Performance_prediction_of_deep_learning_applications_training_in_GPU_as_a_service_systems

Performance prediction of deep learning applications training in GPU as a service systems

Journal paper
31 March 2022
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.
A_serverless_gateway_for_event_driven_machine_learning_inference_in_multiple_clouds

A serverless gateway for event-driven machine learning inference in multiple clouds

Journal paper
15 December 2021
This paper presents a serverless web-based scientific gateway to execute the inference phase of previously trained machine learning and artificial intelligence models.
TaScaaS_A_Multi_Tenant_Serverless_Task_Scheduler_and_Load_Balancer_as_a_Service

TaScaaS: A Multi-Tenant Serverless Task Scheduler and Load Balancer as a Service

Journal paper
03 September 2021
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.
Network_Function_Decomposition_and_Offloading_on_Heterogeneous_Networks_With_Programmable_Data_Planes

Network Function Decomposition and Offloading on Heterogeneous Networks With Programmable Data Planes

Journal paper
02 August 2021
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_Confidential_Multi_stakeholder_Machine_Learning_Framework_with_Hardware_Acceleration_Support

PERUN: Confidential Multi-stakeholder Machine Learning Framework with Hardware Acceleration Support

Journal paper
14 July 2021
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.
Serverless_Workflows_for_Containerised_Applications_in_the_Cloud_Continuum

Serverless Workflows for Containerised Applications in the Cloud Continuum

Journal paper
13 July 2021
The paper introduces an open-source platform to support serverless computing for scientific data-processing workflow-based applications across the Cloud continuum.