang, Mauro Passacantando, Marco Trubian, Michele Ciavotta, Chiara Francalanci, Giuliano Casale, Juan F. Pérez and Weikun Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, European Journal of Operational Research and IEEE Access.

In The Last Decade

Danilo Ardagna

134 papers receiving 2.9k citations

Hit Papers

Adaptive Service Composit... 2007 2026 2013 2019 2007 200 400 600

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Danilo Ardagna 2.5k 2.1k 734 412 236 140 3.1k
Giuliano Casale 1.6k 0.6× 2.3k 1.1× 748 1.0× 460 1.1× 152 0.6× 180 3.0k
Cees de Laat 1.6k 0.6× 1.8k 0.8× 484 0.7× 316 0.8× 215 0.9× 196 2.9k
Sanjiva Weerawarana 1.6k 0.7× 1.3k 0.6× 847 1.2× 545 1.3× 132 0.6× 74 2.4k
Ian Gorton 1.5k 0.6× 1.1k 0.5× 1.1k 1.4× 252 0.6× 125 0.5× 184 2.5k
Zakaria Maamar 1.7k 0.7× 1.3k 0.6× 950 1.3× 524 1.3× 121 0.5× 253 2.6k
Hairong Kuang 2.2k 0.9× 2.7k 1.3× 694 0.9× 181 0.4× 137 0.6× 10 3.7k
Konstantin V. Shvachko 1.9k 0.8× 2.3k 1.1× 621 0.8× 173 0.4× 130 0.6× 7 3.2k
Luis Rodero‐Merino 2.2k 0.9× 2.2k 1.0× 490 0.7× 189 0.5× 92 0.4× 13 3.1k
Marin Litoiu 1.6k 0.7× 1.7k 0.8× 787 1.1× 173 0.4× 77 0.3× 162 2.3k
Gianpaolo Cugola 915 0.4× 1.9k 0.9× 691 0.9× 304 0.7× 112 0.5× 71 2.6k

Countries citing papers authored by Danilo Ardagna

Since Specialization
Citations

This map shows the geographic impact of Danilo Ardagna's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Danilo Ardagna with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danilo Ardagna more than expected).

Fields of papers citing papers by Danilo Ardagna

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Danilo Ardagna. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Danilo Ardagna. The network helps show where Danilo Ardagna may publish in the future.

Co-authorship network of co-authors of Danilo Ardagna

This figure shows the co-authorship network connecting the top 25 collaborators of Danilo Ardagna. A scholar is included among the top collaborators of Danilo Ardagna based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Danilo Ardagna. Danilo Ardagna is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Passacantando, Mauro, et al.. (2025). A Robust Game Approach for On Spot Price Cloud Markets in Microservice-Based Applications. IEEE Access. 13. 42178–42195.
2.
Verticale, Giacomo, et al.. (2025). Tabular Reinforcement Learning Methods for Artificial Intelligence Tasks Offloading in Smart Eye-Wears. ACM Transactions on Autonomous and Adaptive Systems. 21(1). 1–38.
3.
Ardagna, Danilo, et al.. (2024). OSCAR-P and aMLLibrary: Profiling and predicting the performance of FaaS-based applications in computing continua. Journal of Systems and Software. 221. 112282–112282. 1 indexed citations
4.
Ardagna, Danilo, et al.. (2024). Integrating Bayesian Optimization and Machine Learning for the Optimal Configuration of Cloud Systems. IEEE Transactions on Cloud Computing. 12(1). 277–294. 4 indexed citations
5.
Passacantando, Mauro, et al.. (2024). AI Applications Resource Allocation in Computing Continuum: A Stackelberg Game Approach. IEEE Transactions on Cloud Computing. 13(1). 166–183. 1 indexed citations
6.
Ardagna, Danilo, et al.. (2023). POPNASv3: A pareto-optimal neural architecture search solution for image and time series classification. Applied Soft Computing. 145. 110555–110555. 5 indexed citations
7.
Ardagna, Danilo, et al.. (2023). OSCAR-P and aMLLibrary. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 139–146. 4 indexed citations
8.
Aghdasi, Hadi S., et al.. (2022). A Stackelberg Game Approach for Managing AI Sensing Tasks in Mobile Crowdsensing. IEEE Access. 10. 91524–91544. 6 indexed citations
9.
Lattuada, Marco, et al.. (2022). A Path Relinking Method for the Joint Online Scheduling and Capacity Allocation of DL Training Workloads in GPU as a Service Systems. IEEE Transactions on Services Computing. 16(3). 1630–1646. 3 indexed citations
10.
Ardagna, Danilo, et al.. (2021). ANDREAS: Artificial intelligence traiNing scheDuler for accElerAted resource clusterS. BOA (University of Milano-Bicocca). 7 indexed citations
11.
Entezari‐Maleki, Reza, et al.. (2021). Fixed-Point Iteration Approach to Spark Scalable Performance Modeling and Evaluation. IEEE Transactions on Cloud Computing. 11(1). 897–910. 3 indexed citations
12.
Ciavotta, Michele, et al.. (2020). Architectural Design of Cloud Applications: A Performance-Aware Cost Minimization Approach. IEEE Transactions on Cloud Computing. 10(3). 1571–1591. 9 indexed citations
13.
Lattuada, Marco, et al.. (2020). Optimal Resource Allocation of Cloud-Based Spark Applications. IEEE Transactions on Cloud Computing. 10(2). 1301–1316. 11 indexed citations
14.
Ardagna, Danilo, et al.. (2018). Context-aware data quality assessment for big data. Future Generation Computer Systems. 89. 548–562. 70 indexed citations
15.
Ardagna, Danilo, Michele Ciavotta, Riccardo Lancellotti, & Michele Guerriero. (2018). A Hierarchical Receding Horizon Algorithm for QoS-Driven Control of Multi-IaaS Applications. IEEE Transactions on Cloud Computing. 9(2). 418–434. 13 indexed citations
16.
Ciavotta, Michele, et al.. (2018). Optimizing Quality-Aware Big Data Applications in the Cloud. IEEE Transactions on Cloud Computing. 9(2). 737–752. 10 indexed citations
17.
Entezari‐Maleki, Reza, et al.. (2017). Hierarchical Stochastic Models for Performance, Availability, and Power Consumption Analysis of IaaS Clouds. IEEE Transactions on Cloud Computing. 7(4). 1039–1056. 41 indexed citations
18.
Ciavotta, Michele, et al.. (2016). A mixed integer linear programming optimization approach for multi-cloud capacity allocation. Journal of Systems and Software. 123. 64–78. 15 indexed citations
19.
Ardagna, Danilo, et al.. (2013). Model based control for multi-cloud applications. INFM-OAR (INFN Catania). 15 indexed citations
20.
Ardagna, Danilo & Barbara Pernici. (2005). Global and Local QoS Guarantee in Web Service Selection. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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