BOSS-LDG: A Novel Computational Framework that Brings Together Blue Waters, Open Science Grid, Shifter and the LIGO Data Grid to Accelerate Gravitational Wave Discovery

We present a novel computational framework that connects Blue Waters, the NSF-supported, leadership-class supercomputer operated by NCSA, to the Laser Interferometer Gravitational-Wave Observatory (LIGO) Data Grid via Open Science Grid technology. To enable this computational infrastructure, we configured, for the first time, a LIGO Data Grid Tier-1 Center that can submit heterogeneous LIGO workflows using Open Science Grid facilities. In order to enable a seamless connection between the LIGO Data Grid and Blue Waters via Open Science Grid, we utilize Shifter to containerize LIGO's workflow software. This work represents the first time Open Science Grid, Shifter, and Blue Waters are unified to tackle a scientific problem and, in particular, it is the first time a framework of this nature is used in the context of large scale gravitational wave data analysis. This new framework has been used in the last several weeks of LIGO's second discovery campaign to run the most computationally demanding gravitational wave search workflows on Blue Waters, and accelerate discovery in the emergent field of gravitational wave astrophysics. We discuss the implications of this novel framework for a wider ecosystem of Higher Performance Computing users.Comment: 10 pages, 10 figures. Accepted as a Full Research Paper to the 13th IEEE International Conference on eScienc

Automatic deployment and reproducibility of workflow on the Cloud using container virtualization

PhD ThesisCloud computing is a service-oriented approach to distributed computing that has many attractive features, including on-demand access to large compute resources. One type of cloud applications are scientific work ows, which are playing an increasingly important role in building applications from heterogeneous components. Work ows are increasingly used in science as a means to capture, share, and publish computational analysis. Clouds can offer a number of benefits to work ow systems, including the dynamic provisioning of the resources needed for computation and storage, which has the potential to dramatically increase the ability to quickly extract new results from the huge amounts of data now being collected. However, there are increasing number of Cloud computing platforms, each with different functionality and interfaces. It therefore becomes increasingly challenging to de ne work ows in a portable way so that they can be run reliably on different clouds. As a consequence, work ow developers face the problem of deciding which Cloud to select and - more importantly for the long-term - how to avoid vendor lock-in. A further issue that has arisen with work ows is that it is common for them to stop being executable a relatively short time after they were created. This can be due to the external resources required to execute a work ow - such as data and services - becoming unavailable. It can also be caused by changes in the execution environment on which the work ow depends, such as changes to a library causing an error when a work ow service is executed. This "work ow decay" issue is recognised as an impediment to the reuse of work ows and the reproducibility of their results. It is becoming a major problem, as the reproducibility of science is increasingly dependent on the reproducibility of scientific work ows. In this thesis we presented new solutions to address these challenges. We propose a new approach to work ow modelling that offers a portable and re-usable description of the work ow using the TOSCA specification language. Our approach addresses portability by allowing work ow components to be systematically specifed and automatically - v - deployed on a range of clouds, or in local computing environments, using container virtualisation techniques. To address the issues of reproducibility and work ow decay, our modelling and deployment approach has also been integrated with source control and container management techniques to create a new framework that e ciently supports dynamic work ow deployment, (re-)execution and reproducibility. To improve deployment performance, we extend the framework with number of new optimisation techniques, and evaluate their effect on a range of real and synthetic work ows.Ministry of Higher Education and Scientific Research in Iraq and Mosul Universit

Design considerations for workflow management systems use in production genomics research and the clinic

Abstract The changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software components employed to fill this gap. This work provides an approach and systematic evaluation of key features of popular bioinformatics WfMSs in use today: Nextflow, CWL, and WDL and some of their executors, along with Swift/T, a workflow manager commonly used in high-scale physics applications. We employed two use cases: a variant-calling genomic pipeline and a scalability-testing framework, where both were run locally, on an HPC cluster, and in the cloud. This allowed for evaluation of those four WfMSs in terms of language expressiveness, modularity, scalability, robustness, reproducibility, interoperability, ease of development, along with adoption and usage in research labs and healthcare settings. This article is trying to answer, which WfMS should be chosen for a given bioinformatics application regardless of analysis type?. The choice of a given WfMS is a function of both its intrinsic language and engine features. Within bioinformatics, where analysts are a mix of dry and wet lab scientists, the choice is also governed by collaborations and adoption within large consortia and technical support provided by the WfMS team/community. As the community and its needs continue to evolve along with computational infrastructure, WfMSs will also evolve, especially those with permissive licenses that allow commercial use. In much the same way as the dataflow paradigm and containerization are now well understood to be very useful in bioinformatics applications, we will continue to see innovations of tools and utilities for other purposes, like big data technologies, interoperability, and provenance

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

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

Big data workflows: Locality-aware orchestration using software containers

The emergence of the Edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing Big Data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the Edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric Big Data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo Workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.publishedVersio

Big data workflows: Locality-aware orchestration using software containers

The emergence of the Edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing Big Data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the Edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric Big Data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo Workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.publishedVersio

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

Supporting Quality of Service in Scientific Workflows

While workflow management systems have been utilized in enterprises to support businesses for almost two decades, the use of workflows in scientific environments was fairly uncommon until recently. Nowadays, scientists use workflow systems to conduct scientific experiments, simulations, and distributed computations. However, most scientific workflow management systems have not been built using existing workflow technology; rather they have been designed and developed from scratch. Due to the lack of generality of early scientific workflow systems, many domain-specific workflow systems have been developed. Generally speaking, those domain-specific approaches lack common acceptance and tool support and offer lower robustness compared to business workflow systems. In this thesis, the use of the industry standard BPEL, a workflow language for modeling business processes, is proposed for the modeling and the execution of scientific workflows. Due to the widespread use of BPEL in enterprises, a number of stable and mature software products exist. The language is expressive (Turingcomplete) and not restricted to specific applications. BPEL is well suited for the modeling of scientific workflows, but existing implementations of the standard lack important features that are necessary for the execution of scientific workflows. This work presents components that extend an existing implementation of the BPEL standard and eliminate the identified weaknesses. The components thus provide the technical basis for use of BPEL in academia. The particular focus is on so-called non-functional (Quality of Service) requirements. These requirements include scalability, reliability (fault tolerance), data security, and cost (of executing a workflow). From a technical perspective, the workflow system must be able to interface with the middleware systems that are commonly used by the scientific workflow community to allow access to heterogeneous, distributed resources (especially Grid and Cloud resources). The major components cover exactly these requirements: Cloud Resource Provisioner Scalability of the workflow system is achieved by automatically adding additional (Cloud) resources to the workflow system’s resource pool when the workflow system is heavily loaded. Fault Tolerance Module High reliability is achieved via continuous monitoring of workflow execution and corrective interventions, such as re-execution of a failed workflow step or replacement of the faulty resource. Cost Aware Data Flow Aware Scheduler The majority of scientific workflow systems only take the performance and utilization of resources for the execution of workflow steps into account when making scheduling decisions. The presented workflow system goes beyond that. By defining preference values for the weighting of costs and the anticipated workflow execution time, workflow users may influence the resource selection process. The developed multiobjective scheduling algorithm respects the defined weighting and makes both efficient and advantageous decisions using a heuristic approach. Security Extensions Because it supports various encryption, signature and authentication mechanisms (e.g., Grid Security Infrastructure), the workflow system guarantees data security in the transfer of workflow data. Furthermore, this work identifies the need to equip workflow developers with workflow modeling tools that can be used intuitively. This dissertation presents two modeling tools that support users with different needs. The first tool, DAVO (domain-adaptable, Visual BPEL Orchestrator), operates at a low level of abstraction and allows users with knowledge of BPEL to use the full extent of the language. DAVO is a software that offers extensibility and customizability for different application domains. These features are used in the implementation of the second tool, SimpleBPEL Composer. SimpleBPEL is aimed at users with little or no background in computer science and allows for quick and intuitive development of BPEL workflows based on predefined components