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Addressing the management challenges for a multitude of distributed cloud architectures, we focus on the three complementary cloud management problems of predictive elasticity, admission control, and placement (or scheduling) of virtual machines. As these problems are intrinsically intertwined we also propose an approach to optimize the overall system behavior by policy-tuning for the tools handling each of them. Moreover, in order to facilitate the execution of some of the management decisions, we also propose new algorithms for live migration of virtual machines with very high workload and/or over low-bandwidth networks, using techniques such as caching, compression, and prioritization of memory pages. The OPTIMIS project

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.

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.