UML Assisted Visual Debugging for Distributed Systems

The DOD is developing a Joint Battlespace Infosphere, linking a large number of data sources and user applications. To assist in this process, debugging and analysis tools are required. Software debugging is an extremely difficult cognitive process requiring comprehension of the overall application behavior, along with detailed understanding of specific application components. This is further complicated with distributed systems by the addition of other programs, their large size and synchronization issues. Typical debuggers provide inadequate support for this process, focusing primarily on the details accessible through source code. To overcome this deficiency, this research links the dynamic program execution state to a Unified Modeling Language (UML) class diagram that is reverse-engineered from data accessed within the Java Platform Debug Architecture. This research uses focus + context, graph layout, and color encoding techniques to enhance the standard UML diagram. These techniques organize and present objects and events in a manner that facilitates analysis of system behavior. High-level abstractions commonly used in system design support debugging while maintaining access to low-level details with an interactive display. The user is also able to monitor the control flow through highlighting of the relevant object and method in the display

Security for Service-Oriented On-Demand Grid Computing

Grid Computing ist mittlerweile zu einem etablierten Standard für das verteilte Höchstleistungsrechnen geworden. Während die erste Generation von Grid Middleware-Systemen noch mit proprietären Schnittstellen gearbeitet hat, wurde durch die Einführung von service-orientierten Standards wie WSDL und SOAP durch die Open Grid Services Architecture (OGSA) die Interoperabilität von Grids signifikant erhöht. Dies hat den Weg für mehrere nationale und internationale Grid-Projekten bereitet, in denen eine groß e Anzahl von akademischen und eine wachsende Anzahl von industriellen Anwendungen im Grid ausgeführt werden, die die bedarfsgesteuerte (on-demand) Provisionierung und Nutzung von Ressourcen erfordern. Bedarfsgesteuerte Grids zeichnen sich dadurch aus, dass sowohl die Software, als auch die Benutzer einer starken Fluktuation unterliegen. Weiterhin sind sowohl die Software, als auch die Daten, auf denen operiert wird, meist proprietär und haben einen hohen finanziellen Wert. Dies steht in starkem Kontrast zu den heutigen Grid-Anwendungen im akademischen Umfeld, die meist offen im Quellcode vorliegen bzw. frei verfügbar sind. Um den Ansprüchen einer bedarfsgesteuerten Grid-Nutzung gerecht zu werden, muss das Grid administrative Komponenten anbieten, mit denen Anwender autonom Software installieren können, selbst wenn diese Root-Rechte benötigen. Zur gleichen Zeit muss die Sicherheit des Grids erhöht werden, um Software, Daten und Meta-Daten der kommerziellen Anwender zu schützen. Dies würde es dem Grid auch erlauben als Basistechnologie für das gerade entstehende Gebiet des Cloud Computings zu dienen, wo ähnliche Anforderungen existieren. Wie es bei den meisten komplexen IT-Systemen der Fall ist, sind auch in traditionellen Grid Middlewares Schwachstellen zu finden, die durch die geforderten Erweiterungen der administrativen Möglichkeiten potentiell zu einem noch größ erem Problem werden. Die Schwachstellen in der Grid Middleware öffnen einen homogenen Angriffsvektor auf die ansonsten heterogenen und meist privaten Cluster-Umgebungen. Hinzu kommt, dass anders als bei den privaten Cluster-Umgebungen und kleinen akademischen Grid-Projekten die angestrebten groß en und offenen Grid-Landschaften die Administratoren mit gänzlich unbekannten Benutzern und Verhaltenstrukturen konfrontieren. Dies macht das Erkennen von böswilligem Verhalten um ein Vielfaches schwerer. Als Konsequenz werden Grid-Systeme ein immer attraktivere Ziele für Angreifer, da standardisierte Zugriffsmöglichkeiten Angriffe auf eine groß e Anzahl von Maschinen und Daten von potentiell hohem finanziellen Wert ermöglichen. Während die Rechenkapazität, die Bandbreite und der Speicherplatz an sich schon attraktive Ziele darstellen können, sind die im Grid enthaltene Software und die gespeicherten Daten viel kritischere Ressourcen. Modelldaten für die neuesten Crash-Test Simulationen, eine industrielle Fluid-Simulation, oder Rechnungsdaten von Kunden haben einen beträchtlichen Wert und müssen geschützt werden. Wenn ein Grid-Anbieter nicht für die Sicherheit von Software, Daten und Meta-Daten sorgen kann, wird die industrielle Verbreitung der offenen Grid-Technologie nicht stattfinden. Die Notwendigkeit von strikten Sicherheitsmechanismen muss mit der diametral entgegengesetzten Forderung nach einfacher und schneller Integration von neuer Software und neuen Kunden in Einklang gebracht werden. In dieser Arbeit werden neue Ansätze zur Verbesserung der Sicherheit und Nutzbarkeit von service-orientiertem bedarfsgesteuertem Grid Computing vorgestellt. Sie ermöglichen eine autonome und sichere Installation und Nutzung von komplexer, service-orientierter und traditioneller Software auf gemeinsam genutzen Ressourcen. Neue Sicherheitsmechanismen schützen Software, Daten und Meta-Daten der Anwender vor anderen Anwendern und vor externen Angreifern. Das System basiert auf Betriebssystemvirtualisierungstechnologien und bietet dynamische Erstellungs- und Installationsfunktionalitäten für virtuelle Images in einer sicheren Umgebung, in der automatisierte Mechanismen anwenderspezifische Firewall-Regeln setzen, um anwenderbezogene Netzwerkpartitionen zu erschaffen. Die Grid-Umgebung wird selbst in mehrere Bereiche unterteilt, damit die Kompromittierung von einzelnen Komponenten nicht so leicht zu einer Gefährdung des gesamten Systems führen kann. Die Grid-Headnode und der Image-Erzeugungsserver werden jeweils in einzelne Bereiche dieser demilitarisierten Zone positioniert. Um die sichere Anbindung von existierenden Geschäftsanwendungen zu ermöglichen, werden der BPEL-Standard (Business Process Execution Language) und eine Workflow-Ausführungseinheit um Grid-Sicherheitskonzepte erweitert. Die Erweiterung erlaubt eine nahtlose Integration von geschützten Grid Services mit existierenden Web Services. Die Workflow-Ausführungseinheit bietet die Erzeugung und die Erneuerung (im Falle von lange laufenden Anwendungen) von Proxy-Zertifikaten. Der Ansatz ermöglicht die sichere gemeinsame Ausführung von neuen, fein-granularen, service-orientierten Grid Anwendungen zusammen mit traditionellen Batch- und Job-Farming Anwendungen. Dies wird durch die Integration des vorgestellten Grid Sandboxing-Systems in existierende Cluster Scheduling Systeme erreicht. Eine innovative Server-Rotationsstrategie sorgt für weitere Sicherheit für den Grid Headnode Server, in dem transparent das virtuelle Server Image erneuert wird und damit auch unbekannte und unentdeckte Angriffe neutralisiert werden. Um die Angriffe, die nicht verhindert werden konnten, zu erkennen, wird ein neuartiges Intrusion Detection System vorgestellt, das auf Basis von Datenstrom-Datenbanksystemen funktioniert. Als letzte Neuerung dieser Arbeit wird eine Erweiterung des modellgetriebenen Softwareentwicklungsprozesses eingeführt, die eine automatisierte Generierung von sicheren Grid Services ermöglicht, um die komplexe und damit unsichere manuelle Erstellung von Grid Services zu ersetzen. Eine prototypische Implementierung der Konzepte wird auf Basis des Globus Toolkits 4, der Sun Grid Engine und der ActiveBPEL Engine vorgestellt. Die modellgetriebene Entwicklungsumgebung wurde in Eclipse für das Globus Toolkit 4 realisiert. Experimentelle Resultate und eine Evaluation der kritischen Komponenten des vorgestellten neuen Grids werden präsentiert. Die vorgestellten Sicherheitsmechanismem sollen die nächste Phase der Evolution des Grid Computing in einer sicheren Umgebung ermöglichen

Revisiting Actor Programming in C++

The actor model of computation has gained significant popularity over the last decade. Its high level of abstraction makes it appealing for concurrent applications in parallel and distributed systems. However, designing a real-world actor framework that subsumes full scalability, strong reliability, and high resource efficiency requires many conceptual and algorithmic additives to the original model. In this paper, we report on designing and building CAF, the "C++ Actor Framework". CAF targets at providing a concurrent and distributed native environment for scaling up to very large, high-performance applications, and equally well down to small constrained systems. We present the key specifications and design concepts---in particular a message-transparent architecture, type-safe message interfaces, and pattern matching facilities---that make native actors a viable approach for many robust, elastic, and highly distributed developments. We demonstrate the feasibility of CAF in three scenarios: first for elastic, upscaling environments, second for including heterogeneous hardware like GPGPUs, and third for distributed runtime systems. Extensive performance evaluations indicate ideal runtime behaviour for up to 64 cores at very low memory footprint, or in the presence of GPUs. In these tests, CAF continuously outperforms the competing actor environments Erlang, Charm++, SalsaLite, Scala, ActorFoundry, and even the OpenMPI.Comment: 33 page

Deca : a garbage collection optimizer for in-memory data processing

In-memory caching of intermediate data and active combining of data in shuffle buffers have been shown to be very effective in minimizing the recomputation and I/O cost in big data processing systems such as Spark and Flink. However, it has also been widely reported that these techniques would create a large amount of long-living data objects in the heap. These generated objects may quickly saturate the garbage collector, especially when handling a large dataset, and hence, limit the scalability of the system. To eliminate this problem, we propose a lifetime-based memory management framework, which, by automatically analyzing the user-defined functions and data types, obtains the expected lifetime of the data objects and then allocates and releases memory space accordingly to minimize the garbage collection overhead. In particular, we present Deca,1 a concrete implementation of our proposal on top of Spark, which transparently decomposes and groups objects with similar lifetimes into byte arrays and releases their space altogether when their lifetimes come to an end. When systems are processing very large data, Deca also provides field-oriented memory pages to ensure high compression efficiency. Extensive experimental studies using both synthetic and real datasets show that, in comparing to Spark, Deca is able to (1) reduce the garbage collection time by up to 99.9%, (2) reduce the memory consumption by up to 46.6% and the storage space by 23.4%, (3) achieve 1.2× to 22.7× speedup in terms of execution time in cases without data spilling and 16× to 41.6× speedup in cases with data spilling, and (4) provide similar performance compared to domain-specific systems

Evaluation and optimization of Big Data Processing on High Performance Computing Systems

Programa Oficial de Doutoramento en Investigación en Tecnoloxías da Información. 524V01[Resumo] Hoxe en día, moitas organizacións empregan tecnoloxías Big Data para extraer información de grandes volumes de datos. A medida que o tamaño destes volumes crece, satisfacer as demandas de rendemento das aplicacións de procesamento de datos masivos faise máis difícil. Esta Tese céntrase en avaliar e optimizar estas aplicacións, presentando dúas novas ferramentas chamadas BDEv e Flame-MR. Por unha banda, BDEv analiza o comportamento de frameworks de procesamento Big Data como Hadoop, Spark e Flink, moi populares na actualidade. BDEv xestiona a súa configuración e despregamento, xerando os conxuntos de datos de entrada e executando cargas de traballo previamente elixidas polo usuario. Durante cada execución, BDEv extrae diversas métricas de avaliación que inclúen rendemento, uso de recursos, eficiencia enerxética e comportamento a nivel de microarquitectura. Doutra banda, Flame-MR permite optimizar o rendemento de aplicacións Hadoop MapReduce. En xeral, o seu deseño baséase nunha arquitectura dirixida por eventos capaz de mellorar a eficiencia dos recursos do sistema mediante o solapamento da computación coas comunicacións. Ademais de reducir o número de copias en memoria que presenta Hadoop, emprega algoritmos eficientes para ordenar e mesturar os datos. Flame-MR substitúe o motor de procesamento de datos MapReduce de xeito totalmente transparente, polo que non é necesario modificar o código de aplicacións xa existentes. A mellora de rendemento de Flame-MR foi avaliada de maneira exhaustiva en sistemas clúster e cloud, executando tanto benchmarks estándar coma aplicacións pertencentes a casos de uso reais. Os resultados amosan unha redución de entre un 40% e un 90% do tempo de execución das aplicacións. Esta Tese proporciona aos usuarios e desenvolvedores de Big Data dúas potentes ferramentas para analizar e comprender o comportamento de frameworks de procesamento de datos e reducir o tempo de execución das aplicacións sen necesidade de contar con coñecemento experto para elo.[Resumen] Hoy en día, muchas organizaciones utilizan tecnologías Big Data para extraer información de grandes volúmenes de datos. A medida que el tamaño de estos volúmenes crece, satisfacer las demandas de rendimiento de las aplicaciones de procesamiento de datos masivos se vuelve más difícil. Esta Tesis se centra en evaluar y optimizar estas aplicaciones, presentando dos nuevas herramientas llamadas BDEv y Flame-MR. Por un lado, BDEv analiza el comportamiento de frameworks de procesamiento Big Data como Hadoop, Spark y Flink, muy populares en la actualidad. BDEv gestiona su configuración y despliegue, generando los conjuntos de datos de entrada y ejecutando cargas de trabajo previamente elegidas por el usuario. Durante cada ejecución, BDEv extrae diversas métricas de evaluación que incluyen rendimiento, uso de recursos, eficiencia energética y comportamiento a nivel de microarquitectura. Por otro lado, Flame-MR permite optimizar el rendimiento de aplicaciones Hadoop MapReduce. En general, su diseño se basa en una arquitectura dirigida por eventos capaz de mejorar la eficiencia de los recursos del sistema mediante el solapamiento de la computación con las comunicaciones. Además de reducir el número de copias en memoria que presenta Hadoop, utiliza algoritmos eficientes para ordenar y mezclar los datos. Flame-MR reemplaza el motor de procesamiento de datos MapReduce de manera totalmente transparente, por lo que no se necesita modificar el código de aplicaciones ya existentes. La mejora de rendimiento de Flame-MR ha sido evaluada de manera exhaustiva en sistemas clúster y cloud, ejecutando tanto benchmarks estándar como aplicaciones pertenecientes a casos de uso reales. Los resultados muestran una reducción de entre un 40% y un 90% del tiempo de ejecución de las aplicaciones. Esta Tesis proporciona a los usuarios y desarrolladores de Big Data dos potentes herramientas para analizar y comprender el comportamiento de frameworks de procesamiento de datos y reducir el tiempo de ejecución de las aplicaciones sin necesidad de contar con conocimiento experto para ello.[Abstract] Nowadays, Big Data technologies are used by many organizations to extract valuable information from large-scale datasets. As the size of these datasets increases, meeting the huge performance requirements of data processing applications becomes more challenging. This Thesis focuses on evaluating and optimizing these applications by proposing two new tools, namely BDEv and Flame-MR. On the one hand, BDEv allows to thoroughly assess the behavior of widespread Big Data processing frameworks such as Hadoop, Spark and Flink. It manages the configuration and deployment of the frameworks, generating the input datasets and launching the workloads specified by the user. During each workload, it automatically extracts several evaluation metrics that include performance, resource utilization, energy efficiency and microarchitectural behavior. On the other hand, Flame-MR optimizes the performance of existing Hadoop MapReduce applications. Its overall design is based on an event-driven architecture that improves the efficiency of the system resources by pipelining data movements and computation. Moreover, it avoids redundant memory copies present in Hadoop, while also using efficient sort and merge algorithms for data processing. Flame-MR replaces the underlying MapReduce data processing engine in a transparent way and thus the source code of existing applications does not require to be modified. The performance benefits provided by Flame- MR have been thoroughly evaluated on cluster and cloud systems by using both standard benchmarks and real-world applications, showing reductions in execution time that range from 40% to 90%. This Thesis provides Big Data users with powerful tools to analyze and understand the behavior of data processing frameworks and reduce the execution time of the applications without requiring expert knowledge

Service-Oriented Ad Hoc Grid Computing

Subject of this thesis are the design and implementation of an ad hoc Grid infrastructure. The vision of an ad hoc Grid further evolves conventional service-oriented Grid systems into a more robust, more flexible and more usable environment that is still standards compliant and interoperable with other Grid systems. A lot of work in current Grid middleware systems is focused on providing transparent access to high performance computing (HPC) resources (e.g. clusters) in virtual organizations spanning multiple institutions. The ad hoc Grid vision presented in this thesis exceeds this view in combining classical Grid components with more flexible components and usage models, allowing to form an environment combining dedicated HPC-resources with a large number of personal computers forming a "Desktop Grid". Three examples from medical research, media research and mechanical engineering are presented as application scenarios for a service-oriented ad hoc Grid infrastructure. These sample applications are also used to derive requirements for the runtime environment as well as development tools for such an ad hoc Grid environment. These requirements form the basis for the design and implementation of the Marburg ad hoc Grid Environment (MAGE) and the Grid Development Tools for Eclipse (GDT). MAGE is an implementation of a WSRF-compliant Grid middleware, that satisfies the criteria for an ad hoc Grid middleware presented in the introduction to this thesis. GDT extends the popular Eclipse integrated development environment by components that support application development both for traditional service-oriented Grid middleware systems as well as ad hoc Grid infrastructures such as MAGE. These development tools represent the first fully model driven approach to Grid service development integrated with infrastructure management components in service-oriented Grid computing. This thesis is concluded by a quantitative discussion of the performance overhead imposed by the presented extensions to a service-oriented Grid middleware as well as a discussion of the qualitative improvements gained by the overall solution. The conclusion of this thesis also gives an outlook on future developments and areas for further research. One of these qualitative improvements is "hot deployment" the ability to install and remove Grid services in a running node without interrupt to other active services on the same node. Hot deployment has been introduced as a novelty in service-oriented Grid systems as a result of the research conducted for this thesis. It extends service-oriented Grid computing with a new paradigm, making installation of individual application components a functional aspect of the application. This thesis further explores the idea of using peer-to-peer (P2P networking for Grid computing by combining a general purpose P2P framework with a standard compliant Grid middleware. In previous work the application of P2P systems has been limited to replica location and use of P2P index structures for discovery purposes. The work presented in this thesis also uses P2P networking to realize seamless communication accross network barriers. Even though the web service standards have been designed for the internet, the two-way communication requirement introduced by the WSRF-standards and particularly the notification pattern is not well supported by the web service standards. This defficiency can be answered by mechanisms that are part of such general purpose P2P communication frameworks. Existing security infrastructures for Grid systems focus on protection of data during transmission and access control to individual resources or the overall Grid environment. This thesis focuses on security issues within a single node of a dynamically changing service-oriented Grid environment. To counter the security threads arising from the new capabilities of an ad hoc Grid, a number of novel isolation solutions are presented. These solutions address security issues and isolation on a fine-grained level providing a range of applicable basic mechanisms for isolation, ranging from lightweight system call interposition to complete para-virtualization of the operating systems

Performance Evaluation of Job Scheduling and Resource Allocation in Apache Spark

Advancements in data acquisition techniques and devices are revolutionizing the way image data are collected, managed and processed. Devices such as time-lapse cameras and multispectral cameras generate large amount of image data daily. Therefore, there is a clear need for many organizations and researchers to deal with large volume of image data efficiently. On the other hand, Big Data processing on distributed systems such as Apache Spark are gaining popularity in recent years. Apache Spark is a widely used in-memory framework for distributed processing of large datasets on a cluster of inexpensive computers. This thesis proposes using Spark for distributed processing of large amount of image data in a time efficient manner. However, to share cluster resources efficiently, multiple image processing applications submitted to the cluster must be appropriately scheduled by Spark cluster managers to take advantage of all the compute power and storage capacity of the cluster. Spark can run on three cluster managers including Standalone, Mesos and YARN, and provides several configuration parameters that control how resources are allocated and scheduled. Using default settings for these multiple parameters is not enough to efficiently share cluster resources between multiple applications running concurrently. This leads to performance issues and resource underutilization because cluster administrators and users do not know which Spark cluster manager is the right fit for their applications and how the scheduling behaviour and parameter settings of these cluster managers affect the performance of their applications in terms of resource utilization and response times. This thesis parallelized a set of heterogeneous image processing applications including Image Registration, Flower Counter and Image Clustering, and presents extensive comparisons and analyses of running these applications on a large server and a Spark cluster using three different cluster managers for resource allocation, including Standalone, Apache Mesos and Hodoop YARN. In addition, the thesis examined the two different job scheduling and resource allocations modes available in Spark: static and dynamic allocation. Furthermore, the thesis explored the various configurations available on both modes that control speculative execution of tasks, resource size and the number of parallel tasks per job, and explained their impact on image processing applications. The thesis aims to show that using optimal values for these parameters reduces jobs makespan, maximizes cluster utilization, and ensures each application is allocated a fair share of cluster resources in a timely manner

A new parallelisation technique for heterogeneous CPUs

Parallelization has moved in recent years into the mainstream compilers, and the demand for parallelizing tools that can do a better job of automatic parallelization is higher than ever. During the last decade considerable attention has been focused on developing programming tools that support both explicit and implicit parallelism to keep up with the power of the new multiple core technology. Yet the success to develop automatic parallelising compilers has been limited mainly due to the complexity of the analytic process required to exploit available parallelism and manage other parallelisation measures such as data partitioning, alignment and synchronization. This dissertation investigates developing a programming tool that automatically parallelises large data structures on a heterogeneous architecture and whether a high-level programming language compiler can use this tool to exploit implicit parallelism and make use of the performance potential of the modern multicore technology. The work involved the development of a fully automatic parallelisation tool, called VSM, that completely hides the underlying details of general purpose heterogeneous architectures. The VSM implementation provides direct and simple access for users to parallelise array operations on the Cell’s accelerators without the need for any annotations or process directives. This work also involved the extension of the Glasgow Vector Pascal compiler to work with the VSM implementation as a one compiler system. The developed compiler system, which is called VP-Cell, takes a single source code and parallelises array expressions automatically. Several experiments were conducted using Vector Pascal benchmarks to show the validity of the VSM approach. The VP-Cell system achieved significant runtime performance on one accelerator as compared to the master processor’s performance and near-linear speedups over code runs on the Cell’s accelerators. Though VSM was mainly designed for developing parallelising compilers it also showed a considerable performance by running C code over the Cell’s accelerators