Cloud Computing: A Hands-On Approach

Author: Arshdeep Bahga,Vijay Madisetti

Publisher: CreateSpace Independent Publishing Platform

ISBN: 1494435144

Category: Computers

Page: 454

View: 4133

About the Book Recent industry surveys expect the cloud computing services market to be in excess of $20 billion and cloud computing jobs to be in excess of 10 million worldwide in 2014 alone. In addition, since a majority of existing information technology (IT) jobs is focused on maintaining legacy in-house systems, the demand for these kinds of jobs is likely to drop rapidly if cloud computing continues to take hold of the industry. However, there are very few educational options available in the area of cloud computing beyond vendor-specific training by cloud providers themselves. Cloud computing courses have not found their way (yet) into mainstream college curricula. This book is written as a textbook on cloud computing for educational programs at colleges. It can also be used by cloud service providers who may be interested in offering a broader perspective of cloud computing to accompany their own customer and employee training programs. The typical reader is expected to have completed a couple of courses in programming using traditional high-level languages at the college-level, and is either a senior or a beginning graduate student in one of the science, technology, engineering or mathematics (STEM) fields. We have tried to write a comprehensive book that transfers knowledge through an immersive "hands-on approach", where the reader is provided the necessary guidance and knowledge to develop working code for real-world cloud applications. Additional support is available at the book's website: www.cloudcomputingbook.info Organization The book is organized into three main parts. Part I covers technologies that form the foundations of cloud computing. These include topics such as virtualization, load balancing, scalability & elasticity, deployment, and replication. Part II introduces the reader to the design & programming aspects of cloud computing. Case studies on design and implementation of several cloud applications in the areas such as image processing, live streaming and social networks analytics are provided. Part III introduces the reader to specialized aspects of cloud computing including cloud application benchmarking, cloud security, multimedia applications and big data analytics. Case studies in areas such as IT, healthcare, transportation, networking and education are provided.

Laboratory Training Guide

Cloud Computing: A Hands-On Approach

Author: Arshdeep Bahga,Vijay Madisetti

Publisher: Vijay Madisetti

ISBN: N.A

Category: Computers

Page: 188

View: 2445

In response to requests for instructional and training material from instructors, we prepared this laboratory training guide as a companion book to the Cloud Computing: A Hands-On Approach ("Cloud Book"). This book is designed to serve two purposes. First, it provides a tutorial for the laboratory training that can accompany traditional or online instruction using the Cloud Book. Second, it provides access to the complete source code used in the examples provided in the Cloud Book. The authors hope that this laboratory training guide will continue to prove useful to instructors and students using the Cloud Book.

Internet of Things: A Hands-On Approach

Author: Arshdeep Bahga,Vijay Madisetti

Publisher: VPT

ISBN: 0996025510

Category: Computers

Page: 446

View: 4433

Internet of Things (IoT) refers to physical and virtual objects that have unique identities and are connected to the internet to facilitate intelligent applications that make energy, logistics, industrial control, retail, agriculture and many other domains "smarter". Internet of Things is a new revolution of the Internet that is rapidly gathering momentum driven by the advancements in sensor networks, mobile devices, wireless communications, networking and cloud technologies. Experts forecast that by the year 2020 there will be a total of 50 billion devices/things connected to the internet. This book is written as a textbook on Internet of Things for educational programs at colleges and universities, and also for IoT vendors and service providers who may be interested in offering a broader perspective of Internet of Things to accompany their own customer and developer training programs. The typical reader is expected to have completed a couple of courses in programming using traditional high-level languages at the college-level, and is either a senior or a beginning graduate student in one of the science, technology, engineering or mathematics (STEM) fields. Like our companion book on Cloud Computing, we have tried to write a comprehensive book that transfers knowledge through an immersive "hands on" approach, where the reader is provided the necessary guidance and knowledge to develop working code for real-world IoT applications. Additional support is available at the book's website: www.internet-of-things-book.com Organization The book is organized into 3 main parts, comprising of a total of 11 chapters. Part I covers the building blocks of Internet of Things (IoTs) and their characteristics. A taxonomy of IoT systems is proposed comprising of various IoT levels with increasing levels of complexity. Domain specific Internet of Things and their real-world applications are described. A generic design methodology for IoT is proposed. An IoT system management approach using NETCONF-YANG is described. Part II introduces the reader to the programming aspects of Internet of Things with a view towards rapid prototyping of complex IoT applications. We chose Python as the primary programming language for this book, and an introduction to Python is also included within the text to bring readers to a common level of expertise. We describe packages, frameworks and cloud services including the WAMP-AutoBahn, Xively cloud and Amazon Web Services which can be used for developing IoT systems. We chose the Raspberry Pi device for the examples in this book. Reference architectures for different levels of IoT applications are examined in detail. Case studies with complete source code for various IoT domains including home automation, smart environment, smart cities, logistics, retail, smart energy, smart agriculture, industrial control and smart health, are described. Part III introduces the reader to advanced topics on IoT including IoT data analytics and Tools for IoT. Case studies on collecting and analyzing data generated by Internet of Things in the cloud are described.

Big Data Science & Analytics

A Hands-On Approach

Author: Arshdeep Bahga,Vijay Madisetti

Publisher: Vpt

ISBN: 9780996025539

Category:

Page: 544

View: 9148

We are living in the dawn of what has been termed as the "Fourth Industrial Revolution," which is marked through the emergence of "cyber-physical systems" where software interfaces seamlessly over networks with physical systems, such as sensors, smartphones, vehicles, power grids or buildings, to create a new world of Internet of Things (IoT). Data and information are fuel of this new age where powerful analytics algorithms burn this fuel to generate decisions that are expected to create a smarter and more efficient world for all of us to live in. This new area of technology has been defined as Big Data Science and Analytics, and the industrial and academic communities are realizing this as a competitive technology that can generate significant new wealth and opportunity. Big data is defined as collections of datasets whose volume, velocity or variety is so large that it is difficult to store, manage, process and analyze the data using traditional databases and data processing tools. Big data science and analytics deals with collection, storage, processing and analysis of massive-scale data. Industry surveys, by Gartner and e-Skills, for instance, predict that there will be over 2 million job openings for engineers and scientists trained in the area of data science and analytics alone, and that the job market is in this area is growing at a 150 percent year-over-year growth rate. We have written this textbook, as part of our expanding "A Hands-On Approach"(TM) series, to meet this need at colleges and universities, and also for big data service providers who may be interested in offering a broader perspective of this emerging field to accompany their customer and developer training programs. The typical reader is expected to have completed a couple of courses in programming using traditional high-level languages at the college-level, and is either a senior or a beginning graduate student in one of the science, technology, engineering or mathematics (STEM) fields. An accompanying website for this book contains additional support for instruction and learning (www.big-data-analytics-book.com) The book is organized into three main parts, comprising a total of twelve chapters. Part I provides an introduction to big data, applications of big data, and big data science and analytics patterns and architectures. A novel data science and analytics application system design methodology is proposed and its realization through use of open-source big data frameworks is described. This methodology describes big data analytics applications as realization of the proposed Alpha, Beta, Gamma and Delta models, that comprise tools and frameworks for collecting and ingesting data from various sources into the big data analytics infrastructure, distributed filesystems and non-relational (NoSQL) databases for data storage, and processing frameworks for batch and real-time analytics. This new methodology forms the pedagogical foundation of this book. Part II introduces the reader to various tools and frameworks for big data analytics, and the architectural and programming aspects of these frameworks, with examples in Python. We describe Publish-Subscribe messaging frameworks (Kafka & Kinesis), Source-Sink connectors (Flume), Database Connectors (Sqoop), Messaging Queues (RabbitMQ, ZeroMQ, RestMQ, Amazon SQS) and custom REST, WebSocket and MQTT-based connectors. The reader is introduced to data storage, batch and real-time analysis, and interactive querying frameworks including HDFS, Hadoop, MapReduce, YARN, Pig, Oozie, Spark, Solr, HBase, Storm, Spark Streaming, Spark SQL, Hive, Amazon Redshift and Google BigQuery. Also described are serving databases (MySQL, Amazon DynamoDB, Cassandra, MongoDB) and the Django Python web framework. Part III introduces the reader to various machine learning algorithms with examples using the Spark MLlib and H2O frameworks, and visualizations using frameworks such as Lightning, Pygal and Seaborn.

CompTIA Cloud+ Study Guide

Exam CV0-001

Author: Todd Montgomery

Publisher: John Wiley & Sons

ISBN: 111924322X

Category: Computers

Page: 360

View: 2040

CompTIA® Cloud+® Study Guide -- Acknowledgments -- About the Author -- Contents at a Glance -- Contents -- CompTIA -- Introduction -- Assessment Test -- Answers to Assessment Test -- Chapter 1 Cloud Computing Overview, Concepts, and Models -- Overview of Cloud Computing -- What Is Cloud Computing? -- Computing as a Utility Service -- The Growth of the Cloud -- Why Do This? -- Cloud vs. In-House Computing -- The Past of Computing -- Present State of Computing -- The Future of the Cloud -- Cloud Services Models and Architecture -- SaaS -- IaaS -- PaaS -- CaaS -- XaaS -- DaaS -- BPaaS

Marketing Data Science

Modeling Techniques in Predictive Analytics with R and Python

Author: Thomas W. Miller

Publisher: FT Press

ISBN: 0133887340

Category: Business & Economics

Page: 225

View: 9917

Now , a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.

Handbook of Research on Cloud-Based STEM Education for Improved Learning Outcomes

Author: Chao, Lee

Publisher: IGI Global

ISBN: 1466699256

Category: Education

Page: 481

View: 2528

As technology advances, so must our education system. Cloud computing serves as an ideal method for e-learning thanks to its flexibility, affordability, and availability. Cloud-based learning is especially dynamic in STEM education, as it can significantly lower the cost of building cumbersome computer labs while fostering engaged learning and collaboration among students. The Handbook of Research on Cloud-Based STEM Education for Improved Learning Outcomes prepares current and future instructors for exciting breakthroughs in STEM education driven by the advancement of cloud technologies. From virtual lab and app construction, to information sharing and course material distribution, this volume touches on a variety of topics related to the benefits and challenges of adopting cloud technologies in the classroom. This book is an invaluable reference for educators, technology professionals, administrators, and education students who wish to become leaders in their fields.

Cloud Computing Business in Saudi Arabia

An Examination of the Feasibility of Public Cloud Computing by Enterprise Businesses

Author: Nicholas Guantai

Publisher: GRIN Verlag

ISBN: 3656696454

Category: Computers

Page: 50

View: 2976

Doctoral Thesis / Dissertation from the year 2014 in the subject Computer Science - Commercial Information Technology, grade: 4.5, Egerton University, language: English, abstract: Cloud computing has 3 primary service models including SaaS, IaaS and PaaS, which are classified depending on the level for which a service user interacts with the service provider’s systems in accessing memory, processing power and storage. Deployment models of cloud computing include hybrid, community, public and private clouds depending on the approach to hosting and the number of clients sharing a resource. Due to the prohibitive nature of private cloud computing and requirement for specialized systems in community clouds, the most suitable approach to cloud computing for small and medium enterprises is public cloud computing. In this regard, this study was aimed at determining the extent to which implementation of public cloud computing by enterprise companies is feasible. Due to the cultural and the absence of law in Saudi Arabia ensuring the protection of data in the cloud, challenges in implementing cloud computing in the country are related to adherence to the data governance structure. For instance, privacy and security are important for enterprise companies since the local culture values the safeguarding of family and individual information. In addition, information transferred through the cloud system must adhere to the conservative philosophy and data privacy, which limits the level of compatibility in cloud computing between Saudi Arabia and the western world. Since most service providers are based in the west, companies have to identify a service provider that tailors its products to suit the market in Saudi Arabia. Therefore, implementation of public cloud computing in Saudi Arabia is feasible as long as companies select a service provider with a positive reputation, limit posting of sensitive information to the cloud server, and implement cloud computing gradually to avert the possibility of complete failure. This study determined that SaaS cloud computing is feasible for enterprise companies in Saudi Arabia, but further study is required to examine the feasibility of IaaS and PaaS. In addition, a larger study should be done to collect quantitative data to determine the implications of cloud computing in a representative sample.

Network Data Analytics

A Hands-On Approach for Application Development

Author: K. G. Srinivasa,Siddesh G. M.,Srinidhi H.

Publisher: Springer

ISBN: 3319778005

Category: Computers

Page: 398

View: 4898

In order to carry out data analytics, we need powerful and flexible computing software. However the software available for data analytics is often proprietary and can be expensive. This book reviews Apache tools, which are open source and easy to use. After providing an overview of the background of data analytics, covering the different types of analysis and the basics of using Hadoop as a tool, it focuses on different Hadoop ecosystem tools, like Apache Flume, Apache Spark, Apache Storm, Apache Hive, R, and Python, which can be used for different types of analysis. It then examines the different machine learning techniques that are useful for data analytics, and how to visualize data with different graphs and charts. Presenting data analytics from a practice-oriented viewpoint, the book discusses useful tools and approaches for data analytics, supported by concrete code examples. The book is a valuable reference resource for graduate students and professionals in related fields, and is also of interest to general readers with an understanding of data analytics.