Magoules / Pan / Teng Cloud Computing

Data-Intensive Computing and Scheduling
1. Auflage 2012
ISBN: 978-1-4665-0783-8
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 231 Seiten

Reihe: Chapman & Hall/CRC Numerical Analysis and Scientific Computing Series

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.

Zielgruppe

Graduate students and researchers in computer science, computational science, and engineering.

Autoren/Hrsg.

Magoules, Frederic

Pan, Jie

Teng, Fei

Fachgebiete

• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Cloud-Computing, Grid-Computing

Inhaltsverzeichnis

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

Über Autor(innen)

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.