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

A Framework for Grid-Enabling Scientific Workflow Systems. Architecture and application case studies on interoperability and heterogeneity in support for Grid workflow automation.

Since the early 2000s, Service Oriented Architectures (SOAs) have played a key role in the development of complex applications within a virtual organization (VO) context. Grids and workflows have emerged as vital technologies for addressing the (SOA) paradigm. Given the variety of Grid middleware, scientific workflow systems and Grid workflows available, bringing the two technologies together in a flexible, reusable and generalized way has been largely overlooked, particularly from a scientific end user perspective. The lack of domain focus in this area has led to a slow uptake of Grid technologies. This thesis aims to design a framework for Grid-enabling workflows, which identifies the essential technological components, how these components fit together in layered architecture and the interactions between them. To produce such a framework, this thesis first investigates the definition of a Grid-workflow architecture and mapping Grid functionality to workflow nodes, focusing on striking a balance between performance, usability and the Grid functionality supported. Next, it presents an examination of framework extensions for supporting various forms of Grid heterogeneity, essential for ii VO based collaboration. Given the complex nature of Grid technologies, the work presented here investigates abstracting Grid based workflows through high-level definitions and resolution using semantic technologies. Finally, this thesis presents a way to resolves abstract Grid workflows using semantic technologies and intelligent, autonomous agents. The frameworks presented in this thesis are tested and evaluated within the context of domain-based case studies defined in the SIMDAT, BRIDGE and ARGUGRID EU funded research projects

Big Data Management Using Scientific Workflows

Humanity is rapidly approaching a new era, where every sphere of activity will be informed by the ever-increasing amount of data. Making use of big data has the potential to improve numerous avenues of human activity, including scientific research, healthcare, energy, education, transportation, environmental science, and urban planning, just to name a few. However, making such progress requires managing terabytes and even petabytes of data, generated by billions of devices, products, and events, often in real time, in different protocols, formats and types. The volume, velocity, and variety of big data, known as the 3 Vs , present formidable challenges, unmet by the traditional data management approaches. Traditionally, many data analyses have been performed using scientific workflows, tools for formalizing and structuring complex computational processes. While scientific workflows have been used extensively in structuring complex scientific data analysis processes, little work has been done to enable scientific workflows to cope with the three big data challenges on the one hand, and to leverage the dynamic resource provisioning capability of cloud computing to analyze big data on the other hand. In this dissertation, to facilitate efficient composition, verification, and execution of distributed large-scale scientific workflows, we first propose a formal approach to scientific workflow verification, including a workflow model, and the notion of a well-typed workflow. Our approach translates a scientific workflow into an equivalent typed lambda expression, and typechecks the workflow. We then propose a typetheoretic approach to the shimming problem in scientific workflows, which occurs when connecting related but incompatible components. We reduce the shimming problem to a runtime coercion problem in the theory of type systems, and propose a fully automated and transparent solution. Our technique algorithmically inserts invisible shims into the workflow specification, thereby resolving the shimming problem for any well-typed workflow. Next, we identify a set of important challenges for running big data workflows in the cloud. We then propose a generic, implementation-independent system architecture that addresses many of these challenges. Finally, we develop a cloud-enabled big data workflow management system, called DATAVIEW, that delivers a specific implementation of our proposed architecture. To further validate our proposed architecture, we conduct a case study in which we design and run a big data workflow from the automotive domain using the Amazon EC2 cloud environment

AVENTIS - An architecture for event data analysis

Time-stamped event data is being generated at an exponential rate from various sources (sensor networks, e-markets etc.), which are stored in event logs and made available to researchers. Despite the data deluge and evolution of a plethora of tools and technologies, science behind exploratory analysis and knowledge discovery lags. There are several reasons behind this. In conducting event data analysis, researchers typically detect a pattern or trend in the data through computation of time-series measures and apply the computed measures to several mathematical models to glean information from data. This is a complex and time-consuming process covering a range of activities from data capture (from a broad array of data sources) to interpretation and dissemination of experimental results forming a pipeline of activities. Further, data-analysis is conducted by domain-users, who are typically non-IT experts but data processing tools and applications are largely developed by application developers. End-users not only lack the critical skills to build a structured analysis pipeline, but are also perplexed by the number of different ways available to derive the necessary information. Consequently, this thesis proposes AVENTIS (Architecture for eVENT Data analysIS), a novel framework to guide the design of analytic solutions to facilitate time-series analysis of event data and is tailored to the needs of domain users. The framework comprises three components; a knowledge base, a model-driven analytic methodology and an accompanying software architecture that provides the necessary technical and operational requirements. Specifically, the research contribution lies in the ability of the framework to enable expressing analysis requirements at a level of abstraction consistent with the domain users and readily make available the information sought without the users having to build the analysis process themselves. Secondly, the framework also facilitates an abstract design space for the domain experts to enable them to build conceptual models of their experiment as a sequence of structured tasks in a technology neutral manner and transparently translate these abstract process models to executable implementations. To evaluate the AVENTIS framework, a prototype based on AVENTIS is implemented and tested with case studies taken from the financial research domain

A Process Model for the Integrated Reasoning about Quantitative IT Infrastructure Attributes

IT infrastructures can be quantitatively described by attributes, like performance or energy efficiency. Ever-changing user demands and economic attempts require varying short-term and long-term decisions regarding the alignment of an IT infrastructure and particularly its attributes to this dynamic surrounding. Potentially conflicting attribute goals and the central role of IT infrastructures presuppose decision making based upon reasoning, the process of forming inferences from facts or premises. The focus on specific IT infrastructure parts or a fixed (small) attribute set disqualify existing reasoning approaches for this intent, as they neither cover the (complex) interplay of all IT infrastructure components simultaneously, nor do they address inter- and intra-attribute correlations sufficiently. This thesis presents a process model for the integrated reasoning about quantitative IT infrastructure attributes. The process model’s main idea is to formalize the compilation of an individual reasoning function, a mathematical mapping of parametric influencing factors and modifications on an attribute vector. Compilation bases upon model integration to benefit from the multitude of existing specialized, elaborated, and well-established attribute models. The achieved reasoning function consumes an individual tuple of IT infrastructure components, attributes, and external influencing factors to expose a broad applicability. The process model formalizes a reasoning intent in three phases. First, reasoning goals and parameters are collected in a reasoning suite, and formalized in a reasoning function skeleton. Second, the skeleton is iteratively refined, guided by the reasoning suite. Third, the achieved reasoning function is employed for What-if analyses, optimization, or descriptive statistics to conduct the concrete reasoning. The process model provides five template classes that collectively formalize all phases in order to foster reproducibility and to reduce error-proneness. Process model validation is threefold. A controlled experiment reasons about a Raspberry Pi cluster’s performance and energy efficiency to illustrate feasibility. Besides, a requirements analysis on a world-class supercomputer and on the European-wide execution of hydro meteorology simulations as well as a related work examination disclose the process model’s level of innovation. Potential future work employs prepared automation capabilities, integrates human factors, and uses reasoning results for the automatic generation of modification recommendations.IT-Infrastrukturen können mit Attributen, wie Leistung und Energieeffizienz, quantitativ beschrieben werden. Nutzungsbedarfsänderungen und ökonomische Bestrebungen erfordern Kurz- und Langfristentscheidungen zur Anpassung einer IT-Infrastruktur und insbesondere ihre Attribute an dieses dynamische Umfeld. Potentielle Attribut-Zielkonflikte sowie die zentrale Rolle von IT-Infrastrukturen erfordern eine Entscheidungsfindung mittels Reasoning, einem Prozess, der Rückschlüsse (rein) aus Fakten und Prämissen zieht. Die Fokussierung auf spezifische Teile einer IT-Infrastruktur sowie die Beschränkung auf (sehr) wenige Attribute disqualifizieren bestehende Reasoning-Ansätze für dieses Vorhaben, da sie weder das komplexe Zusammenspiel von IT-Infrastruktur-Komponenten, noch Abhängigkeiten zwischen und innerhalb einzelner Attribute ausreichend berücksichtigen können. Diese Arbeit präsentiert ein Prozessmodell für das integrierte Reasoning über quantitative IT-Infrastruktur-Attribute. Die grundlegende Idee des Prozessmodells ist die Herleitung einer individuellen Reasoning-Funktion, einer mathematischen Abbildung von Einfluss- und Modifikationsparametern auf einen Attributvektor. Die Herleitung basiert auf der Integration bestehender (Attribut-)Modelle, um von deren Spezialisierung, Reife und Verbreitung profitieren zu können. Die erzielte Reasoning-Funktion verarbeitet ein individuelles Tupel aus IT-Infrastruktur-Komponenten, Attributen und externen Einflussfaktoren, um eine breite Anwendbarkeit zu gewährleisten. Das Prozessmodell formalisiert ein Reasoning-Vorhaben in drei Phasen. Zunächst werden die Reasoning-Ziele und -Parameter in einer Reasoning-Suite gesammelt und in einem Reasoning-Funktions-Gerüst formalisiert. Anschließend wird das Gerüst entsprechend den Vorgaben der Reasoning-Suite iterativ verfeinert. Abschließend wird die hergeleitete Reasoning-Funktion verwendet, um mittels “What-if”–Analysen, Optimierungsverfahren oder deskriptiver Statistik das Reasoning durchzuführen. Das Prozessmodell enthält fünf Template-Klassen, die den Prozess formalisieren, um Reproduzierbarkeit zu gewährleisten und Fehleranfälligkeit zu reduzieren. Das Prozessmodell wird auf drei Arten validiert. Ein kontrolliertes Experiment zeigt die Durchführbarkeit des Prozessmodells anhand des Reasonings zur Leistung und Energieeffizienz eines Raspberry Pi Clusters. Eine Anforderungsanalyse an einem Superrechner und an der europaweiten Ausführung von Hydro-Meteorologie-Modellen erläutert gemeinsam mit der Betrachtung verwandter Arbeiten den Innovationsgrad des Prozessmodells. Potentielle Erweiterungen nutzen die vorbereiteten Automatisierungsansätze, integrieren menschliche Faktoren, und generieren Modifikationsempfehlungen basierend auf Reasoning-Ergebnissen

A scientific workflow framework for scientific data querying and processing

We are at the beginning of the new era of ``e-science\u27\u27. Researchers in many areas of science, especially in astrophysics, physics, climatology and biology, are now facing tremendous increases in data volumes, as well as corresponding data analysis tools. These increased data and tools demand a better framework to manage the new generation scientific research cycle from data capture, data curation to data analysis, data query and data visualization. Scientific workflows are proving to be one of the key technologies for scientists to formalize and structure complex scientific processes to enable and accelerate many significant scientific discoveries. Although several scientific workflow management systems (SWFMSs) are developed, a formal scientific workflow composition framework, in which workflows and constructs can be composed arbitrarily to process and query collectional scientific data sets, is still to be proposed. In this thesis, I make several contributions towards formalizing a scientific workflow composition framework. First, We proposed a dataflow-based scientific workflow composition model including a scientific workflow model that separates the declaration of the workflow interface from the definition of its functional body; and a set of workflow constructs, including Map, Reduce, Tree, Loop, Conditional, and Curry, which are fully compositional one with another. Our workflow composition framework is unique in that workflows are the only operands for composition; in this way, our approach elegantly solves the two-world problem in existing composition frameworks, in which composition needs to deal with both the world of tasks and the world of workflows. Second, We formalized a collection-oriented data model, called collectional data model, to model hierarchical collection-oriented scientific data, and a set of well-defined operators to manipulate and query such data. To our best knowledge, this is the first algebraic approach to modeling collection-oriented scientific data. Finally, we developed a prototype scientific workflow management system, called View. The View system implemented the above techniques in its subsystems and integrated them within a service-oriented architecture

A Process Model for the Integrated Reasoning about Quantitative IT Infrastructure Attributes

IT infrastructures can be quantitatively described by attributes, like performance or energy efficiency. Ever-changing user demands and economic attempts require varying short-term and long-term decisions regarding the alignment of an IT infrastructure and particularly its attributes to this dynamic surrounding. Potentially conflicting attribute goals and the central role of IT infrastructures presuppose decision making based upon reasoning, the process of forming inferences from facts or premises. The focus on specific IT infrastructure parts or a fixed (small) attribute set disqualify existing reasoning approaches for this intent, as they neither cover the (complex) interplay of all IT infrastructure components simultaneously, nor do they address inter- and intra-attribute correlations sufficiently. This thesis presents a process model for the integrated reasoning about quantitative IT infrastructure attributes. The process model’s main idea is to formalize the compilation of an individual reasoning function, a mathematical mapping of parametric influencing factors and modifications on an attribute vector. Compilation bases upon model integration to benefit from the multitude of existing specialized, elaborated, and well-established attribute models. The achieved reasoning function consumes an individual tuple of IT infrastructure components, attributes, and external influencing factors to expose a broad applicability. The process model formalizes a reasoning intent in three phases. First, reasoning goals and parameters are collected in a reasoning suite, and formalized in a reasoning function skeleton. Second, the skeleton is iteratively refined, guided by the reasoning suite. Third, the achieved reasoning function is employed for What-if analyses, optimization, or descriptive statistics to conduct the concrete reasoning. The process model provides five template classes that collectively formalize all phases in order to foster reproducibility and to reduce error-proneness. Process model validation is threefold. A controlled experiment reasons about a Raspberry Pi cluster’s performance and energy efficiency to illustrate feasibility. Besides, a requirements analysis on a world-class supercomputer and on the European-wide execution of hydro meteorology simulations as well as a related work examination disclose the process model’s level of innovation. Potential future work employs prepared automation capabilities, integrates human factors, and uses reasoning results for the automatic generation of modification recommendations.IT-Infrastrukturen können mit Attributen, wie Leistung und Energieeffizienz, quantitativ beschrieben werden. Nutzungsbedarfsänderungen und ökonomische Bestrebungen erfordern Kurz- und Langfristentscheidungen zur Anpassung einer IT-Infrastruktur und insbesondere ihre Attribute an dieses dynamische Umfeld. Potentielle Attribut-Zielkonflikte sowie die zentrale Rolle von IT-Infrastrukturen erfordern eine Entscheidungsfindung mittels Reasoning, einem Prozess, der Rückschlüsse (rein) aus Fakten und Prämissen zieht. Die Fokussierung auf spezifische Teile einer IT-Infrastruktur sowie die Beschränkung auf (sehr) wenige Attribute disqualifizieren bestehende Reasoning-Ansätze für dieses Vorhaben, da sie weder das komplexe Zusammenspiel von IT-Infrastruktur-Komponenten, noch Abhängigkeiten zwischen und innerhalb einzelner Attribute ausreichend berücksichtigen können. Diese Arbeit präsentiert ein Prozessmodell für das integrierte Reasoning über quantitative IT-Infrastruktur-Attribute. Die grundlegende Idee des Prozessmodells ist die Herleitung einer individuellen Reasoning-Funktion, einer mathematischen Abbildung von Einfluss- und Modifikationsparametern auf einen Attributvektor. Die Herleitung basiert auf der Integration bestehender (Attribut-)Modelle, um von deren Spezialisierung, Reife und Verbreitung profitieren zu können. Die erzielte Reasoning-Funktion verarbeitet ein individuelles Tupel aus IT-Infrastruktur-Komponenten, Attributen und externen Einflussfaktoren, um eine breite Anwendbarkeit zu gewährleisten. Das Prozessmodell formalisiert ein Reasoning-Vorhaben in drei Phasen. Zunächst werden die Reasoning-Ziele und -Parameter in einer Reasoning-Suite gesammelt und in einem Reasoning-Funktions-Gerüst formalisiert. Anschließend wird das Gerüst entsprechend den Vorgaben der Reasoning-Suite iterativ verfeinert. Abschließend wird die hergeleitete Reasoning-Funktion verwendet, um mittels “What-if”–Analysen, Optimierungsverfahren oder deskriptiver Statistik das Reasoning durchzuführen. Das Prozessmodell enthält fünf Template-Klassen, die den Prozess formalisieren, um Reproduzierbarkeit zu gewährleisten und Fehleranfälligkeit zu reduzieren. Das Prozessmodell wird auf drei Arten validiert. Ein kontrolliertes Experiment zeigt die Durchführbarkeit des Prozessmodells anhand des Reasonings zur Leistung und Energieeffizienz eines Raspberry Pi Clusters. Eine Anforderungsanalyse an einem Superrechner und an der europaweiten Ausführung von Hydro-Meteorologie-Modellen erläutert gemeinsam mit der Betrachtung verwandter Arbeiten den Innovationsgrad des Prozessmodells. Potentielle Erweiterungen nutzen die vorbereiteten Automatisierungsansätze, integrieren menschliche Faktoren, und generieren Modifikationsempfehlungen basierend auf Reasoning-Ergebnissen