A DevOps approach to integration of software components in an EU research project

We present a description of the development and deployment infrastructure being created to support the integration effort of HARNESS, an EU FP7 project. HARNESS is a multi-partner research project intended to bring the power of heterogeneous resources to the cloud. It consists of a number of different services and technologies that interact with the OpenStack cloud computing platform at various levels. Many of these components are being developed independently by different teams at different locations across Europe, and keeping the work fully integrated is a challenge. We use a combination of Vagrant based virtual machines, Docker containers, and Ansible playbooks to provide a consistent and up-to-date environment to each developer. The same playbooks used to configure local virtual machines are also used to manage a static testbed with heterogeneous compute and storage devices, and to automate ephemeral larger-scale deployments to Grid5000. Access to internal projects is managed by GitLab, and automated testing of services within Docker-based environments and integrated deployments within virtual-machines is provided by Buildbot

StreamFlow: cross-breeding cloud with HPC

Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single-cell transcriptomic data analysis workflow.Comment: 30 pages - 2020 IEEE Transactions on Emerging Topics in Computin

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

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

Model-Based Design for High-Performance Signal Processing Applications

Developing high-performance signal processing applications requires not only effective signal processing algorithms but also efficient software design methods that can take full advantage of the available processing resources. An increasingly important type of hardware platform for high-performance signal processing is a multicore central processing unit (CPU) combined with a graphics processing unit (GPU) accelerator. Efficiently coordinating computations on both the host (CPU) and device (GPU), and managing host-device data transfers are critical to utilizing CPU-GPU platforms effectively. However, such coordination is challenging for system designers, given the complexity of modern signal processing applications and the stringent constraints under which they must operate. Dataflow models of computation provide a useful framework for addressing this challenge. In such a modeling approach, signal processing applications are represented as directed graphs that can be viewed intuitively as high-level signal flow diagrams. The formal, high-level abstraction provided by dataflow principles provides a useful foundation to investigate model-based analysis and optimization for new challenges in design and implementation of signal processing systems. This thesis presents a new model-based design methodology and an evolution of three novel design tools. These contributions provide an automated design flow for high performance signal processing. The design flow takes high-level dataflow representations as input and systematically derives optimized implementations on CPU-GPU platforms. The proposed design flow and associated design methodology are inspired by a previously-developed application programming interface (API) called the Hybrid Task Graph Scheduler (HTGS). HTGS was developed for implementing scalable workflows for high-performance computing applications on compute nodes that have large numbers of processing cores, and that may be equipped with multiple GPUs. However, HTGS has a limitation due to its relatively loose use of dataflow techniques (or other forms of model-based design), which results in a significant designer effort being required to apply the provided APIs effectively. The main contributions of the thesis are summarized as follows: (1) Development of a companion tool to HTGS that is called the HTGS Model-based Engine (HMBE). HMBE introduces novel capabilities to automatically analyze application dataflow graphs and generate efficient schedules for these graphs through hybrid compile-time and runtime analysis. The systematic, model-based approaches provided by HMBE enable the automation of complex tasks that must be performed manually when using HTGS alone. We have demonstrated the effectiveness of HMBE and the associated model-based design methodology through extensive experiments involving two case studies: an image stitching application for large scale microscopy images, and a background subtraction application for multispectral video streams. (2) Integration of HMBE with HTGS to develop a new design tool for the design and implementation of high-performance signal processing systems. This tool, called HMBE-Integrated-HTGS (HI-HTGS), provides novel capabilities for model-based system design, memory management, and scheduling targeted to multicore platforms. HMBE takes as input a single- or multi-dimensional dataflow model of the given signal processing application. The tool then expands the dataflow model into an expanded representation that exposes more parallelism and provides significantly more detail on the interactions between different application tasks (dataflow actors). This expanded representation is derived by HI-HTGS at compile-time and provided as input to the HI-HTGS runtime system. The runtime system in turn applies the expanded representation to guide dynamic scheduling decisions throughout system execution. (3) Extension of HMBE to the class of CPU-GPU platforms motivated above. We call this new model-based design tool the CPU-GPU Model-Based Engine (CGMBE). CGMBE uses an unfolded dataflow graph representation of the application along with thread-pool-based executors, which are optimized for efficient operation on the targeted CPU-GPU platform. This approach automates complex aspects of the design and implementation process for signal processing system designers while maximizing the utilization of computational power, reducing the memory footprint for both the CPU and GPU, and facilitating experimentation for tuning performance-oriented designs

Workflow models for heterogeneous distributed systems

The role of data in modern scientific workflows becomes more and more crucial. The unprecedented amount of data available in the digital era, combined with the recent advancements in Machine Learning and High-Performance Computing (HPC), let computers surpass human performances in a wide range of fields, such as Computer Vision, Natural Language Processing and Bioinformatics. However, a solid data management strategy becomes crucial for key aspects like performance optimisation, privacy preservation and security. Most modern programming paradigms for Big Data analysis adhere to the principle of data locality: moving computation closer to the data to remove transfer-related overheads and risks. Still, there are scenarios in which it is worth, or even unavoidable, to transfer data between different steps of a complex workflow. The contribution of this dissertation is twofold. First, it defines a novel methodology for distributed modular applications, allowing topology-aware scheduling and data management while separating business logic, data dependencies, parallel patterns and execution environments. In addition, it introduces computational notebooks as a high-level and user-friendly interface to this new kind of workflow, aiming to flatten the learning curve and improve the adoption of such methodology. Each of these contributions is accompanied by a full-fledged, Open Source implementation, which has been used for evaluation purposes and allows the interested reader to experience the related methodology first-hand. The validity of the proposed approaches has been demonstrated on a total of five real scientific applications in the domains of Deep Learning, Bioinformatics and Molecular Dynamics Simulation, executing them on large-scale mixed cloud-High-Performance Computing (HPC) infrastructures

Doctor of Philosophy

dissertationDataflow pipeline models are widely used in visualization systems. Despite recent advancements in parallel architecture, most systems still support only a single CPU or a small collection of CPUs such as a SMP workstation. Even for systems that are specifically tuned towards parallel visualization, their execution models only provide support for data-parallelism while ignoring taskparallelism and pipeline-parallelism. With the recent popularization of machines equipped with multicore CPUs and multi-GPU units, these visualization systems are undoubtedly falling further behind in reaching maximum efficiency. On the other hand, there exist several libraries that can schedule program executions on multiple CPUs and/or multiple GPUs. However, due to differences in executing a task graph and a pipeline along with their APIs being considerably low-level, it still remains a challenge to integrate these run-time libraries into current visualization systems. Thus, there is a need for a redesigned dataflow architecture to fully support and exploit the power of highly parallel machines in large-scale visualization. The new design must be able to schedule executions on heterogeneous platforms while at the same time supporting arbitrarily large datasets through the use of streaming data structures. The primary goal of this dissertation work is to develop a parallel dataflow architecture for streaming large-scale visualizations. The framework includes supports for platforms ranging from multicore processors to clusters consisting of thousands CPUs and GPUs. We achieve this in our system by introducing the notion of Virtual Processing Elements and Task-Oriented Modules along with a highly customizable scheduler that controls the assignment of tasks to elements dynamically. This creates an intuitive way to maintain multiple CPU/GPU kernels yet still provide coherency and synchronization across module executions. We have implemented these techniques into HyperFlow which is made of an API with all basic dataflow constructs described in the dissertation, and a distributed run-time library that can be used to deploy those pipelines on multicore, multi-GPU and cluster-based platforms

On the construction of decentralised service-oriented orchestration systems

Modern science relies on workflow technology to capture, process, and analyse data obtained from scientific instruments. Scientific workflows are precise descriptions of experiments in which multiple computational tasks are coordinated based on the dataflows between them. Orchestrating scientific workflows presents a significant research challenge: they are typically executed in a manner such that all data pass through a centralised computer server known as the engine, which causes unnecessary network traffic that leads to a performance bottleneck. These workflows are commonly composed of services that perform computation over geographically distributed resources, and involve the management of dataflows between them. Centralised orchestration is clearly not a scalable approach for coordinating services dispersed across distant geographical locations. This thesis presents a scalable decentralised service-oriented orchestration system that relies on a high-level data coordination language for the specification and execution of workflows. This system’s architecture consists of distributed engines, each of which is responsible for executing part of the overall workflow. It exploits parallelism in the workflow by decomposing it into smaller sub-workflows, and determines the most appropriate engines to execute them using computation placement analysis. This permits the workflow logic to be distributed closer to the services providing the data for execution, which reduces the overall data transfer in the workflow and improves its execution time. This thesis provides an evaluation of the presented system which concludes that decentralised orchestration provides scalability benefits over centralised orchestration, and improves the overall performance of executing a service-oriented workflow

Improving Usability And Scalability Of Big Data Workflows In The Cloud

Big data workflows have recently emerged as the next generation of data-centric workflow technologies to address the five “V” challenges of big data: volume, variety, velocity, veracity, and value. More formally, a big data workflow is the computerized modeling and automation of a process consisting of a set of computational tasks and their data interdependencies to process and analyze data of ever increasing in scale, complexity, and rate of acquisition. The convergence of big data and workflows creates new challenges in workflow community. First, the variety of big data results in a need for integrating large number of remote Web services and other heterogeneous task components that can consume and produce data in various formats and models into a uniform and interoperable workflow. Existing approaches fall short in addressing the so-called shimming problem only in an adhoc manner and unable to provide a generic solution. We automatically insert a piece of code called shims or adaptors in order to resolve the data type mismatches. Second, the volume of big data results in a large number of datasets that needs to be queried and analyzed in an effective and personalized manner. Further, there is also a strong need for sharing, reusing, and repurposing existing tasks and workflows across different users and institutes. To overcome such limitations, we propose a folksonomy- based social workflow recommendation system to improve workflow design productivity and efficient dataset querying and analyzing. Third, the volume of big data results in the need to process and analyze data of ever increasing in scale, complexity, and rate of acquisition. But a scalable distributed data model is still missing that abstracts and automates data distribution, parallelism, and scalable processing. We propose a NoSQL collectional data model that addresses this limitation. Finally, the volume of big data combined with the unbound resource leasing capability foreseen in the cloud, facilitates data scientists to wring actionable insights from the data in a time and cost efficient manner. We propose BARENTS scheduler that supports high-performance workflow scheduling in a heterogeneous cloud-computing environment with a single objective to minimize the workflow makespan under a user provided budget constraint