Architectures, Design Methodologies, and Service Composition Techniques for Grid Job and Resource Management

The field of Grid computing has in recent years emerged and been established as an enabling technology for a range of computational eScience applications. The use of Grid technology allows researchers and industry experts to address problems too large to efficiently study using conventional computing technology, and enables new applications and collaboration models. Grid computing has today not only introduced new technologies, but also influenced new ways to utilize existing technologies. This work addresses technical aspects of the current methodology of Grid computing; to leverage highly functional, interconnected, and potentially under-utilized high-end systems to create virtual systems capable of processing problems too large to address using individual (supercomputing) systems. In particular, this thesis studies the job and resource management problem inherent to Grid environments, and aims to contribute to development of more mature job and resource management systems and software development processes. A number of aspects related to Grid job and resource management are here addressed, including software architectures for Grid job management, design methodologies for Grid software development, service composition (and refactorization) techniques for Service-Oriented Grid Architectures, Grid infrastructure and application integration issues, and middleware-independent and transparent techniques to leverage Grid resource capabilities. The software development model used in this work has been derived from the notion of an ecosystem of Grid components. In this model, a virtual ecosystem is defined by the set of available Grid infrastructure and application components, and ecosystem niches are defined by areas of component functionality. In the Grid ecosystem, applications are constructed through selection and composition of components, and individual components subject to evolution through meritocratic natural selection. Central to the idea of the Grid ecosystem is that mechanisms that promote traits beneficial to survival in the ecosystem, e.g., scalability, integrability, robustness, also influence Grid application and infrastructure adaptability and longevity. As Grid computing has evolved into a highly interdisciplinary field, current Grid applications are very diverse and utilize computational methodologies from a number of fields. Due to this, and the scale of the problems studied, Grid applications typically place great performance requirements on Grid infrastructures, making Grid infrastructure design and integration challenging tasks. In this work, a model of building on, and abstracting, Grid middlewares has been developed and is outlined in the papers. In addition to the contributions of this thesis, a number of software artefacts, e.g., the Grid Job Management Framework (GJMF), have resulted from this work.