End-to-End QoS Support for a Medical Grid Service Infrastructure

Quality of Service support is an important prerequisite for the adoption of Grid technologies for medical applications. The GEMSS Grid infrastructure addressed this issue by offering end-to-end QoS in the form of explicit timeliness guarantees for compute-intensive medical simulation services. Within GEMSS, parallel applications installed on clusters or other HPC hardware may be exposed as QoS-aware Grid services for which clients may dynamically negotiate QoS constraints with respect to response time and price using Service Level Agreements. The GEMSS infrastructure and middleware is based on standard Web services technology and relies on a reservation based approach to QoS coupled with application specific performance models. In this paper we present an overview of the GEMSS infrastructure, describe the available QoS and security mechanisms, and demonstrate the effectiveness of our methods with a Grid-enabled medical imaging service

Autonomous grid scheduling using probabilistic job runtime scheduling

Computational Grids are evolving into a global, service-oriented architecture – a universal platform for delivering future computational services to a range of applications of varying complexity and resource requirements. The thesis focuses on developing a new scheduling model for general-purpose, utility clusters based on the concept of user requested job completion deadlines. In such a system, a user would be able to request each job to finish by a certain deadline, and possibly to a certain monetary cost. Implementing deadline scheduling is dependent on the ability to predict the execution time of each queued job, and on an adaptive scheduling algorithm able to use those predictions to maximise deadline adherence. The thesis proposes novel solutions to these two problems and documents their implementation in a largely autonomous and self-managing way. The starting point of the work is an extensive analysis of a representative Grid workload revealing consistent workflow patterns, usage cycles and correlations between the execution times of jobs and its properties commonly collected by the Grid middleware for accounting purposes. An automated approach is proposed to identify these dependencies and use them to partition the highly variable workload into subsets of more consistent and predictable behaviour. A range of time-series forecasting models, applied in this context for the first time, were used to model the job execution times as a function of their historical behaviour and associated properties. Based on the resulting predictions of job runtimes a novel scheduling algorithm is able to estimate the latest job start time necessary to meet the requested deadline and sort the queue accordingly to minimise the amount of deadline overrun. The testing of the proposed approach was done using the actual job trace collected from a production Grid facility. The best performing execution time predictor (the auto-regressive moving average method) coupled to workload partitioning based on three simultaneous job properties returned the median absolute percentage error centroid of only 4.75%. This level of prediction accuracy enabled the proposed deadline scheduling method to reduce the average deadline overrun time ten-fold compared to the benchmark batch scheduler. Overall, the thesis demonstrates that deadline scheduling of computational jobs on the Grid is achievable using statistical forecasting of job execution times based on historical information. The proposed approach is easily implementable, substantially self-managing and better matched to the human workflow making it well suited for implementation in the utility Grids of the future

A service-oriented Grid environment with on-demand QoS support

Grid Computing entstand aus der Vision für eine neuartige Recheninfrastruktur, welche darauf abzielt, Rechenkapazität so einfach wie Elektrizität im Stromnetz (power grid) verfügbar zu machen. Der entsprechende Zugriff auf global verteilte Rechenressourcen versetzt Forscher rund um den Globus in die Lage, neuartige Herausforderungen aus Wissenschaft und Technik in beispiellosem Ausmaß in Angriff zu nehmen. Die rasanten Entwicklungen im Grid Computing begünstigten auch Standardisierungsprozesse in Richtung Harmonisierung durch Service-orientierte Architekturen und die Anwendung kommerzieller Web Services Technologien. In diesem Kontext ist auch die Sicherung von Qualität bzw. entsprechende Vereinbarungen über die Qualität eines Services (QoS) wichtig, da diese vor allem für komplexe Anwendungen aus sensitiven Bereichen, wie der Medizin, unumgänglich sind. Diese Dissertation versucht zur Entwicklung im Grid Computing beizutragen, indem eine Grid Umgebung mit Unterstützung für QoS vorgestellt wird. Die vorgeschlagene Grid Umgebung beinhaltet eine sichere Service-orientierte Infrastruktur, welche auf Web Services Technologien basiert, sowie bedarfsorientiert und automatisiert HPC Anwendungen als Grid Services bereitstellen kann. Die Grid Umgebung zielt auf eine kommerzielle Nutzung ab und unterstützt ein durch den Benutzer initiiertes, fallweises und dynamisches Verhandeln von Serviceverträgen (SLAs). Das Design der QoS Unterstützung ist generisch, jedoch berücksichtigt die Implementierung besonders die Anforderungen von rechenintensiven und zeitkritischen parallelen Anwendungen, bzw. Garantien f¨ur deren Ausführungszeit und Preis. Daher ist die QoS Unterstützung auf Reservierung, anwendungsspezifische Abschätzung und Preisfestsetzung von Ressourcen angewiesen. Eine entsprechende Evaluation demonstriert die Möglichkeiten und das rationale Verhalten der QoS Infrastruktur. Die Grid Infrastruktur und insbesondere die QoS Unterstützung wurde in Forschungs- und Entwicklungsprojekten der EU eingesetzt, welche verschiedene Anwendungen aus dem medizinischen und bio-medizinischen Bereich als Services zur Verfügung stellen. Die EU Projekte GEMSS und Aneurist befassen sich mit fortschrittlichen HPC Anwendungen und global verteilten Daten aus dem Gesundheitsbereich, welche durch Virtualisierungstechniken als Services angeboten werden. Die Benutzung von Gridtechnologie als Basistechnologie im Gesundheitswesen ermöglicht Forschern und Ärzten die Nutzung von Grid Services in deren Arbeitsumfeld, welche letzten Endes zu einer Verbesserung der medizinischen Versorgung führt.Grid computing emerged as a vision for a new computing infrastructure that aims to make computing resources available as easily as electric power through the power grid. Enabling seamless access to globally distributed IT resources allows dispersed users to tackle large-scale problems in science and engineering in unprecedented ways. The rapid development of Grid computing also encouraged standardization, which led to the adoption of a service-oriented paradigm and an increasing use of commercial Web services technologies. Along these lines, service-level agreements and Quality of Service are essential characteristics of the Grid and specifically mandatory for Grid-enabling complex applications from certain domains such as the health sector. This PhD thesis aims to contribute to the development of Grid technologies by proposing a Grid environment with support for Quality of Service. The proposed environment comprises a secure service-oriented Grid infrastructure based on standard Web services technologies which enables the on-demand provision of native HPC applications as Grid services in an automated way and subject to user-defined QoS constraints. The Grid environment adopts a business-oriented approach and supports a client-driven dynamic negotiation of service-level agreements on a case-by-case basis. Although the design of the QoS support is generic, the implementation emphasizes the specific requirements of compute-intensive and time-critical parallel applications, which necessitate on-demand QoS guarantees such as execution time limits and price constraints. Therefore, the QoS infrastructure relies on advance resource reservation, application-specific resource capacity estimation, and resource pricing. An experimental evaluation demonstrates the capabilities and rational behavior of the QoS infrastructure. The presented Grid infrastructure and in particular the QoS support has been successfully applied and demonstrated in EU projects for various applications from the medical and bio-medical domains. The EU projects GEMSS and Aneurist are concerned with advanced e-health applications and globally distributed data sources, which are virtualized by Grid services. Using Grid technology as enabling technology in the health domain allows medical practitioners and researchers to utilize Grid services in their clinical environment which ultimately results in improved healthcare

Autonomous grid scheduling using probabilistic job runtime forecasting.

Computational Grids are evolving into a global, service-oriented architecture a universal platform for delivering future computational services to a range of applications of varying complexity and resource requirements. The thesis focuses on developing a new scheduling model for general-purpose, utility clusters based on the concept of user requested job completion deadlines. In such a system, a user would be able to request each job to finish by a certain deadline. and possibly to a certain monetary cost. Implementing deadline scheduling is dependent on the ability to predict the execution time of each queued job. and on an adaptive scheduling algorithm able to use those predictions to maximise deadline adherence. The thesis proposes novel solutions to these two problems and documents their implementation in a largely autonomous and self-managing way. The starting point of the work is an extensive analysis of a representative Grid workload revealing consistent workflow patterns, usage cycles and correlations between the execution times of jobs and its properties commonly collected by the Grid middleware for accounting purposes. An automated approach is proposed to identify these dependencies and use them to partition the highly variable workload into subsets of more consistent and predictable behaviour. A range of time-series forecasting models, applied in this context for the first time, were used to model the job execution times as a function of their historical behaviour and associated properties. Based on the resulting predictions of job runtimes a novel scheduling algorithm is able to estimate the latest job start time necessary to meet the requested deadline and sort the queue accordingly to minimise the amount of deadline overrun. The testing of the proposed approach was done using the actual job trace collected from a production Grid facility. The best performing execution time predictor (the auto-regressive moving average method) coupled to workload partitioning based on three simultaneous job properties returned the median absolute percentage error eentroid of only 4.75CX. This level of prediction accuracy enabled the proposed deadline scheduling method to reduce the average deadline overrun time ten-fold compared to the benchmark batch scheduler. Overall, the thesis demonstrates that deadline scheduling of computational jobs on the Grid is achievable using statistical forecasting of job execution times based on historical information. The proposed approach is easily implementable, substantially self-managing and better matched to the human workflow making it well suited for implementation in the utility Grids of the future