FfDL : A Flexible Multi-tenant Deep Learning Platform

Deep learning (DL) is becoming increasingly popular in several application domains and has made several new application features involving computer vision, speech recognition and synthesis, self-driving automobiles, drug design, etc. feasible and accurate. As a result, large scale on-premise and cloud-hosted deep learning platforms have become essential infrastructure in many organizations. These systems accept, schedule, manage and execute DL training jobs at scale. This paper describes the design, implementation and our experiences with FfDL, a DL platform used at IBM. We describe how our design balances dependability with scalability, elasticity, flexibility and efficiency. We examine FfDL qualitatively through a retrospective look at the lessons learned from building, operating, and supporting FfDL; and quantitatively through a detailed empirical evaluation of FfDL, including the overheads introduced by the platform for various deep learning models, the load and performance observed in a real case study using FfDL within our organization, the frequency of various faults observed including unanticipated faults, and experiments demonstrating the benefits of various scheduling policies. FfDL has been open-sourced.Comment: MIDDLEWARE 201

Enabling Mobile Service Continuity across Orchestrated Edge Networks

Edge networking has become an important technology for providing low-latency services to end users. However, deploying an edge network does not guarantee continuous service for mobile users. Mobility can cause frequent interruptions and network delays as users leave the initial serving edge. In this paper, we propose a solution to provide transparent service continuity for mobile users in large-scale WiFi networks. The contribution of this work has three parts. First, we propose ARNAB architecture to achieve mobile service continuity. The term ARNAB means rabbit in Arabic, which represents an Architecture for Transparent Service Continuity via Double-tier Migration. The first tier migrates user connectivity, while the second tier migrates user containerized applications. ARNAB provides mobile services just like rabbits hop through the WiFi infrastructure. Second, we identify the root-causes for prolonged container migration downtime. Finally, we enhance the container migration scheme by improving system response time. Our experimental results show that the downtime of ARNAB container migration solution is 50% shorter than that of the state-of-the-art migration.This work has been partially funded by the H2020 Europe/Taiwan joint action 5G-DIVE (Grant #859881) and also partially funded by the Ministry of Science and Technology, under the Grant Number MOST 108-2634-F-009-006 - through Pervasive Artificial Intelligence Research (PAIR) Labs, Taiwan

Autonomic Approach based on Semantics and Checkpointing for IoT System Management

Le résumé en français n'a pas été communiqué par l'auteur.Le résumé en anglais n'a pas été communiqué par l'auteur

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS

Self-management for large-scale distributed systems

Autonomic computing aims at making computing systems self-managing by using autonomic managers in order to reduce obstacles caused by management complexity. This thesis presents results of research on self-management for large-scale distributed systems. This research was motivated by the increasing complexity of computing systems and their management. In the first part, we present our platform, called Niche, for programming self-managing component-based distributed applications. In our work on Niche, we have faced and addressed the following four challenges in achieving self-management in a dynamic environment characterized by volatile resources and high churn: resource discovery, robust and efficient sensing and actuation, management bottleneck, and scale. We present results of our research on addressing the above challenges. Niche implements the autonomic computing architecture, proposed by IBM, in a fully decentralized way. Niche supports a network-transparent view of the system architecture simplifying the design of distributed self-management. Niche provides a concise and expressive API for self-management. The implementation of the platform relies on the scalability and robustness of structured overlay networks. We proceed by presenting a methodology for designing the management part of a distributed self-managing application. We define design steps that include partitioning of management functions and orchestration of multiple autonomic managers. In the second part, we discuss robustness of management and data consistency, which are necessary in a distributed system. Dealing with the effect of churn on management increases the complexity of the management logic and thus makes its development time consuming and error prone. We propose the abstraction of Robust Management Elements, which are able to heal themselves under continuous churn. Our approach is based on replicating a management element using finite state machine replication with a reconfigurable replica set. Our algorithm automates the reconfiguration (migration) of the replica set in order to tolerate continuous churn. For data consistency, we propose a majority-based distributed key-value store supporting multiple consistency levels that is based on a peer-to-peer network. The store enables the tradeoff between high availability and data consistency. Using majority allows avoiding potential drawbacks of a master-based consistency control, namely, a single-point of failure and a potential performance bottleneck. In the third part, we investigate self-management for Cloud-based storage systems with the focus on elasticity control using elements of control theory and machine learning. We have conducted research on a number of different designs of an elasticity controller, including a State-Space feedback controller and a controller that combines feedback and feedforward control. We describe our experience in designing an elasticity controller for a Cloud-based key-value store using state-space model that enables to trade-off performance for cost. We describe the steps in designing an elasticity controller. We continue by presenting the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores that combines feedforward and feedback control

Automatic Generation of Distributed Runtime Infrastructure for Internet of Things

Ph. D. ThesisThe Internet of Things (IoT) represents a network of connected devices that are able to cooperate and interact with each other in order to reach a particular goal. To attain this, the devices are equipped with identifying, sensing, networking and processing capabilities. Cloud computing, on the other hand, is the delivering of on-demand computing services – from applications, to storage, to processing power – typically over the internet. Clouds bring a number of advantages to distributed computing because of highly available pool of virtualized computing resource. Due to the large number of connected devices, real-world IoT use cases may generate overwhelmingly large amounts of data. This prompts the use of cloud resources for processing, storage and analysis of the data. Therefore, a typical IoT system comprises of a front-end (devices that collect and transmit data), and back-end – typically distributed Data Stream Management Systems (DSMSs) deployed on the cloud infrastructure, for data processing and analysis. Increasingly, new IoT devices are being manufactured to provide limited execution environment on top of their data sensing and transmitting capabilities. This consequently demands a change in the way data is being processed in a typical IoT-cloud setup. The traditional, centralised cloud-based data processing model – where IoT devices are used only for data collection – does not provide an efficient utilisation of all available resources. In addition, the fundamental requirements of real-time data processing such as short response time may not always be met. This prompts a new processing model which is based on decentralising the data processing tasks. The new decentralised architectural pattern allows some parts of data streaming computation to be executed directly on edge devices – closer to where the data is collected. Extending the processing capabilities to the IoT devices increases the robustness of applications as well as reduces the communication overhead between different components of an IoT system. However, this new pattern poses new challenges in the development, deployment and management of IoT applications. Firstly, there exists a large resource gap between the two parts of a typical IoT system (i.e. clouds and IoT devices); hence, prompting a new approach for IoT applications deployment and management. Secondly, the new decentralised approach necessitates the deployment of DSMS on distributed clusters of heterogeneous nodes resulting in unpredictable runtime performance and complex fault characteristics. Lastly, the environment where DSMSs are deployed is very dynamic due to user or device mobility, workload variation, and resource availability. In this thesis we present solutions to address the aforementioned challenges. We investigate how a high-level description of a data streaming computation can be used to automatically generate a distributed runtime infrastructure for Internet of Things. Subsequently, we develop a deployment and management system capable of distributing different operators of a data streaming computation onto different IoT gateway devices and cloud infrastructure. To address the other challenges, we propose a non-intrusive approach for performance evaluation of DSMSs and present a protocol and a set of algorithms for dynamic migration of stateful data stream operators. To improve our migration approach, we provide an optimisation technique which provides minimal application downtime and improves the accuracy of a data stream computation

A Design Framework for Efficient Distributed Analytics on Structured Big Data

Distributed analytics architectures are often comprised of two elements: a compute engine and a storage system. Conventional distributed storage systems usually store data in the form of files or key-value pairs. This abstraction simplifies how the data is accessed and reasoned about by an application developer. However, the separation of compute and storage systems makes it difficult to optimize costly disk and network operations. By design the storage system is isolated from the workload and its performance requirements such as block co-location and replication. Furthermore, optimizing fine-grained data access requests becomes difficult as the storage layer is hidden away behind such abstractions. Using a clean slate approach, this thesis proposes a modular distributed analytics system design which is centered around a unified interface for distributed data objects named the DDO. The interface couples key mechanisms that utilize storage, memory, and compute resources. This coupling makes it ideal to optimize data access requests across all memory hierarchy levels, with respect to the workload and its performance requirements. In addition to the DDO, a complementary DDO controller implementation controls the logical view of DDOs, their replication, and distribution across the cluster. A proof-of-concept implementation shows improvement in mean query time by 3-6x on the TPC-H and TPC-DS benchmarks, and more than an order of magnitude improvement in many cases

Programming models to support data science workflows

Data Science workflows have become a must to progress in many scientific areas such as life, health, and earth sciences. In contrast to traditional HPC workflows, they are more heterogeneous; combining binary executions, MPI simulations, multi-threaded applications, custom analysis (possibly written in Java, Python, C/C++ or R), and real-time processing. Furthermore, in the past, field experts were capable of programming and running small simulations. However, nowadays, simulations requiring hundreds or thousands of cores are widely used and, to this point, efficiently programming them becomes a challenge even for computer sciences. Thus, programming languages and models make a considerable effort to ease the programmability while maintaining acceptable performance. This thesis contributes to the adaptation of High-Performance frameworks to support the needs and challenges of Data Science workflows by extending COMPSs, a mature, general-purpose, task-based, distributed programming model. First, we enhance our prototype to orchestrate different frameworks inside a single programming model so that non-expert users can build complex workflows where some steps require highly optimised state of the art frameworks. This extension includes the @binary, @OmpSs, @MPI, @COMPSs, and @MultiNode annotations for both Java and Python workflows. Second, we integrate container technologies to enable developers to easily port, distribute, and scale their applications to distributed computing platforms. This combination provides a straightforward methodology to parallelise applications from sequential codes along with efficient image management and application deployment that ease the packaging and distribution of applications. We distinguish between static, HPC, and dynamic container management and provide representative use cases for each scenario using Docker, Singularity, and Mesos. Third, we design, implement and integrate AutoParallel, a Python module to automatically find an appropriate task-based parallelisation of affine loop nests and execute them in parallel in a distributed computing infrastructure. It is based on sequential programming and requires one single annotation (the @parallel Python decorator) so that anyone with intermediate-level programming skills can scale up an application to hundreds of cores. Finally, we propose a way to extend task-based management systems to support continuous input and output data to enable the combination of task-based workflows and dataflows (Hybrid Workflows) using one single programming model. Hence, developers can build complex Data Science workflows with different approaches depending on the requirements without the effort of combining several frameworks at the same time. Also, to illustrate the capabilities of Hybrid Workflows, we have built a Distributed Stream Library that can be easily integrated with existing task-based frameworks to provide support for dataflows. The library provides a homogeneous, generic, and simple representation of object and file streams in both Java and Python; enabling complex workflows to handle any data type without dealing directly with the streaming back-end.Els fluxos de treball de Data Science s’han convertit en una necessitat per progressar en moltes àrees científiques com les ciències de la vida, la salut i la terra. A diferència dels fluxos de treball tradicionals per a la CAP, els fluxos de Data Science són més heterogenis; combinant l’execució de binaris, simulacions MPI, aplicacions multiprocés, anàlisi personalitzats (possiblement escrits en Java, Python, C / C ++ o R) i computacions en temps real. Mentre que en el passat els experts de cada camp eren capaços de programar i executar petites simulacions, avui dia, aquestes simulacions representen un repte fins i tot per als experts ja que requereixen centenars o milers de nuclis. Per aquesta raó, els llenguatges i models de programació actuals s’esforcen considerablement en incrementar la programabilitat mantenint un rendiment acceptable. Aquesta tesi contribueix a l’adaptació de models de programació per a la CAP per afrontar les necessitats i reptes dels fluxos de Data Science estenent COMPSs, un model de programació distribuïda madur, de propòsit general, i basat en tasques. En primer lloc, millorem el nostre prototip per orquestrar diferent programari per a que els usuaris no experts puguin crear fluxos complexos usant un únic model on alguns passos requereixin tecnologies altament optimitzades. Aquesta extensió inclou les anotacions de @binary, @OmpSs, @MPI, @COMPSs, i @MultiNode per a fluxos en Java i Python. En segon lloc, integrem tecnologies de contenidors per permetre als desenvolupadors portar, distribuir i escalar fàcilment les seves aplicacions en plataformes distribuïdes. A més d’una metodologia senzilla per a paral·lelitzar aplicacions a partir de codis seqüencials, aquesta combinació proporciona una gestió d’imatges i una implementació d’aplicacions eficients que faciliten l’empaquetat i la distribució d’aplicacions. Distingim entre la gestió de contenidors estàtica, CAP i dinàmica i proporcionem casos d’ús representatius per a cada escenari amb Docker, Singularity i Mesos. En tercer lloc, dissenyem, implementem i integrem AutoParallel, un mòdul de Python per determinar automàticament la paral·lelització basada en tasques de nius de bucles afins i executar-los en paral·lel en una infraestructura distribuïda. AutoParallel està basat en programació seqüencial, requereix una sola anotació (el decorador @parallel) i permet a un usuari intermig escalar una aplicació a centenars de nuclis. Finalment, proposem una forma d’estendre els sistemes basats en tasques per admetre dades d’entrada i sortida continus; permetent així la combinació de fluxos de treball i dades (Fluxos Híbrids) en un únic model. Conseqüentment, els desenvolupadors poden crear fluxos complexos seguint diferents patrons sense l’esforç de combinar diversos models al mateix temps. A més, per a il·lustrar les capacitats dels Fluxos Híbrids, hem creat una biblioteca (DistroStreamLib) que s’integra fàcilment amb els models basats en tasques per suportar fluxos de dades. La biblioteca proporciona una representació homogènia, genèrica i simple de seqüències contínues d’objectes i arxius en Java i Python; permetent gestionar qualsevol tipus de dades sense tractar directament amb el back-end de streaming.Los flujos de trabajo de Data Science se han convertido en una necesidad para progresar en muchas áreas científicas como las ciencias de la vida, la salud y la tierra. A diferencia de los flujos de trabajo tradicionales para la CAP, los flujos de Data Science son más heterogéneos; combinando la ejecución de binarios, simulaciones MPI, aplicaciones multiproceso, análisis personalizados (posiblemente escritos en Java, Python, C/C++ o R) y computaciones en tiempo real. Mientras que en el pasado los expertos de cada campo eran capaces de programar y ejecutar pequeñas simulaciones, hoy en día, estas simulaciones representan un desafío incluso para los expertos ya que requieren cientos o miles de núcleos. Por esta razón, los lenguajes y modelos de programación actuales se esfuerzan considerablemente en incrementar la programabilidad manteniendo un rendimiento aceptable. Esta tesis contribuye a la adaptación de modelos de programación para la CAP para afrontar las necesidades y desafíos de los flujos de Data Science extendiendo COMPSs, un modelo de programación distribuida maduro, de propósito general, y basado en tareas. En primer lugar, mejoramos nuestro prototipo para orquestar diferentes software para que los usuarios no expertos puedan crear flujos complejos usando un único modelo donde algunos pasos requieran tecnologías altamente optimizadas. Esta extensión incluye las anotaciones de @binary, @OmpSs, @MPI, @COMPSs, y @MultiNode para flujos en Java y Python. En segundo lugar, integramos tecnologías de contenedores para permitir a los desarrolladores portar, distribuir y escalar fácilmente sus aplicaciones en plataformas distribuidas. Además de una metodología sencilla para paralelizar aplicaciones a partir de códigos secuenciales, esta combinación proporciona una gestión de imágenes y una implementación de aplicaciones eficientes que facilitan el empaquetado y la distribución de aplicaciones. Distinguimos entre gestión de contenedores estática, CAP y dinámica y proporcionamos casos de uso representativos para cada escenario con Docker, Singularity y Mesos. En tercer lugar, diseñamos, implementamos e integramos AutoParallel, un módulo de Python para determinar automáticamente la paralelización basada en tareas de nidos de bucles afines y ejecutarlos en paralelo en una infraestructura distribuida. AutoParallel está basado en programación secuencial, requiere una sola anotación (el decorador @parallel) y permite a un usuario intermedio escalar una aplicación a cientos de núcleos. Finalmente, proponemos una forma de extender los sistemas basados en tareas para admitir datos de entrada y salida continuos; permitiendo así la combinación de flujos de trabajo y datos (Flujos Híbridos) en un único modelo. Consecuentemente, los desarrolladores pueden crear flujos complejos siguiendo diferentes patrones sin el esfuerzo de combinar varios modelos al mismo tiempo. Además, para ilustrar las capacidades de los Flujos Híbridos, hemos creado una biblioteca (DistroStreamLib) que se integra fácilmente a los modelos basados en tareas para soportar flujos de datos. La biblioteca proporciona una representación homogénea, genérica y simple de secuencias continuas de objetos y archivos en Java y Python; permitiendo manejar cualquier tipo de datos sin tratar directamente con el back-end de streaming

Programming models to support data science workflows

Data Science workflows have become a must to progress in many scientific areas such as life, health, and earth sciences. In contrast to traditional HPC workflows, they are more heterogeneous; combining binary executions, MPI simulations, multi-threaded applications, custom analysis (possibly written in Java, Python, C/C++ or R), and real-time processing. Furthermore, in the past, field experts were capable of programming and running small simulations. However, nowadays, simulations requiring hundreds or thousands of cores are widely used and, to this point, efficiently programming them becomes a challenge even for computer sciences. Thus, programming languages and models make a considerable effort to ease the programmability while maintaining acceptable performance. This thesis contributes to the adaptation of High-Performance frameworks to support the needs and challenges of Data Science workflows by extending COMPSs, a mature, general-purpose, task-based, distributed programming model. First, we enhance our prototype to orchestrate different frameworks inside a single programming model so that non-expert users can build complex workflows where some steps require highly optimised state of the art frameworks. This extension includes the @binary, @OmpSs, @MPI, @COMPSs, and @MultiNode annotations for both Java and Python workflows. Second, we integrate container technologies to enable developers to easily port, distribute, and scale their applications to distributed computing platforms. This combination provides a straightforward methodology to parallelise applications from sequential codes along with efficient image management and application deployment that ease the packaging and distribution of applications. We distinguish between static, HPC, and dynamic container management and provide representative use cases for each scenario using Docker, Singularity, and Mesos. Third, we design, implement and integrate AutoParallel, a Python module to automatically find an appropriate task-based parallelisation of affine loop nests and execute them in parallel in a distributed computing infrastructure. It is based on sequential programming and requires one single annotation (the @parallel Python decorator) so that anyone with intermediate-level programming skills can scale up an application to hundreds of cores. Finally, we propose a way to extend task-based management systems to support continuous input and output data to enable the combination of task-based workflows and dataflows (Hybrid Workflows) using one single programming model. Hence, developers can build complex Data Science workflows with different approaches depending on the requirements without the effort of combining several frameworks at the same time. Also, to illustrate the capabilities of Hybrid Workflows, we have built a Distributed Stream Library that can be easily integrated with existing task-based frameworks to provide support for dataflows. The library provides a homogeneous, generic, and simple representation of object and file streams in both Java and Python; enabling complex workflows to handle any data type without dealing directly with the streaming back-end.Els fluxos de treball de Data Science s’han convertit en una necessitat per progressar en moltes àrees científiques com les ciències de la vida, la salut i la terra. A diferència dels fluxos de treball tradicionals per a la CAP, els fluxos de Data Science són més heterogenis; combinant l’execució de binaris, simulacions MPI, aplicacions multiprocés, anàlisi personalitzats (possiblement escrits en Java, Python, C / C ++ o R) i computacions en temps real. Mentre que en el passat els experts de cada camp eren capaços de programar i executar petites simulacions, avui dia, aquestes simulacions representen un repte fins i tot per als experts ja que requereixen centenars o milers de nuclis. Per aquesta raó, els llenguatges i models de programació actuals s’esforcen considerablement en incrementar la programabilitat mantenint un rendiment acceptable. Aquesta tesi contribueix a l’adaptació de models de programació per a la CAP per afrontar les necessitats i reptes dels fluxos de Data Science estenent COMPSs, un model de programació distribuïda madur, de propòsit general, i basat en tasques. En primer lloc, millorem el nostre prototip per orquestrar diferent programari per a que els usuaris no experts puguin crear fluxos complexos usant un únic model on alguns passos requereixin tecnologies altament optimitzades. Aquesta extensió inclou les anotacions de @binary, @OmpSs, @MPI, @COMPSs, i @MultiNode per a fluxos en Java i Python. En segon lloc, integrem tecnologies de contenidors per permetre als desenvolupadors portar, distribuir i escalar fàcilment les seves aplicacions en plataformes distribuïdes. A més d’una metodologia senzilla per a paral·lelitzar aplicacions a partir de codis seqüencials, aquesta combinació proporciona una gestió d’imatges i una implementació d’aplicacions eficients que faciliten l’empaquetat i la distribució d’aplicacions. Distingim entre la gestió de contenidors estàtica, CAP i dinàmica i proporcionem casos d’ús representatius per a cada escenari amb Docker, Singularity i Mesos. En tercer lloc, dissenyem, implementem i integrem AutoParallel, un mòdul de Python per determinar automàticament la paral·lelització basada en tasques de nius de bucles afins i executar-los en paral·lel en una infraestructura distribuïda. AutoParallel està basat en programació seqüencial, requereix una sola anotació (el decorador @parallel) i permet a un usuari intermig escalar una aplicació a centenars de nuclis. Finalment, proposem una forma d’estendre els sistemes basats en tasques per admetre dades d’entrada i sortida continus; permetent així la combinació de fluxos de treball i dades (Fluxos Híbrids) en un únic model. Conseqüentment, els desenvolupadors poden crear fluxos complexos seguint diferents patrons sense l’esforç de combinar diversos models al mateix temps. A més, per a il·lustrar les capacitats dels Fluxos Híbrids, hem creat una biblioteca (DistroStreamLib) que s’integra fàcilment amb els models basats en tasques per suportar fluxos de dades. La biblioteca proporciona una representació homogènia, genèrica i simple de seqüències contínues d’objectes i arxius en Java i Python; permetent gestionar qualsevol tipus de dades sense tractar directament amb el back-end de streaming.Los flujos de trabajo de Data Science se han convertido en una necesidad para progresar en muchas áreas científicas como las ciencias de la vida, la salud y la tierra. A diferencia de los flujos de trabajo tradicionales para la CAP, los flujos de Data Science son más heterogéneos; combinando la ejecución de binarios, simulaciones MPI, aplicaciones multiproceso, análisis personalizados (posiblemente escritos en Java, Python, C/C++ o R) y computaciones en tiempo real. Mientras que en el pasado los expertos de cada campo eran capaces de programar y ejecutar pequeñas simulaciones, hoy en día, estas simulaciones representan un desafío incluso para los expertos ya que requieren cientos o miles de núcleos. Por esta razón, los lenguajes y modelos de programación actuales se esfuerzan considerablemente en incrementar la programabilidad manteniendo un rendimiento aceptable. Esta tesis contribuye a la adaptación de modelos de programación para la CAP para afrontar las necesidades y desafíos de los flujos de Data Science extendiendo COMPSs, un modelo de programación distribuida maduro, de propósito general, y basado en tareas. En primer lugar, mejoramos nuestro prototipo para orquestar diferentes software para que los usuarios no expertos puedan crear flujos complejos usando un único modelo donde algunos pasos requieran tecnologías altamente optimizadas. Esta extensión incluye las anotaciones de @binary, @OmpSs, @MPI, @COMPSs, y @MultiNode para flujos en Java y Python. En segundo lugar, integramos tecnologías de contenedores para permitir a los desarrolladores portar, distribuir y escalar fácilmente sus aplicaciones en plataformas distribuidas. Además de una metodología sencilla para paralelizar aplicaciones a partir de códigos secuenciales, esta combinación proporciona una gestión de imágenes y una implementación de aplicaciones eficientes que facilitan el empaquetado y la distribución de aplicaciones. Distinguimos entre gestión de contenedores estática, CAP y dinámica y proporcionamos casos de uso representativos para cada escenario con Docker, Singularity y Mesos. En tercer lugar, diseñamos, implementamos e integramos AutoParallel, un módulo de Python para determinar automáticamente la paralelización basada en tareas de nidos de bucles afines y ejecutarlos en paralelo en una infraestructura distribuida. AutoParallel está basado en programación secuencial, requiere una sola anotación (el decorador @parallel) y permite a un usuario intermedio escalar una aplicación a cientos de núcleos. Finalmente, proponemos una forma de extender los sistemas basados en tareas para admitir datos de entrada y salida continuos; permitiendo así la combinación de flujos de trabajo y datos (Flujos Híbridos) en un único modelo. Consecuentemente, los desarrolladores pueden crear flujos complejos siguiendo diferentes patrones sin el esfuerzo de combinar varios modelos al mismo tiempo. Además, para ilustrar las capacidades de los Flujos Híbridos, hemos creado una biblioteca (DistroStreamLib) que se integra fácilmente a los modelos basados en tareas para soportar flujos de datos. La biblioteca proporciona una representación homogénea, genérica y simple de secuencias continuas de objetos y archivos en Java y Python; permitiendo manejar cualquier tipo de datos sin tratar directamente con el back-end de streaming.Postprint (published version