Cloud Computing

Cloud Computing: Data-Intensive Computing and Scheduling

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Features

  • Discusses the implementation of priority-based strategies
  • Presents the elements underlying a cloud datacenter
  • Offers solutions to resource allocation problems in clouds
  • Describes the features of multidimensional data analysis queries
  • Illustrates the use of MapReduce, a new parallel programming model
  • Explores directions for further research

Summary

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
Introduction
Cloud evolution
Cloud services
Cloud projects
Cloud challenges
Concluding remarks

Resource Scheduling for Cloud Computing
Introduction
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
Introduction
Game theory
Cloud resource allocation model
Nash equilibrium allocation algorithms
Implementation in a cloud datacenter
Concluding remarks

Multidimensional Data Analysis in a Cloud Datacenter
Introduction
Pre-computing
Data indexing
Data partitioning
Data replication
Query processing parallelism
Concluding remarks

Data-Intensive Applications with MapReduce
Introduction
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
Introduction
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
Introduction
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
Introduction
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

Bibliography

Index

Author Bio(s)

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.