Dr. Andrea Morichetta

University assistant (Postdoc)

a.morichetta @ dsg.tuwien.ac.at

+43 1 58801 184902

Google Scholar

last edited: 22.10.2024

Short CV

Andrea Morichetta joined the Distributed Systems Group of the Institute of Information Systems Engineering in January 2020 as a University assistant.

He received his Doctoral degree in Electrical, Electronics, and Communications Engineering in January 2020, in Politecnico di Torino in the Telecommunication Network Group. He worked under the supervision of Prof. Marco Mellia, with a grant fully funded by the Big-DAMA project. From 2017 to 2020, he collaborated with the SmartData@PoliTO center for data science and big data. In 2017 he visited, for a summer internship, Cisco in San Jose, CA. From January 2019 to July 2019, he was a visiting student at AIT, Vienna, Austria.

His research focuses on the intersection of intelligence and systems, combining data analysis and machine learning to distributed systems design. The work pays particular attention to unsupervised methodologies, emphasizing security and parallelization.

Research Interests

• ML for predictive/Proactive scaling in the cloud (time series analysis)
• Edge ML
• Serverless Computing
• Security for distributed systems
• Fairness, accountability and transparency in ML

Teaching

Courses

Thesis & Project Supervision

I am willing to co-supervising bachelor and master thesis for motivated students interested in topics that match my research focus. For any proposal/question/idea don’t hesitate to contact me.

Supervised Theses

• Florian Drucker, “Revisiting Tensorflow Experiments on FAASM”, Bachelor Thesis, TU Wien, 2024.
• Philipp Kogler, “Reliable Generation of Engineering Process Specification from Natural Language”, Bachelor Thesis, TU Wien, 2024.
• Robert Angerer, “A Comparative Study of Federated Versus Centralized Learning for Time Series Forecasting for Financial Data”, Bachelor Thesis, TU Wien, 2023.
• Konstantin Palikarsky, “Functions-as-a-Service (FaaS): A comparative study of OpenFaaS and OpenWhisk for Web API deployment”, Bachelor Thesis, TU Wien, 2023.
• Nikolaus Spring, “Intent-based Systems Management”, Bachelor Thesis, TU Wien, 2022.
• Johannes Knopp, “Irregular Time Series Forecasting With Deep Neural Networks: From Recurrent Independent Mechanisms to Global Workspace Theory”, Bachelor Thesis, TU Wien, 2022.
• Paul Pinter, “Opinion Spam Detection on Distributed Systems”, Bachelor Thesis, TU Wien, 2022.
• Veronika Bekbulatova, “Federated Semi-Supervised Anomaly Detection: Application in Intrusion Detection Systems for Industrial IoT”, Bachelor Thesis, TU Wien, 2022.

Co-Supervised Theses

In progress

• Michael Springsits, “A cloud native approach to stateful server-less computing”, Diploma Thesis, TU Wien, in progress.
• Jürgen Brandl, “Walk back the cat: Augmenting incident response with RCA and causal inference”, Diploma Thesis, TU Wien, in progress.
• Simon Müller-Guttenbrunn, “Semi-supervised federated learning based intrusion detection and classification in a Client-Edge-Cloud setting”, Diploma Thesis, TU Wien, in progress.
• Stephan Podlipnig, “Scheduling and Controlling Smart Micro Grids / Energy Communities as Kubernetes First-class-citizen”, Diploma Thesis, TU Wien, in progress.
• Konstantin Strümpf, “Supporting domain experts develop data exploration and modelling workflows: a ML-based approach”, Diploma Thesis, TU Wien, in progress.

Completed

• Nikolaus Spring, “Multi-Agent Systems in Vehicular Edge Computing: A Communication-Centric Approach to Task Handovers”, Diploma Thesis, TU Wien, 2024.
• Rastko Gajanin, “Navigating the Transition from Centralized to Federated Learning with Non-IID Data: A Human Activity Recognition Case Study”, Diploma Thesis, TU Wien, 2024.
• Anna Lackinger, “Towards Accurate Time Series Predictions for Cloud Workloads”, Diploma Thesis, TU Wien, 2023.
• Georg Reckendorfer, “Edge Computing Framework for the Use Case of Steel Saws in the Steel Industry”, Diploma Thesis, TU Wien, 2023.
• Robin Mayerhofer, “Reinforcement-learning-based, application-agnostic, and explainable auto-scaling in the cloud utilizing high-level SLOs”, Diploma Thesis, TU Wien, 2023.
• Lukas Ettenauer, “Stateful Serverless Computing: An evaluation of a new paradigm within cloud computing”, Diploma Thesis, TU Wien, 2023.
• Filip Loisel, “Decentralized Task Coordination in Heterogeneous IoT Clusters”, Diploma Thesis, TU Wien, 2023.
• Geri Zeqo, “Decentralized Communication in Heterogeneous IoT Clusters”, Diploma Thesis, TU Wien, 2023.
• Manuel Kroiß, “From Backend to Frontend - Case study on adopting Micro Frontends from a Single Page ERP Application monolith”, Diploma Thesis, TU Wien, 2020.

Academic Activities

Reviewer

I have been official reviewer for

• ACM Transactions on the Web
• Computer Networks
• Computers and Security
• IEEE Access
• IEEE Transaction on Services Computing
• …

Conferences/Workshops

Publication co-chair for

• 2023 - IEEE International Conference on High Performance Switching and Routing IEEE HPSR

I have been a PC member for

• 2020 - 2nd Workshop on AI in Networks and Distributed Systems, WAIN
• 2020 - 32nd International Conference on Advanced Information Systems Engineering, CAiSE’20
• 2019 - 3rd ACM CoNEXT Workshop on Big DAta, Machine Learning and AI for Data Communication Networks, Big-DAMA

Awards and Honors

• 2019 Best Poster Award - Runner up: Steps Towards Explainable AI for Clustering: the Case of Unsupervised QoE Analysis for YouTube Encrypted Traffic, in: TMA 2019, Paris, France.
• 2018 Best Student Paper Award: LENTA: Longitudinal Exploration for Network Traffic Analysis, in: 30th International Teletraffic Congress (ITC 30), Vienna, Austria.

Publications

1. Casamayor Pujol, V., Morichetta, A., Murturi, I., Kumar Donta, P., & Dustdar, S. (2023). Fundamental research challenges for distributed computing continuum systems. Information, 14(3), 198.
2. Morichetta, A., Pusztai, T., Vij, D., Pujol Vı́ctor Casamayor, Raith, P., Xiong, Y., … Zhang, Z. (2023). Demystifying deep learning in predictive monitoring for cloud-native SLOs. In 2023 IEEE 16th International Conference on Cloud Computing (CLOUD) (pp. 1–11). IEEE.
3. Pujol, V. C., Donta, P. K., Morichetta, A., Murturi, I., & Dustdar, S. (2023). Edge intelligence—research opportunities for distributed computing continuum systems. IEEE Internet Computing, 27(4), 53–74.
4. Pujol, V. C., Morichetta, A., & Nastic, S. (2023). Intelligent sampling: A novel approach to optimize workload scheduling in large-scale heterogeneous computing continuum. In 2023 IEEE International Conference on Service-Oriented System Engineering (SOSE) (pp. 140–149). IEEE.
5. Morichetta, A., Pujol, V. C., Nastic, S., Dustdar, S., Vij, D., Xiong, Y., & Zhang, Z. (2023). PolarisProfiler: A novel metadata-based profiling approach for optimizing resource management in the edge-cloud continnum. In 2023 18th Annual System of Systems Engineering Conference (SOSE).
6. Morichetta, A., Spring, N., Raith, P., & Dustdar, S. (2023). Intent-based Management for the Distributed Computing Continuum. In 2023 IEEE International Conference on Service-Oriented System Engineering (SOSE) (pp. 239–249). IEEE.
7. Bekbulatova, V., Morichetta, A., & Dustdar, S. (2023). FL-SERENADE: Federated Learning for SEmi-supeRvisEd Network Anomaly DEtection. A Case Study. In 2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) (pp. 1072–1079). IEEE.
8. Morichetta, A. (2023). The Cohabitation of Intelligence and Systems: Examples and Future Scenarios.
9. Morichetta, A. (2023). Distributed Intelligence: The Next Leap for the Distributed Computing Continuum.
10. Murturi, I., Donta, P. K., Pujol, V. C., Morichetta, A., & Dustdar, S. (2023). Learning-driven Zero Trust in Distributed Computing Continuum Systems. In 2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) (pp. 0044–0049). IEEE.
11. Hazra, A., Morichetta, A., Murturi, I., Lovén, L., Dehury, C. K., Pujol, V. C., … Dustdar, S. (2023). Distributed AI in Zero-touch Provisioning for Edge Networks: Challenges and Research Directions. ArXiv Preprint ArXiv:2311.17471.
12. Bartelucci, N., Bellavista, P., Pusztai, T., Morichetta, A., & Dustdar, S. (2022). High-Level Metrics for Service Level Objective-aware Autoscaling in Polaris: a Performance Evaluation. In 2022 IEEE 6th International Conference on Fog and Edge Computing (ICFEC) (pp. 73–77). IEEE.
13. Li, K., Wang, X., He, Q., Yi, B., Morichetta, A., & Huang, M. (2022). Cooperative multiagent deep reinforcement learning for computation offloading: a mobile network operator perspective. IEEE Internet of Things Journal, 9(23), 24161–24173.
14. Pusztai, T., Nastic, S., Morichetta, A., Pujol Vı́ctor Casamayor, Raith, P., Dustdar, S., … Zhang, Z. (2022). Polaris scheduler: SLO-and topology-aware microservices scheduling at the edge. In 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) (pp. 61–70). IEEE.
15. Casamayor Pujol, V., Donta, P. K., Morichetta, A., Murturi, I., & Dustdar, S. (2022). Distributed computing continuum systems–opportunities and research challenges. In International Conference on Service-Oriented Computing (pp. 405–407). Springer Nature Switzerland Cham.
16. Marcelletti, A., & Morichetta, A. (2022). Exploring the Benefits of Blockchain Technology for MLOps Pipeline.
17. Faroughi, A., Morichetta, A., Vassio, L., Figueiredo, F., Mellia, M., & Javidan, R. (2021). Towards website domain name classification using graph based semi-supervised learning. Computer Networks, 188, 107865.
18. Morichetta, A., Trevisan, M., Vassio, L., & Krickl, J. (2021). Understanding web pornography usage from traffic analysis. Computer Networks, 189, 107909.
19. Nastic, S., Pusztai, T., Morichetta, A., Pujol Vı́ctor Casamayor, Dustdar, S., Vii, D., & Xiong, Y. (2021). Polaris scheduler: Edge sensitive and slo aware workload scheduling in cloud-edge-iot clusters. In 2021 IEEE 14th International Conference on Cloud Computing (CLOUD) (pp. 206–216). IEEE.
20. Pusztai, T., Morichetta, A., Pujol Vı́ctor Casamayor, Dustdar, S., Nastic, S., Ding, X., … Xiong, Y. (2021). A novel middleware for efficiently implementing complex cloud-native slos. In 2021 IEEE 14th International Conference on Cloud Computing (CLOUD) (pp. 410–420). IEEE.
21. Pusztai, T., Morichetta, A., Pujol Vı́ctor Casamayor, Dustdar, S., Nastic, S., Ding, X., … Xiong, Y. (2021). Slo script: A novel language for implementing complex cloud-native elasticity-driven slos. In 2021 IEEE International Conference on Web Services (ICWS) (pp. 21–31). IEEE.
22. Morichetta, A., Pujol, V. C., & Dustdar, S. (2021). A roadmap on learning and reasoning for distributed computing continuum ecosystems. In 2021 IEEE International Conference on Edge Computing (EDGE) (pp. 25–31). IEEE.
23. Nastic, S., Morichetta, A., Pusztai, T., Dustdar, S., Ding, X., Vij, D., & Xiong, Y. (2020). Sloc: Service level objectives for next generation cloud computing. IEEE Internet Computing, 24(3), 39–50.
24. Morichetta, A. (2020). Machine Learning and Big Data Approaches for Automatic Internet Monitoring (PhD thesis). Politecnico di Torino.
25. Morichetta, A., Trevisan, M., & Vassio, L. (2019). Characterizing web pornography consumption from passive measurements. In International Conference on Passive and Active Network Measurement (pp. 304–316). Springer International Publishing Cham.
26. Morichetta, A., & Mellia, M. (2019). LENTA: Longitudinal exploration for network traffic analysis from passive data. IEEE Transactions on Network and Service Management, 16(3), 814–827.
27. D’Alconzo, A., Drago, I., Morichetta, A., Mellia, M., & Casas, P. (2019). A survey on big data for network traffic monitoring and analysis. IEEE Transactions on Network and Service Management, 16(3), 800–813.
28. Morichetta, A., & Mellia, M. (2019). Clustering and evolutionary approach for longitudinal web traffic analysis. Performance Evaluation, 135, 102033.
29. Morichetta, A., Casas, P., & Mellia, M. (2019). EXPLAIN-IT: Towards explainable AI for unsupervised network traffic analysis. In Proceedings of the 3rd ACM CoNEXT Workshop on Big DAta, Machine Learning and Artificial Intelligence for Data Communication Networks (pp. 22–28).
30. Morichetta, A., & Mellia, M. (2018). Lenta: Longitudinal exploration for network traffic analysis. In 2018 30th International Teletraffic Congress (ITC 30) (Vol. 1, pp. 176–184). IEEE.
31. Faroughi, A., Javidan, R., Mellia, M., Morichetta, A., Soro, F., & Trevisan, M. (2018). Achieving horizontal scalability in density-based clustering for URLs. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 3841–3846). IEEE.
32. Mellia, M., Metwalley, H., Bocchi, E., Morichetta, A., & others. (2018). A method for exploring traffic passive traces and grouping similar urls.
33. Ciociola, A., Cocca, M., Giordano, D., Mellia, M., Morichetta, A., Putina, A., & Salutari, F. (2017). UMAP: Urban mobility analysis platform to harvest car sharing data. In 2017 IEEE SmartWorld, ubiquitous intelligence & computing, advanced & trusted computed, scalable computing & communications, cloud & big data computing, internet of people and smart city innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp. 1–8). IEEE.
34. Morichetta, A., Bocchi, E., Metwalley, H., & Mellia, M. (2016). Clue: Clustering for mining web urls. In 2016 28th International Teletraffic Congress (ITC 28) (Vol. 1, pp. 286–294). IEEE.
35. Mellia, M., Metwalley, H., Bocchi, E., Morichetta, A., & others. (2016). METODO PER L’ESPLORAZIONE DI TRACCE PASSIVE DI TRAFFICO E RAGGRUPPAMENTO DI URL SIMILI.