Scientific Publications

This paper proposes performance models to predict GPU-deployed neural networks (NNs) training. The proposed approach is based on machine learning and exploits two main sets of features, thus capturing both NNs properties and hardware characteristics.

This paper presents a serverless web-based scientific gateway to execute the inference phase of previously trained machine learning and artificial intelligence models.

This work introduces TaScaaS, a highly scalable and completely serverless service deployed on AWS to distribute loosely coupled jobs among several computing infrastructures, and load balance them using a completely asynchronous approach to cope with the performance fluctuations with minimum impact in the execution time.

This work presents a framework for the automatic deployment of disaggregated and decomposed network functions. The framework comprises an orchestrator capable of deploying the decomposed network functions on programmable network hardware and software switches running in containers.

PERUN is a framework for confidential multi-stakeholder machine learning that allows users to make a trade-off between security and performance. PERUN executes ML training on hardware accelerators (e.g., GPU) while providing security guarantees using trusted computing technologies, such as trusted platform module and integrity measurement architecture.

The paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum.

This paper presents the work conducted in the area of Trust Domain Extensions (TDX) using ProVerif's specification language.