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Networking for Big Data supplies an unprecedented look at cutting-edge research on the networking and communication aspects of Big Data. Starting with a comprehensive introduction to Big Data and its networking issues, it offers deep technical coverage of both theory and applications. The book is d
Chapman all/ crc Big Data series NETWORKING F○RB| G DATA EDITED BY SHUI YU DEAKIN UNIVERSITY BURWOOD AUSTRALIA XIAODONG LIN UNIVERSITY OF ONTARIO INSTITUTE OF TECHNOLOGY OSHAWA ONTARIO. CANADA JELENA MISIC RYERSON UNIVERSITY TORONTO, ONTARIO, CANADA XUEMIN (SHERMAN SHEN UNIVERSITY OF WATERLOO WATERLOO ONTARIO. CANADA (CRC) CRC Press Ta lor& francis g Boca raton London New york CRC Press is an imprint of the Taylor Francis Group, an informa business a chapman hal book o 2016 by Taylor Francis Group, LLC CRC Press Taylor Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca raton Fl 33487-2742 o 2016 by Taylor Francis Group, LLC CRC Press is an imprint of Taylor Francis Group, an Informa business No claim to original U.S. Government works Version date: 20150610 International Standard Book Number-13: 978-1-4822-6350-3(eBook - PDF) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the valid ity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or uti- lized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopy ing, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers,Inc.(ccc),222RosewoodDrive,Danvers,Ma01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe Visit the Taylor Francis Web site at and the crc press Web site at o 2016 by Taylor Francis Group, LLC Contents Preface, ix Editors, XV Contributors, xix SECTIOn 1 Introduction of big Data CHAPTER 1. Orchestrating Science DMZS for Big Data Acceleration Challenges and Approaches SAPTARSHI DEBROY, PRASAD CALYAM, AND MATTHEW DICKINSON ChaPTer 2. A Survey of virtual Machine placement in Cloud Computing for big data 27 YANG WANG, JIE WU, SHAOJIE TANG, AND WU ZHANG CHAPTER 3. Big Data Management Challenges, Approaches, Tools, and their limitations 43 MICHEL ADIBA, JUAN CARLOS CASTREJON, JAVIER A. ESPINOSA-OVIEDO GENOVEVA VARGAS-SOLAR, AND JOSE-LUIS ZECHINELLI-MARTINI CHAPTER 4. Big Data Distributed Systems Management 57 RASHID A. SAEED AND ELMUSTAFA SAYED ALI SeCtion II Networking theory and design for Big data CHAPTER 5. Moving Big Data to the Cloud: Online Cost-Minimizing Algorithms LINQUAN ZHANG, CHUAN WU, ZoNGPENG LL, CHUANXIONG GUO, MINGHUA CHEN AND FRANCIS C. M. LAU CHAPTEr 6. Data Process and analysis technologies of big data 103 PETER WLODARCZAK, MUSTAFA ALLY, AND JEFFREY SOAR o 2016 by Taylor Francis Group, LLC vi■ Contents CHAPTER 7. Network Configuration and flow scheduling for big data Applications 121 LAUTARo DOLBERG, JEROME FRANCOIS, SHIHABUR RAHMAN CHOWDHURY, REAZ AHMED, RAOUF BOUTABA, AND THOMAS ENGEL CHAPTER 8. Speedup of big data Transfer on the Internet 139 GUANGYAN HUANG, WANLEI ZHOU, AND JING HE CHAPTER 9 Energy-Aware Survivable routing in Ever-Escalating data Environments 157 BING LUO, WILLIAM LIU, AND ADNAN AL-ANBUKY Networking Security for Big Data CHAPTER 10. A Review of Network Intrusion Detection in the Big Data Era: Challenges and Future Trends 195 WEIZHI MENG AND WENJUAN LI CHAPTER 11. Toward MapReduce-Based Machine-Learning Techniques for Processing Massive Network Threat monitoring 215 LINQIANG GE, HANLING ZHANG, GUoBIN XU, WEI YU, CHENCHEN, AND ERIK BLASCH CHAPTER 12 Anonymous Communication for Big Data 233 LICHUN LI AND RONGXING LU CHAPTER 13. Flow-Based Anomaly Detection in Big Data 257 ZAHRA JADIDL, VALLIPURAM MUTHUKKUMARASAMY, ELANKAYER SITHIRASENAN AND KALVINDER SINGH SECTION IV Platforms and Systems for Big Data Applications CHAPTER 14 Mining Social Media with SDN-Enabled Big data Platform to transform Tv Watching Experience 283 HAN HU YONGGANG WEN TAT-SENG CHUA, AND XUELONG LI CHAPTER 15. Trends in Cloud Infrastructures for Big Data 305 YACINE DJEMAIEL, BOUTHEINA A. FESSI, AND NOUREDDINE BOUDRIGA o 2016 by Taylor Francis Group, LLC Contents■vi CHAPTER 16. A User Data Profile-Aware Policy-Based Network Management Framework in the Era of Big Data 323 FADI ALHADDADIN, WILLIAM LIU, AND JAIRO A GUTIERREZ CHAPTER 17. Circuit Emulation for Big Data Transfers in Clouds 359 MARAT ZHANIKEEV INDEX 393 o 2016 by Taylor Francis Group, LLC o 2016 by Taylor Francis Group, LLC Preface W E HAVE WITNESSED THE dramatic increase of the use of information technology in every aspect of our lives. For example, Canadas healthcare providers have been moving to electronic record systems that store patients' personal health information in digital format These provide healthcare professionals an easy reliable and safe way to share and access patients health information, thereby providing a reliable and cost-effed tive way to improve efficiency and quality of healthcare. However, e-health applications, together with many others that serve our society, lead to the explosive growth of data Therefore, the crucial question is how to turn the vast amount of data into insight, helping us to better understand what's really happening in our society. In other words, we have come to a point where we need to quickly identify the trends of societal changes through the analysis of the huge amounts of data generated in our daily lives so that proper recom- mendations can be made in order to react quickly before tragedy occurs. This brand new challenge is named Big data Big Data is emerging as a very active research topic due to its pervasive applications in human society, such as governing, climate, finance, science, and so on. In 2012, the Obama administration announced the Big Data Research and Development Initiative, which aims to explore the potential of how Big Data could be used to address important problems facing the government. Although many research studies have been carried out over the past several years, most of them fall under data mining, machine learning, and data analysis. However, these amazing top-level killer applications would not be possible without the underlying support of network infrastructure due to their extremely large vol ume and computing complexity, especially when real-time or near-real-time applications are demanded To date, Big Data is still quite mysterious to various research communities, and par- ticularly, the networking perspective for Big Data to the best of our knowledge is seldom tackled. Many problems wait to be solved, including optimal network topology for Big Data, parallel structures and algorithms for Big Data computing, information retrieval in Big data, network security, and privacy issues in big data This book aims to fill the lacunae in Big Data research, and focuses on important net- working issues in Big Data. Specifically, this book is divided into four major sections Introduction to Big Data, Networking Theory and Design for Big Data, Networking Security for Big Data, and Platforms and Systems for Big Data Applications IX o 2016 by Taylor Francis Group, LLC X■ Preface Section i gives a comprehensive introduction to Big Data and its networking issues. It consists of four chapters Chapter I deals with the challenges in networking for science Big Data movement across campuses, the limitations of legacy campus infrastructure, the technological and policy transformation requirements in building science DMZ infrastructures within campuses through two exemplar case studies, and open problems to personalize such science dmz infrastructures for accelerated big data movement Chapter 2 introduces some representative literature addressing the Virtual Machine Placement Problem(VMPP) in the hope of providing a clear and comprehensive vision on different objectives and corresponding algorithms concerning this subject. VMPP is one of the key technologies for cloud-based big data analytics and recently has drawn much attention. It deals with the problem of assigning virtual machines to servers in order to achieve desired objectives, such as minimizing costs and maximizing performance. Chapter 3 investigates the main challenges involved in the three Vs of Big Data-volume, velocity, and variety. It reviews the main characteristics of existing solutions for address ing each of the Vs(e. g, NOSQL, parallel RDBMS, stream data management systems, and complex event processing systems). Finally, it provides a classification of different func tions offered by newsql systems and discusses their benefits and limitations for process ing big data. Chapter 4 deals with th he concept o of Big Data systems management, especially distr uted systems management, and describes the huge problems of storing, processing, and managing Big Data that are faced by the current data systems. It then explains the types of current data management systems and what will accrue to these systems in cases of Big Data. It also describes the types of modern systems, such as Hadoop technology, that can be used to manage big data systems Section II covers networking theory and design for Big Data. It consists of five chapters Chapter 5 deals with an important open issue of efficiently moving Big Data, produced at different geographical locations over time, into a cloud for processing in an online man- ner. Two representative scenarios are examined and online algorithms are introduced to achieve the timely, cost-minimizing upload of big data into the cloud. The first scenario focuses on uploading dynamically generated, geodispersed data into a cloud for processing using a centralized MapReduce-like framework. The second scenario involves uploading deferral Big Data for processing by a(possibly distributed)MapReduce framework Chapter 6 describes some of the most widespread technologies used for Big Data Emerging technologies for the parallel, distributed processing of Big Data are introduced in this chapter. At the storage level, distributed filesystems for the effective storage of large data volumes on hardware media are described. NoSQL databases, widely in use for persist ing, manipulating, and retrieving Big Data, are explained. At the processing level, frame works for massive, parallel processing capable of handling the volumes and complexities of Big Data are explicated. Analytic techniques extract useful patterns from Big Data and turn data into knowledge. At the analytic layer, the chapter describes the techniques for understanding the data, finding useful patterns, and making predictions on future data Finally, the chapter gives some future directions where Big Data technologies will develo o 2016 by Taylor Francis Group, LLC

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