Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Marco Paolieri"'
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems. 33:2900-2912
Publikováno v:
ACM Transactions on Intelligent Systems and Technology. 13:1-25
Federated learning allows multiple users to collaboratively train a shared classification model while preserving data privacy. This approach, where model updates are aggregated by a central server, was shown to be vulnerable to poisoning backdoor att
Publikováno v:
ACM SIGMETRICS Performance Evaluation Review. 49:81-86
ORIS is a tool for quantitative modeling and evaluation of concurrent systems with non-Markovian durations. It provides a Graphical User Interface (GUI) for model specification as Stochastic Time Petri Nets (STPNs), validation by interactive simulati
Publikováno v:
Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering.
Publikováno v:
ACM Transactions on Modeling and Computer Simulation.
We evaluate a stochastic upper bound on the response time Probability Density Function (PDF) of complex workflows through an efficient and accurate compositional approach. Workflows consist of activities having generally distributed stochastic durati
Publikováno v:
ACM SIGMETRICS Performance Evaluation Review. 50:24-26
Due to a growing interest in deep learning applications [5], compute-intensive and long-running (hours to days) training jobs have become a significant component of datacenter workloads. A large fraction of these jobs is often exploratory, with the g
Autor:
Laura Carnevali, Marco Paolieri, Riccardo Reali, Leonardo Scommegna, Federico Tammaro, Enrico Vicario
Publikováno v:
Computer Performance Engineering ISBN: 9783031250484
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8151988f99375b10e387a703027e4c98
https://doi.org/10.1007/978-3-031-25049-1_13
https://doi.org/10.1007/978-3-031-25049-1_13
Publikováno v:
2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW).
Publikováno v:
IEEE Transactions on Software Engineering. 47:1211-1225
We present the next generation of ORIS, a toolbox for quantitative evaluation of concurrent models with non-Markovian timers. The tool shifts its focus from timed models to stochastic ones, it includes a new graphical user interface, new analysis met
Publikováno v:
2022 IEEE 15th International Conference on Cloud Computing (CLOUD).