Survey and Analysis of Production Distributed Computing Infrastructures

This report has two objectives. First, we describe a set of the production distributed infrastructures currently available, so that the reader has a basic understanding of them. This includes explaining why each infrastructure was created and made available and how it has succeeded and failed. The set is not complete, but we believe it is representative. Second, we describe the infrastructures in terms of their use, which is a combination of how they were designed to be used and how users have found ways to use them. Applications are often designed and created with specific infrastructures in mind, with both an appreciation of the existing capabilities provided by those infrastructures and an anticipation of their future capabilities. Here, the infrastructures we discuss were often designed and created with specific applications in mind, or at least specific types of applications. The reader should understand how the interplay between the infrastructure providers and the users leads to such usages, which we call usage modalities. These usage modalities are really abstractions that exist between the infrastructures and the applications; they influence the infrastructures by representing the applications, and they influence the ap- plications by representing the infrastructures

Integration of BPM systems

New technologies have emerged to support the global economy where for instance suppliers, manufactures and retailers are working together in order to minimise the cost and maximise efficiency. One of the technologies that has become a buzz word for many businesses is business process management or BPM. A business process comprises activities and tasks, the resources required to perform each task, and the business rules linking these activities and tasks. The tasks may be performed by human and/or machine actors. Workflow provides a way of describing the order of execution and the dependent relationships between the constituting activities of short or long running processes. Workflow allows businesses to capture not only the information but also the processes that transform the information - the process asset (Koulopoulos, T. M., 1995). Applications which involve automated, human-centric and collaborative processes across organisations are inherently different from one organisation to another. Even within the same organisation but over time, applications are adapted as ongoing change to the business processes is seen as the norm in today’s dynamic business environment. The major difference lies in the specifics of business processes which are changing rapidly in order to match the way in which businesses operate. In this chapter we introduce and discuss Business Process Management (BPM) with a focus on the integration of heterogeneous BPM systems across multiple organisations. We identify the problems and the main challenges not only with regards to technologies but also in the social and cultural context. We also discuss the issues that have arisen in our bid to find the solutions

A FRAMEWORK FOR BIOPROFILE ANALYSIS OVER GRID

An important trend in modern medicine is towards individualisation of healthcare to tailor care to the needs of the individual. This makes it possible, for example, to personalise diagnosis and treatment to improve outcome. However, the benefits of this can only be fully realised if healthcare and ICT resources are exploited (e.g. to provide access to relevant data, analysis algorithms, knowledge and expertise). Potentially, grid can play an important role in this by allowing sharing of resources and expertise to improve the quality of care. The integration of grid and the new concept of bioprofile represents a new topic in the healthgrid for individualisation of healthcare. A bioprofile represents a personal dynamic "fingerprint" that fuses together a person's current and past bio-history, biopatterns and prognosis. It combines not just data, but also analysis and predictions of future or likely susceptibility to disease, such as brain diseases and cancer. The creation and use of bioprofile require the support of a number of healthcare and ICT technologies and techniques, such as medical imaging and electrophysiology and related facilities, analysis tools, data storage and computation clusters. The need to share clinical data, storage and computation resources between different bioprofile centres creates not only local problems, but also global problems. Existing ICT technologies are inappropriate for bioprofiling because of the difficulties in the use and management of heterogeneous IT resources at different bioprofile centres. Grid as an emerging resource sharing concept fulfils the needs of bioprofile in several aspects, including discovery, access, monitoring and allocation of distributed bioprofile databases, computation resoiuces, bioprofile knowledge bases, etc. However, the challenge of how to integrate the grid and bioprofile technologies together in order to offer an advanced distributed bioprofile environment to support individualized healthcare remains. The aim of this project is to develop a framework for one of the key meta-level bioprofile applications: bioprofile analysis over grid to support individualised healthcare. Bioprofile analysis is a critical part of bioprofiling (i.e. the creation, use and update of bioprofiles). Analysis makes it possible, for example, to extract markers from data for diagnosis and to assess individual's health status. The framework provides a basis for a "grid-based" solution to the challenge of "distributed bioprofile analysis" in bioprofiling. The main contributions of the thesis are fourfold: A. An architecture for bioprofile analysis over grid. The design of a suitable aichitecture is fundamental to the development of any ICT systems. The architecture creates a meaiis for categorisation, determination and organisation of core grid components to support the development and use of grid for bioprofile analysis; B. A service model for bioprofile analysis over grid. The service model proposes a service design principle, a service architecture for bioprofile analysis over grid, and a distributed EEG analysis service model. The service design principle addresses the main service design considerations behind the service model, in the aspects of usability, flexibility, extensibility, reusability, etc. The service architecture identifies the main categories of services and outlines an approach in organising services to realise certain functionalities required by distributed bioprofile analysis applications. The EEG analysis service model demonstrates the utilisation and development of services to enable bioprofile analysis over grid; C. Two grid test-beds and a practical implementation of EEG analysis over grid. The two grid test-beds: the BIOPATTERN grid and PlymGRID are built based on existing grid middleware tools. They provide essential experimental platforms for research in bioprofiling over grid. The work here demonstrates how resources, grid middleware and services can be utilised, organised and implemented to support distributed EEG analysis for early detection of dementia. The distributed Electroencephalography (EEG) analysis environment can be used to support a variety of research activities in EEG analysis; D. A scheme for organising multiple (heterogeneous) descriptions of individual grid entities for knowledge representation of grid. The scheme solves the compatibility and adaptability problems in managing heterogeneous descriptions (i.e. descriptions using different languages and schemas/ontologies) for collaborated representation of a grid environment in different scales. It underpins the concept of bioprofile analysis over grid in the aspect of knowledge-based global coordination between components of bioprofile analysis over grid

Supporting simulation in industry through the application of grid computing

An increased need for collaborative research, together with continuing advances in communication technology and computer hardware, has facilitated the development of distributed systems that can provide users access to geographically dispersed computing resources that are administered in multiple computer domains. The term grid computing, or grids, is popularly used to refer to such distributed systems. Simulation is characterized by the need to run multiple sets of computationally intensive experiments. Large scale scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates how grid technology can be effectively exploited by users to model simulations in industry. It introduces our desktop grid, WinGrid, and presents a case study conducted at a leading European investment bank. Results indicate that grid computing does indeed hold promise for simulation in industry

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

High Energy Physics Forum for Computational Excellence: Working Group Reports (I. Applications Software II. Software Libraries and Tools III. Systems)

Computing plays an essential role in all aspects of high energy physics. As computational technology evolves rapidly in new directions, and data throughput and volume continue to follow a steep trend-line, it is important for the HEP community to develop an effective response to a series of expected challenges. In order to help shape the desired response, the HEP Forum for Computational Excellence (HEP-FCE) initiated a roadmap planning activity with two key overlapping drivers -- 1) software effectiveness, and 2) infrastructure and expertise advancement. The HEP-FCE formed three working groups, 1) Applications Software, 2) Software Libraries and Tools, and 3) Systems (including systems software), to provide an overview of the current status of HEP computing and to present findings and opportunities for the desired HEP computational roadmap. The final versions of the reports are combined in this document, and are presented along with introductory material.Comment: 72 page

A Framework for Model-Driven Scientific Workflow Engineering

So-called scientific workflows are one important means in the context of data-intensive science for reliable and efficient scientific data processing in distributed computing infrastructures such as Grids. Scientific Workflow Management Systems (SWfMS) help scientists model and run scientific workflows, whereas a domain-specific layer for workflow modeling by a scientist and a technical layer for automated workflow execution can be distinguished. Initially, many SWfMS were developed from scratch using custom workflow technologies languages without application of already existing and established business workflow technologies. Among the reasons were different life cycles for scientific and business workflows as well as incompatible interfaces and communication protocols of the respective execution infrastructures. Meanwhile, several business IT infrastructures have evolved to serviceoriented architectures (SOAs), for which many Web service standards and technologies have been developed. The Web Services Business Process Execution Language (BPEL), for example, is a well-accepted standard for the implementation and execution of business workflows in SOAs. The SOA architecture pattern has been adopted in scientific IT infrastructures by so-called Service Grids based on existing standards and technologies. Due to this development, BPEL is also suitable for the execution of scientific workflows at the technical layer, which has been elaborated on in many publications and projects. However, BPEL is a workflow language for IT experts and is originally not suited for scientific workflow modeling by a scientist at the domain-specific layer. A domain-specific abstraction of BPEL is therefore required that can be specifically tailored for scientific workflow modeling as well as a corresponding mapping to the technical layer. These challenges of the domain-specific abstraction and the mapping are addressed in this thesis with the help of the Business Process Model and Notation (BPMN) standard and technologies from Model-Driven Software Development (MDSD). Therefore, the MoDFlow approach for Model-Driven Scientific WorkFlow Engineering is presented to map domain-specific scientific workflow models via a BPMN-based intermediate layer to an executable workflow model. The intermediate layer is specified by MoDFlow.BPMN, which is a BPMN metamodel subset with custom extensions for the scientific domain. MoDFlow.BPMN2BPEL defines three consecutive transformation steps to map MoDFlow.BPMN to BPEL for workflow execution. Furthermore, different methods to utilize and extend MoDFlow.BPMN and MoDFlow.BPMN2BPEL are described in the MoDFlow approach, in which the definition of so-called domain-specific languages (DSLs) for the modeling of scientific workflows at the domain-specific layer is focused. The MoDFlow framework is an implementation of the MoDFlow approach, which is based on the Eclipse Modeling Framework (EMF). The MoDFlow framework is evaluated in three application scenarios, in which different utilization and extension mechanisms are examined. The first two application scenarios investigate the technical feasibility of the approach and support scientific workflows with parameter sweeps that are executed on a Grid infrastructure. The third application scenario has been conducted in collaboration with the PubFlow project, which aims to create an infrastructure to model and execute data publication workflows. Based on the Xtext framework, a textual DSL and a corresponding language infrastructure is defined for this purpose that supports developers in creating data publication workflows. This scenario aims to illustrate the practicability of the MoDFlow framework. PubFlow currently plans to implement an additional graphical DSL based on the BPMN notation and a corresponding workflow editor for scientists

Machine Learning And A Workflow Engine For An Agent-Based Structural Health Monitoring System

This thesis reports on work in machine learning and high-performance computing for structural heath monitoring. The data used are acoustic emission signals, and we classify these signals according to source mechanisms, those associated with crack growth being particularly significant. The work reported here is part of a larger project to develop an agent-based structural health monitoring system. The agents are proxies for communication- and computation-intensive techniques and respond to the situation at hand by determining an appropriate constellation of techniques. The techniques thus structured are executed by a workflow engine, which is part of the contribution reported here. It is critical that the system have a repertoire of classifiers with different characteristics so that a combination appropriate for the situation at hand can generally be found. The classifiers are trained using machine-learning techniques, and we report on investigations we conducted on three supervised and two unsupervised learning techniques to determine which techniques are the best to use in a particular situation