Descriptive and predictive data analytics techniques to monitor the performance
Developer and open-source communities.
Apply best practices, mitigation actions and integrate services derived from the project to improve quantitatively the trustworthiness, both a priori and at runtime. Improve the trustworthiness of the application
Performance prediction for supporting development of serverless applications.
7BULLS and E4Company, along with POLIMI are planning to embed the performance models within an advanced scheduler for disaggregated resource hardware, for the H2020 TETRAMAX proposal they are working together, for the 3rd open call on Value Chain Oriented and Interdisciplinary Technology Transfer Experiments.
POLIMI is opening a self-fund a new one-year position to support the open-source version of the machine learning library developed within the ATMOSPHERE project (i.e., a-MLLibrary) and to start the development of an online scheduler for GPU-based disaggregated clusters.
Future exploitation and sustainability plans based on new funding opportunities that POLIMI and Federal University of Minas Gerais are looking for.