New techniques to integrate blockchain in Internet of Things scenarios for massive data management

Mención Internacional en el título de doctorNowadays, regardless of the use case, most IoT data is processed using workflows that are executed on different infrastructures (edge-fog-cloud), which produces dataflows from the IoT through the edge to the fog/cloud. In many cases, they also involve several actors (organizations and users), which poses a challenge for organizations to establish verification of the transactions performed by the participants in the dataflows built by the workflow engines and pipeline frameworks. It is essential for organizations, not only to verify that the execution of applications is performed in the strict sequence previously established in a DAG by authenticated participants, but also to verify that the incoming and outgoing IoT data of each stage of a workflow/pipeline have not been altered by third parties or by the users associated to the organizations participating in a workflow/pipeline. Blockchain technology and its mechanism for recording immutable transactions in a distributed and decentralized manner, characterize it as an ideal technology to support the aforementioned challenges and challenges since it allows the verification of the records generated in a secure manner. However, the integration of blockchain technology with workflows for IoT data processing is not trivial considering that it is a challenge not to lose the generalization of workflows and/or pipeline engines, which must be modified to include the embedded blockchain module. The main objective of this doctoral research was to create new techniques to use blockchain in the Internet of Things (IoT). Thus, we defined the main goal of this thesis is to develop new techniques to integrate blockchain in Internet of Things scenarios for massive data management in edge-fog-cloud environments. To fulfill this general objective, we have designed a content delivery model for processing big IoT data in Edge-Fog-Cloud computing by using micro/nanoservice composition, a continuous verification model based on blockchain to register significant events from the continuous delivery model, selecting techniques to integrate blockchain in quasi-real systems that allow ensuring traceability and non-repudiation of data obtained from devices and sensors. The evaluation proposed has been thoroughly evaluated, showing its feasibility and good performance.Hoy en día, independientemente del caso de uso, la mayoría de los datos de IoT se procesan utilizando flujos de trabajo que se ejecutan en diferentes infraestructuras (edge-fog-cloud) desde IoT a través del edge hasta la fog/cloud. En muchos casos, también involucran a varios actores (organizaciones y usuarios), lo que plantea un desafío para las organizaciones a la hora de verificar las transacciones realizadas por los participantes en los flujos de datos. Es fundamental para las organizaciones, no solo para verificar que la ejecución de aplicaciones se realiza en la secuencia previamente establecida en un DAG y por participantes autenticados, sino también para verificar que los datos IoT entrantes y salientes de cada etapa de un flujo de trabajo no han sido alterados por terceros o por usuarios asociados a las organizaciones que participan en el mismo. La tecnología Blockchain, gracias a su mecanismo para registrar transacciones de manera distribuida y descentralizada, es un tecnología ideal para soportar los retos y desafíos antes mencionados ya que permite la verificación de los registros generados de manera segura. Sin embargo, la integración de la tecnología blockchain con flujos de trabajo para IoT no es baladí considerando que es un desafío proporcionar el rendimiento necesario sin perder la generalización de los motores de flujos de trabajo, que deben ser modificados para incluir el módulo blockchain integrado. El objetivo principal de esta investigación doctoral es desarrollar nuevas técnicas para integrar blockchain en Internet de las Cosas (IoT) para la gestión masiva de datos en un entorno edge-fog-cloud. Para cumplir con este objetivo general, se ha diseñado un modelo de flujos para procesar grandes datos de IoT en computación Edge-Fog-Cloud mediante el uso de la composición de micro/nanoservicio, un modelo de verificación continua basado en blockchain para registrar eventos significativos de la modelo de entrega continua de datos, seleccionando técnicas para integrar blockchain en sistemas cuasi-reales que permiten asegurar la trazabilidad y el no repudio de datos obtenidos de dispositivos y sensores, La evaluación propuesta ha sido minuciosamente evaluada, mostrando su factibilidad y buen rendimiento.This work has been partially supported by the project "CABAHLA-CM: Convergencia Big data-Hpc: de los sensores a las Aplicaciones" S2018/TCS-4423 from Madrid Regional Government.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Paolo Trunfio.- Secretario: David Exposito Singh.- Vocal: Rafael Mayo Garcí

Fast algorithm for real-time rings reconstruction

The GAP project is dedicated to study the application of GPU in several contexts in which real-time response is important to take decisions. The definition of real-time depends on the application under study, ranging from answer time of μs up to several hours in case of very computing intensive task. During this conference we presented our work in low level triggers [1] [2] and high level triggers [3] in high energy physics experiments, and specific application for nuclear magnetic resonance (NMR) [4] [5] and cone-beam CT [6]. Apart from the study of dedicated solution to decrease the latency due to data transport and preparation, the computing algorithms play an essential role in any GPU application. In this contribution, we show an original algorithm developed for triggers application, to accelerate the ring reconstruction in RICH detector when it is not possible to have seeds for reconstruction from external trackers

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance and Power-Aware Graph Processing on GPUs

Graphs are a common representation in many problem domains, including engineering, finance, medicine, and scientific applications. Different problems map to very large graphs, often involving millions of vertices. Even though very efficient sequential implementations of graph algorithms exist, they become impractical when applied on such actual very large graphs. On the other hand, graphics processing units (GPUs) have become widespread architectures as they provide massive parallelism at low cost. Parallel execution on GPUs may achieve speedup up to three orders of magnitude with respect to the sequential counterparts. Nevertheless, accelerating efficient and optimized sequential algorithms and porting (i.e., parallelizing) their implementation to such many-core architectures is a very challenging task. The task is made even harder since energy and power consumption are becoming constraints in addition, or in same case as an alternative, to performance. This work aims at developing a platform that provides (I) a library of parallel, efficient, and tunable implementations of the most important graph algorithms for GPUs, and (II) an advanced profiling model to analyze both performance and power consumption of the algorithm implementations. The platform goal is twofold. Through the library, it aims at saving developing effort in the parallelization task through a primitive-based approach. Through the profiling framework, it aims at customizing such primitives by considering both the architectural details and the target efficiency metrics (i.e., performance or power)

Next Generation Nuclear Plant Pre-Conceptual Design Report

Pre-conceptual design report for the Next Generation Nuclear Plan