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Cloud Computing

Data-Intensive Computing and Scheduling

Cloud Computing by Frederic Magoules
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Overview of Cloud Computing
Cloud evolution
Cloud services
Cloud projects
Cloud challenges
Concluding remarks

Resource Scheduling for Cloud Computing
Cloud service scheduling hierarchy
Economic models for resource-allocation scheduling
Heuristic models for task-execution scheduling
Real-time scheduling in cloud computing
Concluding remarks

Game Theoretical Allocation in a Cloud Datacenter
Game theory
Cloud resource allocation model
Nash equilibrium allocation algorithms
Implementation in a cloud datacenter
Concluding remarks

Multidimensional Data Analysis in a Cloud Datacenter
Data indexing
Data partitioning
Data replication
Query processing parallelism
Concluding remarks

Data-Intensive Applications with MapReduce
MapReduce: a new parallel computing model in cloud computing
Distributed data storage underlying MapReduce
Large-scale data analysis based on MapReduce
SimMapReduce: a simulator for modeling MapReduce framework
Concluding remarks

Large-Scale Multidimensional Data Aggregation
Data organization
Choosing a right MapReduce framework
Parallelizing single group-by query with MapReduce
Parallelizing multiple group-by query with MapReduce
Cost estimation
Concluding remarks

Multidimensional Data Analysis Optimization
Data-locating-based job-scheduling
Improvements by speed-up measurements
Improvements by affecting factors
Improvement by cost estimation
Compressed data structures
Concluding remarks

Real-Time Scheduling with MapReduce
A real-time scheduling problem
Schedulability test in the cloud datacenter
Utilization bounds for schedulability testing
Real-time task scheduling with MapReduce
Reliability indication methods
Concluding remarks

Future for Cloud Computing



CRC Press; September 2012
230 pages; ISBN 9781466507838
Read online, or download in secure PDF format
Title: Cloud Computing
Author: Frederic Magoules; Jie Pan; Fei Teng
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