easily modelled. In this thesis a novel compositional approach to performance modelling is presented. This approach is based on a suitably enhanced process algebra, PEPA (Performance Evaluation Process Algebra). The compositional nature of the language provides benefits for model solution as well

Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College. I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.

not support ACID transactions. Examples of systems proposed for cloud serving use include BigTable, PNUTS, Cassandra, HBase, Azure, CouchDB, SimpleDB, Voldemort, and many others. Further, they are being applied to a diverse range of applications that differ considerably from traditional (e.g., TPC-C like

. Here, the processing of single tuples in one step is not enough work to utilize the hardware resources and the cache efficiently and to justify the overhead introduced by iterators. To overcome these problems, we use just-in-time compilation to execute whole OLAP queries on the GPU minimizing

Many scientific disciplines are now data and information driven, and new scientific knowledge is often gained by scientists putting together data analysis and knowledge discovery “pipelines”. A related trend is that more and more scientific communities realize the benefits of sharing their data

. As a result, current GPU query co-processing paradigms can severely suffer from memory stalls. In this paper, we propose a novel in-cache query co-processing paradigm for main memory On-Line Analytical Process-ing (OLAP) databases on coupled CPU-GPU architectures. Specifically, we adapt CPU

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing

Abstract not found

, the performance of spatial database systems has not been satisfactory since their implementations of spatial operations cannot fully utilize the power of modern parallel hardware. In this paper, we provide a customized software solutionthatexploitsGPUsandmulti-coreCPUstoaccelerate spatial cross-comparison in a

. In a general database context, only few research projects address hybrid query processing, i.e., using a mix of CPU- and GPU-based processing to achieve optimal performance. In this paper, we extend our CPU/GPU scheduling framework to support hybrid query processing in database systems. We point out