Cloud Computing. and Scheduling. Data-Intensive Computing. Frederic Magoules, Jie Pan, and Fei Teng SILKQH. CRC Press. Taylor & Francis Group
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1 Cloud Computing Data-Intensive Computing and Scheduling Frederic Magoules, Jie Pan, and Fei Teng SILKQH CRC Press Taylor & Francis Group Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Croup, an Informa business A CHAPMAN & HALL BOOK
2 Contents List of figures xiii List of tables xv Foreword xvii Preface xix Warranty xxv 1 Overview of cloud computing Introduction Cloud definitions System architecture Deployment models Cloud characteristics Cloud evolution Getting ready for the cloud Brief history Comparison w ith related technologies Cloud services Cloud projects Commercial products Research projects Cloud challenges MapReduce programming model Data management Resource scheduling Concluding remarks 17 2 Resource scheduling for cloud computing Introduction Cloud service scheduling hierarchy Economic models for resource-allocation scheduling Market strategies Auction strategies Economic schedulers 25 vii
3 viii 2.4 Heuristic models for task-execution scheduling Static strategies Dynamic strategies Heuristic schedulers Real-time scheduling in cloud computing Fixed priority strategies Dynamic priority strategies Real-time schedulers Concluding remarks 39 3 Game theoretical allocation in a cloud datacenter Introduction Game theory Normal formulation Payoff choice and utility function Strategy choice and Nash equilibrium Cloud resource allocation model Bid-shared auction Non-cooperative game Nash equilibrium allocation algorithms Bid functions Parameters estimation Equilibrium price Implementation in a cloud datacenter Cloudsim toolkit Communication among entities Bidding algorithms Comparison of forecasting methods Concluding remarks Multi-dimensional data analysis in a cloud datacenter Introduction Pre-computing Data cube Sparse cube Reuse of previous query results Data compressing Data indexing Data partitioning Data partitioning methods Horizontal partitioning of a multi-dimensional dataset Vertical partitioning of a multi-dimensional dataset Data replication Query processing parallelism Inter-and intra-operators 76
4 IX Exchange operator SQL operator parallelization Concluding remarks 84 Data intensive applications with MapReduce Introduction MapReduce: New parallel computing model in cloud computing Dataflow model Two frameworks: GridGain versus Hadoop Communication cost analysis Distributed data storage underlying MapReduce Google file system Distributed cache memory Data accessing Large-scale data analysis based on MapReduce Data query languages Data analysis applications Comparison with shared-nothing parallel databases SimMapReduce: Simulator for modeling MapReduce framework Multi-layer architecture Input and output of simulator Implementation details of simulator Modeling process Concluding remarks 109 Large-scale multi-dimensional data aggregation Introduction Ill 6.2 Data organization Ill Computations in data explorations Multiple group-by query Choosing a right MapReduce framework Advantages of GridGain Combiner support in Hadoop and GridGain Realizing MapReduce applications with GridGain Workflow analysis of GridGain procedure Parallelizing single group-by query with MapReduce Parallelizing multiple group-by query with MapReduce Data partitioning and data placement MapReduce model-based implementation MapCombineReduce model-based implementation Cost estimation MapReduce model-based implementation MapCombineReduce model-based implementation Comparison of implementations Concluding remarks 133
5 X 7 Multi-dimensional data analysis optimization Introduction Data-locating based job-scheduling Job-scheduling implementation Two-level scheduling Alternative job-scheduling schemes Improvements by speed-up measurements Horizontal partitioning Vertical partitioning Improvements by affecting factors Query selectivity Side effects Improvement by cost estimation Horizontal partitioning Vertical partitioning Comparison of partitioning Compressed data structures Data structure description Data structures for storing recordld-list Compressed... data structures for different dimensions Bitmap sparcity and compressing Concluding remarks Real-time scheduling with MapReduce Introduction Real-time scheduling problem Real-time task Processing resource Scheduling algorithms Schedulability test in the cloud datacenter Pseudo-polynomial complexity Polynomial complexity Constant complexity Utilization bounds for schedulability testing Classical bound Closer periods Harmonic chains Hyperbolic bound Real-time task scheduling with MapReduce System model MapReduce segmentation Worst pattern for a schedulable task set Reliability indication methods Reliability indicator Schedulability test conditions 176
6 xi Comparison of rate monotonic conditions Comparison of deadline monotonic conditions Concluding remarks Future for cloud computing 183 Bibliography 187 Index 203
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