| Software performance self-adaptation through efficient model predictive control E Incerto, M Tribastone, C Trubiani 2017 32nd IEEE/ACM International Conference on Automated Software …, 2017 | 50 | 2017 |
| Combined vertical and horizontal autoscaling through model predictive control E Incerto, M Tribastone, C Trubiani European Conference on Parallel Processing, 147-159, 2018 | 26 | 2018 |
| Learning queuing networks by recurrent neural networks G Garbi, E Incerto, M Tribastone Proceedings of the ACM/SPEC international conference on performance …, 2020 | 25 | 2020 |
| Symbolic performance adaptation E Incerto, M Tribastone, C Trubiani Proceedings of the 11th International Symposium on Software Engineering for …, 2016 | 20 | 2016 |
| Autoscaling solutions for cloud applications under dynamic workloads G Quattrocchi, E Incerto, R Pinciroli, C Trubiani, L Baresi IEEE Transactions on Services Computing 17 (3), 804-820, 2024 | 14 | 2024 |
| Moving horizon estimation of service demands in queuing networks E Incerto, A Napolitano, M Tribastone 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation …, 2018 | 13 | 2018 |
| An Efficient Performance-Driven Approach for HW/SW Co-Design D Di Pompeo, E Incerto, V Muttillo, L Pomante, G Valenete Proceedings of the 8th ACM/SPEC on International Conference on Performance …, 2017 | 11 | 2017 |
| Learning queuing networks via linear optimization E Incerto, A Napolitano, M Tribastone Proceedings of the ACM/SPEC International Conference on Performance …, 2021 | 9 | 2021 |
| A proactive approach for runtime self-adaptation based on queueing network fluid analysis E Incerto, M Tribastone, C Trubiani Proceedings of the 1st International Workshop on Quality-Aware DevOps, 19-24, 2015 | 8 | 2015 |
| A model-driven approach for the development of an ide for spacecraft on-board software L Pomante, S Candia, E Incerto 2015 IEEE Aerospace Conference, 1-17, 2015 | 7 | 2015 |
| Inference of probabilistic programs with moment-matching gaussian mixtures F Randone, L Bortolussi, E Incerto, M Tribastone Proceedings of the ACM on Programming Languages 8 (POPL), 1882-1912, 2024 | 6 | 2024 |
| μOpt: An Efficient Optimal Autoscaler for Microservice Applications E Incerto, R Pizziol, M Tribastone 2023 IEEE International Conference on Autonomic Computing and Self …, 2023 | 4 | 2023 |
| μP: A Development Framework for Predicting Performance of Microservices by Design G Garbi, E Incerto, M Tribastone 2023 IEEE 16th International Conference on Cloud Computing (CLOUD), 178-188, 2023 | 4 | 2023 |
| Inferring performance from code: a review E Incerto, A Napolitano, M Tribastone International Symposium on Leveraging Applications of Formal Methods, 307-322, 2020 | 4 | 2020 |
| Model-based performance self-adaptation: A tutorial E Incerto, M Tribastone Companion of the 2019 ACM/SPEC International Conference on Performance …, 2019 | 3 | 2019 |
| HW/SW Co-Design of Heterogeneous Multiprocessor Dedicated Systems: a SystemC-based Environment L Pomante, P Serri, E Incerto, J Volpe Proc. 2nd World Congr. Multimed. Comput. Sci, 9-11, 2014 | 3 | 2014 |
| Statistical learning of markov chains of programs E Incerto, A Napolitano, M Tribastone 2020 28th International Symposium on Modeling, Analysis, and Simulation of …, 2020 | 2 | 2020 |
| Flocks of birds: a quantitative evaluation E Incerto, C Trubiani International Symposium on Leveraging Applications of Formal Methods, 271-286, 2024 | 1 | 2024 |
| AIPerf'24: 2nd International Workshop on Artificial Intelligence for Performance Modeling, Prediction, and Control E Incerto, M Litoiu, D Masti Companion of the 15th ACM/SPEC International Conference on Performance …, 2024 | | 2024 |
| ICPE'23 AIPerf Workshop Chairs' Welcome E Incerto, M Litoiu, R Pinciroli Companion of the 2023 ACM/SPEC International Conference on Performance …, 2023 | | 2023 |
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