Cloud Computing: Data-Intensive Computing and Scheduling, 1st Edition (Hardback) book cover

Cloud Computing

Data-Intensive Computing and Scheduling, 1st Edition

By Frederic Magoules, Jie Pan, Fei Teng

Chapman and Hall/CRC

231 pages | 48 B/W Illus.

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As more and more data is generated at a faster-than-ever rate, processing large volumes of data is becoming a challenge for data analysis software. Addressing performance issues, Cloud Computing: Data-Intensive Computing and Scheduling explores the evolution of classical techniques and describes completely new methods and innovative algorithms. The book delineates many concepts, models, methods, algorithms, and software used in cloud computing.

After a general introduction to the field, the text covers resource management, including scheduling algorithms for real-time tasks and practical algorithms for user bidding and auctioneer pricing. It next explains approaches to data analytical query processing, including pre-computing, data indexing, and data partitioning. Applications of MapReduce, a new parallel programming model, are then presented. The authors also discuss how to optimize multiple group-by query processing and introduce a MapReduce real-time scheduling algorithm.

A useful reference for studying and using MapReduce and cloud computing platforms, this book presents various technologies that demonstrate how cloud computing can meet business requirements and serve as the infrastructure of multidimensional data analysis applications.

Table of Contents

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



About the Authors

Frédéric Magoulès is a professor at École Centrale Paris, where he leads the high performance computing research group. His research focuses on the algorithmic interface between parallel computing and the numerical analysis of PDEs and algebraic differential equations. He earned a Ph.D. in applied mathematics from Université Pierre et Marie Curie.

Jie Pan is a Java developer at the Klee Group Company. She earned a Ph.D. in applied mathematics. During her doctoral work, she focused on large-scale data analysis on distributed systems.

Fei Teng is a researcher in the Key Lab of Cloud Computing and Intelligent Technology at Southwest Jiaotong University. Her research interests are mainly in cloud computing, data mining, resource allocation, and distributed scheduling algorithms.

About the Series

Chapman & Hall/CRC Numerical Analysis and Scientific Computing Series

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Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Internet / General
MATHEMATICS / Number Systems