Professional Joomla Templates Free

Projects

  • SOUNDLESS

    Research Area: Uncategorized

    Este proyecto se origina con la idea de crear una plataforma para ayudar a todos aquellos ciudadanos cansados de despertarse por las noches por una serie de ruidos originados por distintos motivos. Esta herramienta ha sido posible gracias algrupo de investigación Cloud and Distributed Systems Lab de la Universitat Rovira i Virgili, quehanllevado a cabo este proyecto de investigación de ciencia ciudadana llamado Soundless.

    Este e se enfoca en la auditoria de los efectos del ruidode lasciudadesen la salud de los residentes, en concreto en este caso, de la ciudad de Tarragona. Este proyecto ha sido posible cofinanciado porla Diputación de Tarragona a través del Conveni Marc, para tratar de dar luz a un problema que lleva ocurriendo desde hace algún tiempo.

    El objetivo principal de Soundlesses la creación de una herramienta, con el objetivo de ayudar a la ciudadaníaa identificar esos problemas de ruido que afectan su salud y solventarlos. Esta ha sido diseñada para poder ser utilizada por cientos de personas en cualquier ciudaddel mundo donde se considere que haya una alta incidencia de ruido.

  • CLOUDLESS

    Research Area: Uncategorized

    El objetivo es desarrollar una plataforma de computación en el borde (Edge) para la creación y despliegue de Espacios de Datos abiertos (International Data Spaces). La plataforma seguirá una arquitectura distribuida descentralizada que ofrezca servicios de descubrimiento e interconexión abiertos (data brokers) basados en eventos a diferentes consumidores (data consumers) y proveedores de datos (data providers). La arquitectura ofrecerá también infraestructuras seguras Cloud/Edge que permitan el análisis de datos eficiente (data connectors) en base a variables como localidad, coste económico, privacidad o latencia. La plataforma será validada en diferentes espacios de datos como la ciencia ciudadana, el turismo o los datos ómicos (genómica y metabolómica).

  • PLANET: Augmented Collaborative Platform for content distribution and advanced training

    Research Area: Collaborative applications and middleware

    Ref. TIC2003-09288-C02-00

    The main goal of this project is to develop a low-cost multiuser collaborative platform for advanced training in settings like Architecture, Medicine or scientific simulation. The platform will extend an existing Collaborative Virtual Environment (MOVE) in order to provide advanced interaction and visualization with immersive virtual reality 3D devices like Head Mounted Displays, gloves and stereoscopic projection systems. We will also experiment with augmented reality systems in mobile scenarios in order to generate improved training contents for the selected disciplines.

  • POPEYE: Peer to Peer Collaborative Working Environments over Mobile AdHoc Networks

    Research Area: Mobile Ad-hoc Networks

    Ref. IST-2006-034241

    Next generation collaborative systems will offer the mobile users seamless and natural collaboration amongst a diversity of agents within distributed, knowledge-rich and virtualised working environment. However this ambitious goal needs to face numerous challenges from the underlying communication infrastructure through to the high level application services which, depending on the operational need to address, can receive different answers both technological and scientific.

    When most of the currently available tools supporting collaboration are based on a rigid client-server architecture and rely on a communication infrastructure like the Internet, POPEYE’ ambition is to get collaborative working free from such constraints, i.e. in an infrastructure-less environment.

  • P2PGRID: Self-adjusting P2P and Grid Systems

    Research Area: Peer-to-peer computing

    Ref. TIN2007-68050-C03-03

    P2PGRID project is focused on the design of innovative large scale distributed systems, decentralized, able to adapt to complex environments – in terms of infrastructure or use- and heterogeneous in the access to communication and computation resources.

    A key goal is the use of economic algorithms in order to create a Grid infrastructure with efficient resource allocation mechanisms. It is thus necessary the study, design, and evaluation of resource allocation algorithms in distributed computation systems. Such systems must take into account several variables --like client demand, resource state and availability, and the competition among services—to provide decentralized and autonomous system adaptivity.

  • NEUROLEARNING: Sistema Telemédico de Intervención Neuropsicológica basado en Entornos Virtuales Interactivos

    Research Area: E-learning

    Ref. TSI-020501-2008-154

    El grupo de Arquitecturas y Servicios Telemáticos (AST) del Departamento de Ingeniería Informática y Matemáticas ha desarrollado la plataforma PREVIRNEC en el contexto de un proyecto de investigación con el Instituto Guttman y el Centro de Investigación en Ingeniería Biomédica (CREB) de la UPC. Previrnec es un novedoso sistema de neuro-rehabilitación que funciona a través de Internet y permite tratar problemas de atención, memoria y diversas disfunciones neuro-cognitivas. Mediante dicho sistema, terapeutas expertos del

  • DELFIN: Self-adapting decentralized systems for the Future Internet

    Research Area: Distributed storage

    Ref. TIN2010-20140-C03-03

    The DELFIN project is focused on the design of self-adapting decentralized systems for the Future Internet. The network of the future is facing serious challenges due to an enormous increase in traffic, computing power and a miriad of novel devices connected to the network. For this reason, a new generation of distributed systems must provide extreme scalability and adaptivity to variable node and network conditions. We aim to cover the whole feedback loop of adaptive systems including: sensing and analysis, control and self-regulation, and finally system adaptation and reaction.

  • RealCloud: Aplicación del Cloud Computing en empresas

    Research Area: Cloud Computing

    Ref. IPT-2011-1232-430000

    Las soluciones de Cloud Computing (Iaas, Paas, y SaaS) mejoran la eficiencia y agilidad de la operaciones de las TIC, al permitir un mejor aprovechamiento de las infraestructuras y plataformas TIC con el subsiguiente ahorro energético, al tiempo que permiten ofrecer servicios más aquilatados en costes y con modelo de facturación de pago por uso. Además, permiten asegurar la disponibilidad del servicio, realizar planes DRP de bajo coste y la posibilidad de crecer bajo demanda fácilmente.

  • CloudSpaces: Open Service Platform for the Next Generation of Personal Clouds

    Research Area: Cloud Computing

    Funding

    ,

    EUR 4.011.303,-

    In the following years, users will access their data from a variety of devices, operating systems and applications. The CloudSpaces project advocates for a paradigm shift from application-centric to person-centric models where users will retake the control of their information. CloudSpaces aims to create the next generation of open Personal Clouds using three main building blocks: CloudSpaces Share, CloudSpaces Storage and CloudSpaces Services.

  • EXTRACT

    Research Area: Cloud Computing

    Funding

    ,

    AUD 4.997.875,-

     

    Ref. HE-101093110

    Data has become one of the most valuable assets, driving the digital transformation across many sectors. Current data mining solutions are optimized to deal with specific data requirements, but fail to cope as the data characteristics become extreme. There is therefore an urgent need for novel and holistic approaches to enable the development, deployment and efficient execution of datamining workflows across a heterogeneous, secure and energy-efficient compute continuum, while fulfilling the diverse extreme data characteristics.

    To fill this technological gap, EXTRACT will deliver a data-driven open-source software platform integrating the most relevant technologies, to facilitate the development of trustworthy, accurate, fair and green data mining workflows able to generate high-quality actionable knowledge. The EXTRACT platform will improve the complete lifecycle of extreme data mining workflows, significantly enhancing performance, energy-efficiency, scalability and security, while fulfilling the extreme data characteristics in aholistic way. Moreover, multiple computing technologies, from edge to cloud to HPC, will be integrated into a unified and secure compute continuum. Specifically, the platform will feature enhanced data infrastructures and AI & big-data frameworks, novel data-driven orchestration and distributed monitoring mechanisms, a unified continuum abstraction and cybersecurity and digital privacy across all software layers.

    The EXTRACT platform will be validated in two real-world use-cases with different extreme data requirements:

    1. Personalized Evacuation Route service, integrating data from the European data sources, Copernicus and Galileo, with 5G localization signals and smart city IoT sensors for civilian-centric crisis management; and

    2. Transient Astrophysics with a SKA pathfinder, processing extreme data from 2000 radio-telescopes for the real-time assessment of solar activity, generating knowledge for further scientific exploitation.

  • Cloud4Comm: Servicios Cloud y redes comunitarias

    Research Area: Cloud Computing

    Ref: TIN2013-47245-C2-2-R

    The size, growth and scope of the Internet and the perception of the key role of infrastructures for access and services in the digital society has put under stress the architecture and protocols shifting from pure networking, to end-user oriented services: the Cloud. The aim of this project is to provide the means for communities of citizens in bootstrapping, running and expanding community-owned networks that provide community services organised as community clouds, key for the development and sustainability of the digital society. To achieve that aim the main objective is solving specific research challenges around:

    1. Self-managing and scalable infrastructure services for the management and aggregation of a large number of widespread networking, storage and limited computing resources;

    2. Distributed and persistent storage, computation and communication platform services to support and facilitate the design and operation of

    3. Elastic, resilient and scalable service overlays and user-oriented services built over these underlying services, providing a good quality of experience at the lowest economic and environmental cost.

     

  • IOStack: Software-defined Storage for Big Data

    Research Area: Distributed storage

    Funding

    ,

    EUR 3.318.624,-

    The main objective is to create IOStack: a Software-defined Storage toolkit for Big Data on top of the OpenStack platform. IOStack will enable efficient execution of virtualized analytics applications over virtualized storage resources thanks to flexible, automated, and low cost data management models based on software-defined storage (SDS).

  • SWEDGE: Software-Defined Edge Clouds

    Research Area: Cloud Computing

    Ref: TIN2016-77836-C2-1-R

    We envision an evolution in Cloud platforms from purely centralized data centers to more heterogeneous and distributed infrastructures integrating a myriad of devices in the edge of the Internet. On the one hand, there are growing user and enterprise concerns about trust, privacy, and autonomy that require taking the control of computing applications, data, and services away from a few central nodes (data centers) to the logical extremes (the edges) of the Internet. On the other hand, the proliferation of edge devices, increased connectivity and powerful wireless networks involve interesting opportunities for hybrid cloud services and networks in the extremes of the Internet.

    The aim of this project is twofold: to provide the means for users, enterprises and institutions to retake the control of their digital resources (digital data, processing and transfer) and to deliver services to the edge such that data does not necessarily need to go beyond edge boundaries. We outline the two major research challenges:

    1. Software defined controls and architectures to enable automated data management and protection of data (namely, software defined protection) for cloud repositories. Instead of relinquishing control over data, customers will have the chance to retake control, without losing the advantages of cloud processing.

    2. Elastic, resilient and scalable service overlays and user-oriented cloud services built over open IP-based networks, providing a good quality of experience at the lowest economic and environmental cost. The result sought is empowering citizens and communities at the edge of Internet, to grasp the opportunity of digital participation by providing a digital ecosystem of services.

  • CloudButton: Serverless Data Analytics Platform

    Research Area: Cloud Computing

    Funding

    ,

    EUR 4.277.507,-

    Ref. H2020-825184

    The main goal of this project is to create CloudButton: a Serverless Data Analytics Platform. CloudButton will democratize big data by overly simplifying the overall life cycle and programming model thanks to serverless technologies. To demonstrate the impact of the project, we target two settings with large data volumes: bioinformatics (genomics, metabolomics) and geospatial data (LiDAR, satellital).

  • CLOUDSKIN

    Research Area: Cloud Computing

    Funding

    ,

    EUR 3.405.322,-

    Ref. HE-101092646

     

    CLOUDSKIN aims to design a cognitive cloud continuum platform to fully exploit the available Cloud-edge heterogeneous resources, finding the “sweet spot” between the cloud and the edge, and smartly adapting to changes in application behavior via AI. To facilitate automatic deployment, mobility and security of services, CloudSkin will build an innovative universal container-like execution abstraction based on WebAssembly that allows the seamless and trustworthy execution of (legacy) applications across the Cloud-edge continuum.

    The goals of CLOUDSKIN are the following:

    • Smart management for the Cloud-edge continuum: The overall objective is to leverage the generated knowledge from state-of-art AI methods to transparently orchestrate Cloud-edge resources. The key goal is to build a “Learning Plane” that, in cooperation with the application execution framework and continuum infrastructure, can enhance the overall orchestration of Cloud-edge resources. Such plane is the materialization of the cognitive cloud, where decisions on the cloud and the edge are driven by the continuously obtained knowledge and awareness of the computing environment through AI, and particularly, neural networks and statistical learning, taking the challenge of enabling these methods into low-power edge devices.

    • Virtual execution for the Cloud-edge continuum: This goal focuses on a new universal and flexible execution abstraction, we called it “Cloud-edge cells”, that will enable the execution of legacy and highly granular applications in the cloud continuum. The new container-like execution abstraction will be based on the WebAssembly technology. It will enable the execution of the same computation on a wide range of cloud and embedded devices and make task execution migratable across different servers and devices in the continuum infrastructure. We will integrate our WebAssembly executor with Kubernetes. More specifically, we will contribute new features to Kubernetes that will support the efficient migration of WebAssembly containers between different levels of the continuum, exploiting WebAssembly’s capability for state serialization.

    • Infrastructure support for the Cloud-edge continuum: This objective is to prepare the infrastructure to turn it into a virtual resource continuum, where the large set of Cloud-edge cells composing applications can be allocated flexible resources, according to their dynamically changing needs. One of the major challenges here is to design an infrastructure to support extremely short-lived Cloud-edge cells and tasks (of 1 to 10ms, or less) and extremely intense bursts with fast data access requirements. This requires delivering bare metal resource performance to storage, despite virtualization and dynamic reallocation, which today is not possible in the cloud continuum. CLOUDSKIN will achieve this by leveraging high-performance I/O (RDMA networking) and near-storage CPU compute capacity (GPUs, FPGAs) to the fine-grained application tasks.

  • NEARDATA

    Research Area: Cloud Computing

    Funding

    ,

    EUR 3.913.585,-

     

    Ref. HE-101092644

    The goal of NEARDATA is to create an extreme data infrastructure mediating data flows between Object Storage and Data Analytics platforms across the Compute Continuum. Our novel XtremeDataHub platform is an intermediary data service that intercepts and optimises data flows (S3 API, stream APIs) with highperformance near-data connectors (Cloud/Edge). Finally, our unique Data Broker service will provide secure data access and orchestration of dispersed data sources thanks to TEEs and federated learning architectures. Our NEARDATA platform is a novel technology for data mining of large and dispersed unstructured data sets that can be deployed in the Cloud and in the Edge (HPC, IoT Devices), that leverages advanced AI technologies and offers a novel confidential cybersecurity layer for trusted data computation.

    The goals of NearData are the following:

    • Provide high-performance near-data processing for Extreme Data Types: The first objective is to create a novel intermediary data service (XtremeDataHub) providing serverless data connectors that optimize data management operations (partitioning, filtering, transformation, aggregation) and interactive queries (search, discovery, matching, multi-object queries) to efficiently present data to analytics platforms. Our data connectors facilitate a elas- tic data-driven process-then-compute paradigm which significantly reduces data communication on the data interconnect, ultimately resulting in higher overall data throughput.

    • Support real-time video streams but also event streams that must be ingested and processed very fast to Object Storage: The second objective is to seamlessly combine streaming and batch data processing for analytics. To this end, we will develop stream data connectors deployed as stream operators offering very fast stateful computations over low-latency event and video streams.

    • The third objective is to create a Data Broker service enabling trustworthy data sharing and confidential orchestration of data pipelines across the Compute Continuum. We will provide secure data orchestration, transfer, processing and access thanks to Trusted Execution Environments (TEEs) and federated learning architectures.

  • CLOUDSTARS

    Research Area: Cloud Computing

    Funding

    ,

    EUR 1.301.800,-

    Ref. HE- 101086248

    CLOUDSTARS presents a joint research programme in the fields of Cloud computing and AI technologies. CloudStars pursues innovation in the Cloud infrastructures to support the next generation of low-latency, high-performance complex workloads. CloudStars also pursues high innovation in the Cloud, making the best application of artificial intelligence techniques and AI models with automatic adaptation to the computing resources.

    The main objectives of CLOUDSTARS are:

    • Development and benchmarking of open-source technologies to advance the next generation cloud infrastructure, where it will play a vital role the development of new container technologies, as well as elastic and low-latency storage for ephemeral data, efficient resource management of hardware accelerators (GPUs, FPGAs and smartNICs) and networking.

    • Design and benchmarking of novel cloud and edge serverless middleware to leverage the advances in the cloud infrastructure, which pivots upon all sorts of containerized technologies such as Function-as-a-Service (FaaS) platforms, serverless containers, and event-based orchestration to open the serverless execution model to HPC and analytics workloads. The project will also explore how the platform should protect and govern data throughout its lifecycle.

    • Application of novel machine learning techniques (e.g., statistical and deep learning) for managing containerized cloud systems, involving the infrastructure and configuration of executions and services, and the optimization of data analytics platforms over serverless middleware and container infrastructures. The project will also explore the interplay between Edge and Cloud for enabling a diverse range of AI-enabled applications.

    We note that CLOUDSTARS will also conduct standardization activities in industrial open-source projects such as those encompassed by the Cloud Native Computing Foundation (CNCF), namely, Kubernetes, Istio, or Knative, to mention a few, and contribute to thriving open-source communities such as Apache Spark and Dask, or Lithops. The project will make public all the contributions to the different open-source projects and contribute to the European digital sovereignty and European standards such as Gaia-X.

Results 1 - 18 of 18

Login Form

Joomla Templates
Joomla Templates
Joomla Templates
Joomla Templates
Joomla Templates