Scientific Publications

This paper presents and formally proves a novel defense against the cuckoo attack.

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 paper explains how efficient component placement and resource allocation algorithms are crucial to orchestrate at best the computing continuum resources.

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 paper proposes a solution to organise the devices within the Cloud-Edge Continuum in such a way that each one, as an autonomous individual –Agent–, processes events/data on its embedded compute resources while offering its computing capacity to the rest of the infrastructure in a Function-as-a-Service manner.

ADAM-CS is an asynchronous monotonic counter service to protect such high-traffic applications against rollback attacks. Leveraging a set of distributed monotonic counters and specific algorithms, ADAM-CS minimizes the maximum vulnerability window (MVW), i.e., the amount of transactions an adversary could successfully rollback.

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.

This paper presents the Neural Architecture Search (NAS): the process of automating architecture engineering, searching for the best deep learning configuration.

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.