<p>The emergence of Grid computing infrastructures enables researchers to share resources and collaborate in more efficient ways than before, despite belonging to different organizations and being distanced geographically. While the Grid computing paradigm offers new opportunities, it also gives rise to new difficulties. One such problem is the selection of resources for user applications. Given the large and disparate set of Grid resources, manual resource selection becomes impractical, even for experienced users. This thesis investigates methods, algorithms and software for a Grid resource broker, i.e., a scheduling agent that automates the resource selection process for the user. The development of such a component is a non-trivial task as Grid resources are heterogeneous in hardware, software, availability, ownership and usage policies. A wide range of algorithmically difficult issues must also be solved, including characterization of jobs, prediction of resource performance, data placement considerations, and, how to provide Quality of Service guarantees. One contribution of this thesis is the development of resource brokering algorithms that enable resource selection based on Grid job performance predictions and use advance reservations to provide Quality of Service guarantees. The thesis also includes an algorithm for coallocation of sets of jobs. This algorithm guarantees a simultaneous start of each subjob, as required e.g., when running larger-than-supercomputer simulations that involve multiple resources.</p><p>We today have the somewhat paradoxal situation where Grids, originally aimed to overcome interoperability problems between different computing platforms, themselves struggle with interoperability problems caused by the wide range of interfaces, protocols and data formats that are used in different environments. The reasons for this situation are obvious, expected and almost impossible to avoid, as the task of defining appropriate standards, models and best-practices must be preceded by basic research, proof-of-concept implementations and real-world testing. We address the interoperability problem with a generic Grid resource brokering architecture and job submission service.</p><p>By using (proposed) standard formats and protocols, the service acts as an interoperability-bridge that translates job requests between clients and resources running different Grid middlewares. This concept is demonstrated by the integration of the service with three different Grid middlewares. The service also enables users to both fine-tune the existing resource selection algorithms and plug in custom brokering algorithms tailored to their requirements.</p>

The emergence of Grid computing infrastructures enables researchers to share resources and collaborate in more efficient ways than before, despite belonging to different organizations and being distanced geographically. While the Grid computing paradigm offers new opportunities, it also gives rise to new difficulties. One such problem is the selection of resources for user applications. Given the large and disparate set of Grid resources, manual resource selection becomes impractical, even for experienced users. This thesis investigates methods, algorithms and software for a Grid resource broker, i.e., a scheduling agent that automates the resource selection process for the user. The development of such a component is a non-trivial task as Grid resources are heterogeneous in hardware, software, availability, ownership and usage policies. A wide range of algorithmically difficult issues must also be solved, including characterization of jobs, prediction of resource performance, data placement considerations, and, how to provide Quality of Service guarantees. One contribution of this thesis is the development of resource brokering algorithms that enable resource selection based on Grid job performance predictions and use advance reservations to provide Quality of Service guarantees. The thesis also includes an algorithm for coallocation of sets of jobs. This algorithm guarantees a simultaneous start of each subjob, as required e.g., when running larger-than-supercomputer simulations that involve multiple resources. We today have the somewhat paradoxal situation where Grids, originally aimed to overcome interoperability problems between different computing platforms, themselves struggle with interoperability problems caused by the wide range of interfaces, protocols and data formats that are used in different environments. The reasons for this situation are obvious, expected and almost impossible to avoid, as the task of defining appropriate standards, models and best-practices must be preceded by basic research, proof-of-concept implementations and real-world testing. We address the interoperability problem with a generic Grid resource brokering architecture and job submission service. By using (proposed) standard formats and protocols, the service acts as an interoperability-bridge that translates job requests between clients and resources running different Grid middlewares. This concept is demonstrated by the integration of the service with three different Grid middlewares. The service also enables users to both fine-tune the existing resource selection algorithms and plug in custom brokering algorithms tailored to their requirements.

The emergence of Grid computing infrastructures enables researchers to shareresources and collaborate in more efficient ways than before, despite belongingto different organizations and being geographically distributed. While the Gridcomputing paradigm offers new opportunities, it also gives rise to newdifficulties. This thesis investigates methods, architectures, and algorithmsfor a range of topics in the area of Grid resource management. One studiedtopic is how to automate and improve resource selection, despite heterogeneityin Grid hardware, software, availability, ownership, and usage policies.Algorithmical difficulties for this are, e.g., characterization of jobs andresources, prediction of resource performance, and data placementconsiderations. Investigated Quality of Service aspects of resource selectioninclude how to guarantee job start and/or completion times as well as how tosynchronize multiple resources for coordinated use through coallocation.Another explored research topic is architectural considerations for frameworksthat simplify and automate submission, monitoring, and fault handling for largeamounts of jobs. This thesis also investigates suitable Grid interactionpatterns for scientific workflows, studies programming models that enable dataparallelism for such workflows, as well as analyzes how workflow compositiontools should be designed to increase flexibility and expressiveness. We today have the somewhat paradoxical situation where Grids, originally aimed tofederate resources and overcome interoperability problems between differentcomputing platforms, themselves struggle with interoperability problems causedby the wide range of interfaces, protocols, and data formats that are used indifferent environments. This thesis demonstrates how proof-of-concept softwaretools for Grid resource management can, by using (proposed) standard formatsand protocols as well as leveraging state-of-the-art principles fromservice-oriented architectures, be made independent of current Gridinfrastructures. Further interoperability contributions include an in-depthstudy that surveys issues related to the use of Grid resources in scientificworkflows. This study improves our understanding of interoperability amongscientific workflow systems by viewing this topic from three differentperspectives: model of computation, workflow language, and executionenvironment. A final contribution in this thesis is the investigation of how the design ofGrid middleware tools can adopt principles and concepts from softwareengineering in order to improve, e.g., adaptability and interoperability.

Computational grids are distributed systems composed of heterogeneous computing resources which are distributed geographically and administratively. These highly scalable systems are designed to meet the large computational demands of many users from scientific and business orientations. This dissertation address problems related to the allocation of the computing resources which compose a grid. First, the design of a pan-Canadian grid is presented. The design exploits the maturing stability of grid deployment toolkits, and introduces novel services for efficiently allocating the grids resources. The challenges faced by this grid deployment motivate further exploration in optimizing grid resource allocations. The primary contribution of this dissertation is one such technique for allocating grid resources. By applying a utility model to the grid allocation options, it is possible to quantify the relative merits of the various possible scheduling decisions. Indeed, a number of utility heuristics are derived to provide quality-of-service policies on the grid; these implement scheduling policies which favour efficiency and also allow users to intervene with urgent tasks. Using this model, the allocation problem is then formulated as a knapsack problem. Formulation in this manner allows for rapid solution times and results in nearly optimal allocations. The combined utility/knapsack approach to grid resource allocation is first presented in the allocation of single resource type, processors. By evaluating the approach with novel utility heuristics using both random and real workloads, it is shown to result in efficient schedules which have characteristics that match the intended policies. Additionally, two design and analysis techniques are performed to optimize the design of the utility/knapsack scheduler; these techniques play a significant role in practical adoption of the approach. Next, the utility/knapsack approach is extended to the allocation of multiple resource types. This extension generalizes the grid allocation solution a wider variety of resources, including processors, disk storage, and network bandwidth. The general technique, when combined with new heuristics for the varied resource types, is shown to result in improved performance against reference strategies. This dissertation concludes with a novel application of the utility/knapsack approach to fault-tolerant task scheduling. Computational grids typically feature many techniques for providing fault tolerance to the grid tasks, including retrying failed tasks or replicating running tasks. By applying the utility/knapsack approach, the relative merits of these varied techniques can be quantified, and the overall number of failures can be decreased subject to resource cost considerations.