Handbook of Cloud Computing

Preface

Cloud computing has become a great solution for providing a flexible, on-demand, and dynamically scalable computing infrastructure for many applications. Cloud computing also presents a significant technology trends, and it is already obvious that it is reshaping information technology processes and the IT marketplace.

This Handbook is a carefully edited book – contributors are 65 worldwide experts in the field of cloud computing and their applications. The Handbook Advisory Board, comprised of nine researchers and practitioners from academia and industry, helped in reshaping the Handbook and selecting the right topics and creative and knowledgeable contributors. The scope of the book includes leading-edge cloud computing technologies, systems, and architectures; cloud computing services; and

a variety of cloud computing applications. The Handbook comprises four parts, which consist of 26 chapters. The first part on Technologies and Systems includes articles dealing with cloud computing tech- nologies, storage and fault tolerant strategies in cloud computing, workflows, grid computing technologies, and the role of networks in cloud computing.

The second part on Architectures focuses on articles on several specific architec- tural concepts applied in cloud computing, including enterprise knowledge clouds, high-performance computing clouds, clouds with vertical load distribution, and peer-to-peer based clouds.

The third part on Services consists of articles on various issues relating to cloud services, including types of services, service scalability, scientific services, and dynamic collaborative services.

The forth part on Applications describes various cloud computing applications from enterprise knowledge clouds, scientific and statistical computing, scientific data management, to medical applications.

With the dramatic growth of cloud computing technologies, platforms and ser- vices, this Handbook can be the definitive resource for persons working in this field as researchers, scientists, programmers, engineers, and users. The book is intended for a wide variety of people including academicians, designers, developers, edu- cators, engineers, practitioners, researchers, and graduate students. This book can also be beneficial for business managers, entrepreneurs, and investors. The book With the dramatic growth of cloud computing technologies, platforms and ser- vices, this Handbook can be the definitive resource for persons working in this field as researchers, scientists, programmers, engineers, and users. The book is intended for a wide variety of people including academicians, designers, developers, edu- cators, engineers, practitioners, researchers, and graduate students. This book can also be beneficial for business managers, entrepreneurs, and investors. The book

Cloud Computing. The main features of this Handbook can be summarized as:

1. The Handbook describes and evaluates the current state-of-the-art in a new field of cloud computing.

2. It also presents current systems, services, and main players in this explosive field.

3. Contributors to the Handbook are the leading researchers from academia and practitioners from industry.

We would like to thank the authors for their contributions. Without their expertise and effort, this Handbook would never come to fruition. Springer editors and staff also deserve our sincere recognition for their support throughout the project.

Boca Raton, Florida Borko Furht Armando Escalante

Contents

Part I Technologies and Systems

1 Cloud Computing Fundamentals ...................

3 Borko Furht

2 Cloud Computing Technologies and Applications ..........

21 Jinzy Zhu

3 Key Enabling Technologies for Virtual Private Clouds .......

47 Jeffrey M. Nick, David Cohen, and Burton S. Kaliski Jr.

4 The Role of Networks in Cloud Computing .............

65 Geng Lin and Mac Devine

83 Anthony M. Middleton

5 Data-Intensive Technologies for Cloud Computing .........

6 Survey of Storage and Fault Tolerance Strategies Used

in Cloud Computing ..........................

137 Kathleen Ericson and Shrideep Pallickara

7 Scheduling Service Oriented Workflows Inside Clouds

Using an Adaptive Agent Based Approach .............. 159 Marc Eduard Frîncu

8 The Role of Grid Computing Technologies

in Cloud Computing ..........................

183 David Villegas, Ivan Rodero, Liana Fong, Norman Bobroff, Yanbin Liu, Manish Parashar, and S. Masoud Sadjadi

9 Cloudweaver: Adaptive and Data-Driven Workload

Manager for Generic Clouds ..................... 219 Rui Li, Lei Chen, and Wen-Syan Li Manager for Generic Clouds ..................... 219 Rui Li, Lei Chen, and Wen-Syan Li

Part II Architectures

10 Enterprise Knowledge Clouds: Architecture and Technologies ... 239 Kemal A. Delic and Jeff A. Riley

11 Integration of High-Performance Computing into Cloud

Computing Services .......................... 255 Mladen A. Vouk, Eric Sills, and Patrick Dreher

12 Vertical Load Distribution for Cloud Computing via

Multiple Implementation Options ................... 277 Thomas Phan and Wen-Syan Li

13 SwinDeW-C: A Peer-to-Peer Based Cloud Workflow System .... 309 Xiao Liu, Dong Yuan, Gaofeng Zhang, Jinjun Chen, and Yun Yang

Part III Services

14 Cloud Types and Services ....................... 335 Hai Jin, Shadi Ibrahim, Tim Bell, Wei Gao, Dachuan Huang, and Song Wu

15 Service Scalability Over the Cloud .................. 357 Juan Cáceres, Luis M. Vaquero, Luis Rodero-Merino, Álvaro Polo, and Juan J. Hierro

16 Scientific Services on the Cloud .................... 379 David Chapman, Karuna P. Joshi, Yelena Yesha, Milt Halem, Yaacov Yesha, and Phuong Nguyen

17 A Novel Market-Oriented Dynamic Collaborative Cloud

Service Platform ............................ 407 Mohammad Mehedi Hassan and Eui-Nam Huh

Part IV Applications

18 Enterprise Knowledge Clouds: Applications and Solutions ..... 437 Jeff A. Riley and Kemal A. Delic

19 Open Science in the Cloud: Towards a Universal Platform

for Scientific and Statistical Computing ............... 453 Karim Chine

20 Multidimensional Environmental Data Resource Brokering

on Computational Grids and Scientific Clouds ............ 475 Raffaele Montella, Giulio Giunta, and Giuliano Laccetti

21 HPC on Competitive Cloud Resources ................ 493 Paolo Bientinesi, Roman Iakymchuk, and Jeff Napper

Contents ix

22 Scientific Data Management in the Cloud: A Survey of

Technologies, Approaches and Challenges .............. 517 Sangmi Lee Pallickara, Shrideep Pallickara, and Marlon Pierce

23 Feasibility Study and Experience on Using Cloud

Infrastructure and Platform for Scientific Computing ....... 535 Mikael Fernandus Simalango and Sangyoon Oh

24 A Cloud Computing Based Patient Centric Medical

Information System .......................... 553 Ankur Agarwal, Nathan Henehan, Vivek Somashekarappa, A.S. Pandya, Hari Kalva, and Borko Furht

25 Cloud@Home: A New Enhanced Computing Paradigm ...... 575 Salvatore Distefano, Vincenzo D. Cunsolo, Antonio Puliafito, and Marco Scarpa

26 Using Hybrid Grid/Cloud Computing Technologies for Environmental Data Elastic Storage, Processing,

and Provisioning ............................ 595 Raffaele Montella and Ian Foster

Index ..................................... 619

Contributors

Ankur Agarwal Department of Computer Science and Engineering, FAU, Boca Raton, FL, USA, ankur@cse.fau.edu

Tim Bell Department of Computer Science and Software Engineering, University of Canterbury, Christchurch, New Zealand, tim.bell@canterbury.ac.nz

Paolo Bientinesi AICES, RWTH, Aachen, Germany, pauldj@aices.rwth-aachen.de

Norman Bobroff IBM Watson Research Center, Hawthorne, NY, USA, bobroff@us.ibm.com

Juan Cáceres Telefónica Investigación y Desarrollo, Madrid, Spain, caceres@tid.es

David Chapman Computer Science and Electrical Engineering Department, University of Maryland, Baltimore County, MD, USA, dchapm2@umbc.edu

Jinjun Chen Faculty of Information and Communication Technologies, Swinburne University of Technology, Hawthorn, Melbourne, Australia 3122, jchen@swin.edu.au

Lei Chen Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, leichen@cse.ust.hk

Karim Chine Cloud Era Ltd, Cambridge, UK, karim.chine@polytechnique.org David Cohen Cloud Infrastructure Group, EMC Corporation, Cambridge, MA,

USA, david.cohen@emc.com Vincenzo D. Cunsolo University of Messina, Contrada di Dio, S. Agata, Messina,

Italy, vdcunsolo@unime.it Kemal A. Delic Hewlett-Packard Co., New York, NY, USA, kemal.delic@hp.com Mac Devine IBM Corporation, Research Triangle Park, NC, USA,

wdevine@us.ibm.com wdevine@us.ibm.com

Italy, sdistefano@unime.it Patrick Dreher Renaissance Computing Institute, Chapel Hill, NC, 27517 USA,

dreher@renci.org Kathleen Ericson Department of Computer Science, Colorado State University,

Fort Collins, CO, USA, ericson@cs.colostate.edu Liana Fong IBM Watson Research Center, Hawthorne, NY, USA,

llfong@us.ibm.com Ian Foster Argonne National Laboratory, Argonne, IL, USA; The University

of Chicago, Chicago, IL, USA, foster@anl.gov Marc Eduard Frîncu Institute e-Austria, Blvd. Vasile Parvan No 4 300223,

Room 045B, Timisoara, Romania, mfrincu@info.uvt.ro Borko Furht Department of Computer & Electrical Engineering and Computer

Science, Florida Atlantic University, Boca Raton, FL, USA, bfurht@fau.edu Wei Gao Services Computing Technology and System Lab; Cluster and Grid

Computing Lab, Huazhong University of Science and Technology, Wuhan, China, gaowei715@gmail.com

Giulio Giunta Department of Applied Science, University of Napoli Parthenope, Napoli, Italy, giulio.giunta@uniparthenope.it

Milt Halem Computer Science and Electrical Engineering Department, University of Maryland, Baltimore County, MD, USA, halem@umbc.edu

Mohammad Mehedi Hassan Department of Computer Engineering, Kyung Hee University, Global Campus, South Korea, hassan@khu.ac.kr

Nathan Henehan Senior Software Developer, NACS Solutions, Oberlin, OH, USA, nhenehan@gmail.com

Juan J. Hierro Telefónica Investigación y Desarrollo, Madrid, Spain, jhierro@tid.es

Dachuan Huang Services Computing Technology and System Lab; Cluster and Grid Computing Lab, Huazhong University of Science and Technology, Wuhan, China, hdc1112@gmail.com

Eui-Nam Huh Department of Computer Engineering, Kyung Hee University, Global Campus, South Korea, johnhuh@khu.ac.kr

Roman Iakymchuk AICES, RWTH, Aachen, Germany, iakymchuk@aices.rwth-aachen.de

Shadi Ibrahim Services Computing Technology and System Lab; Cluster and Grid Computing Lab, Huazhong University of Science and Technology, Wuhan, China, shadi@hust.edu.cn

Contributors xiii Hai Jin Services Computing Technology and System Lab; Cluster and Grid

Computing Lab, Huazhong University of Science and Technology, Wuhan, China, hjin@hust.edu.cn

Karuna P. Joshi Computer Science and Electrical Engineering Department, University of Maryland, Baltimore County, MD, USA, kjoshi1@umbc.edu

Burton S. Kaliski Jr. Office of the CTO, EMC Corporation, Hopkinton, MA, USA, burt.kaliski@emc.com

Hari Kalva Department of Computer Science and Engineering, FAU, Boca Raton, FL, USA, hari.kalva@fau.edu

Giuliano Laccetti Department of Mathematics and Applications, University of Napoli Federico II, Napoli, Italy, giuliano.laccetti@unina.it

Rui Li Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, cslr@cse.ust.hk

Wen-Syan Li SAP Technology Lab, Shanghai, China, wen-syan.li@sap.com Geng Lin IBM Alliance, Cisco Systems, San Francisco, CA, USA,

gelin@cisco.com Xiao Liu Faculty of Information and Communication Technologies, Swinburne

University of Technology, Hawthorn, Melbourne, Australia 3122, xliu@groupwise.swin.edu.au

Yanbin Liu IBM Watson Research Center, Hawthorne, NY, USA, ygliu@us.ibm.com

Anthony M. Middleton LexisNexis Risk Solutions, Boca Raton, FL, USA, tony.middleton@lexisnexis.com

Raffaele Montella Department of Applied Science, University of Napoli Parthenope, Napoli, Italy, raffaele.montella@uniparthenope.it

Jeff Napper Vrije Universiteit, Amsterdam, Netherlands, jnapper@cs.vu.nl Phuong Nguyen Computer Science and Electrical Engineering Department,

University of Maryland, Baltimore County, MD, USA, phuong3@umbc.edu Jeffrey M. Nick Office of the CTO, EMC Corporation, Hopkinton, MA, USA,

jeff.nick@emc.com Sangyoon Oh WISE Research Lab, School of Information and Communication

Engineering, Ajou University, Suwon, South Korea, syoh@ajou.ac.kr Shrideep Pallickara Department of Computer Science, Colorado State University,

Fort Collins, CO, USA, shrideep@cs.colostate.edu Sangmi Lee Pallickara Department of Computer Science, Colorado State

University, Fort Collins, CO, USA, sangmi@cs.colostate.edu University, Fort Collins, CO, USA, sangmi@cs.colostate.edu

Boca Raton, FL, USA, pandya@fau.edu Manish Parashar NSF CAC, Rutgers University, Piscataway, NJ, USA,

parashar@rutgers.edu Thomas Phan Microsoft Corporation, Washington, DC, USA,

thomas.phan@acm.org Marlon Pierce Community Grids Lab, Indiana University, Bloomington, IN,

USA, mpierce@cs.indiana.edu Álvaro Polo Telefónica Investigación y Desarrollo, Madrid, Spain, apv@tid.es Antonio Puliafito University of Messina, Contrada di Dio, S. Agata, Messina,

Italy, apuliafito@unime.it Jeff A. Riley Hewlett-Packard Co., New York, NY, USA, jeff.riley@hp.com Ivan Rodero NSF CAC, Rutgers University, Piscataway, NJ, USA,

irodero@rutgers.edu Luis Rodero-Merino INRIA-ENS, INRIA, Lyon, France,

luis.rodero-merino@ens-lyon.fr S. Masoud Sadjadi CIS, Florida International University, Miami, FL, USA,

sadjadi@cis.fiu.edu Marco Scarpa University of Messina, Contrada di Dio, S. Agata, Messina, Italy,

mscarpa@unime.it Eric Sills North Carolina State University, Raleigh, NC 27695, USA,

edsills@ncsu.edu Mikael Fernandus Simalango WISE Research Lab, Ajou University, Suwon,

South Korea, mikael@ajou.ac.kr Vivek Somashekarappa Armellini Inc., Palm City, FL, USA,

vsomashekar@gmail.com Luis M. Vaquero Telefónica Investigación y Desarrollo, Madrid, Spain,

lmvg@tid.es David Villegas CIS, Florida International University, Miami, FL, USA,

dvill013@cis.fiu.edu Mladen A. Vouk Department of Computer Science, North Carolina State

University, Box 8206, Raleigh, NC 27695, USA, vouk@ncsu.edu Song Wu Services Computing Technology and System Lab; Cluster and Grid

Computing Lab, Huazhong University of Science and Technology, Wuhan, China, wusong@mail.hust.edu.cn

Contributors xv Yun Yang Faculty of Information and Communication Technologies, Swinburne

University of Technology, Hawthorn, Melbourne, Australia 3122, yyang@groupwise.swin.edu.au

Yelena Yesha Computer Science and Electrical Engineering Department, University of Maryland, Baltimore County, MD, USA, yeyesha@csee.umbc.edu

Yaacov Yesha Computer Science and Electrical Engineering Department, University of Maryland, Baltimore County, MD, USA, yayesha@umbc.edu

Dong Yuan Faculty of Information and Communication Technologies, Swinburne University of Technology, Hawthorn, Melbourne, Australia 3122, dyuan@groupwise.swin.edu.au

Gaofeng Zhang Faculty of Information and Communication Technologies, Swinburne University of Technology, Hawthorn, Melbourne, Australia 3122, gzhang@groupwise.swin.edu.au

Jinzy Zhu IBM Cloud Computing Center, China, jinzyzhu@cn.ibm.com

About the Editors

Borko Furht is a professor and chairman of the Department of Electrical & Computer Engineering and Computer Science at Florida Atlantic University (FAU) in Boca Raton, Florida. He is also Director of recently formed NSF-sponsored Borko Furht is a professor and chairman of the Department of Electrical & Computer Engineering and Computer Science at Florida Atlantic University (FAU) in Boca Raton, Florida. He is also Director of recently formed NSF-sponsored

Enablement. Before joining FAU, he was a vice president of research and a senior director of development at Modcomp (Ft. Lauderdale), a computer company of Daimler Benz, Germany; a professor at University of Miami in Coral Gables, Florida; and a senior researcher in the Institute Boris Kidric-Vinca, Yugoslavia. Professor Furht received a Ph.D. degree in electrical and computer engineering from the University of Belgrade. His current research is in multimedia systems, video coding and compression, 3D video and image systems, wireless multimedia, and Internet and cloud computing. He is presently Principal Investigator and Co-PI of several multiyear, multimillion-dollar projects, including NSF PIRE project and NSF High-Performance Computing Center. He is the author of numerous books and articles in the areas of multimedia, computer architecture, real-time computing, and operating systems. He is a founder and editor-in-chief of the Journal of Multimedia Tools and Applications (Springer). He has received several technical and publishing awards, and has consulted for many high-tech companies including IBM, Hewlett-Packard, Xerox, General Electric, JPL, NASA, Honeywell, and RCA. He has also served as a consultant to various colleges and universities. He has given many invited talks, keynote lectures, seminars, and tutorials. He served on the Board of Directors of several high-tech companies.

Armando J. Escalante is Senior Vice President and Chief Technology Officer of Risk Solutions for the LexisNexis Group, a division of Reed Elsevier. In this position, Escalante is responsible for technology development, information systems and operations. Previously, Escalante was Chief Operating Officer for Seisint, a

About the Editors xix privately owned company, which was purchased by LexisNexis in 2004. In this

position, he was responsible for Technology, Development and Operations. Prior to 2001, Escalante served as Vice President of Engineering and Operations for Diveo Broadband Networks, where he led world-class Data Centers located in the U.S. and Latin America. Before Diveo Broadband Networks, Escalante was VP for one of the fastest growing divisions of Vignette Corporation, an eBusiness software leader. Escalante earned his bachelor’s degree in electronic engineering at the USB in Caracas, Venezuela and a master’s degree in computer science from Stevens Institute of Technology, as well as a master’s degree in business administration from West Coast University.

Part I

Technologies and Systems

Chapter 1 Cloud Computing Fundamentals

Borko Furht

1.1 Introduction

In the introductory chapter we define the concept of cloud computing and cloud services, and we introduce layers and types of cloud computing. We discuss the differences between cloud computing and cloud services. New technologies that enabled cloud computing are presented next. We also discuss cloud computing features, standards, and security issues. We introduce the key cloud computing plat- forms, their vendors, and their offerings. We discuss cloud computing challenges and the future of cloud computing.

Cloud computing can be defined as a new style of computing in which dynam- ically scalable and often virtualized resources are provided as a services over the Internet. Cloud computing has become a significant technology trend, and many experts expect that cloud computing will reshape information technology (IT) pro- cesses and the IT marketplace. With the cloud computing technology, users use

a variety of devices, including PCs, laptops, smartphones, and PDAs to access programs, storage, and application-development platforms over the Internet, via ser- vices offered by cloud computing providers. Advantages of the cloud computing technology include cost savings, high availability, and easy scalability.

Figure 1.1 , adapted from Voas and Zhang ( 2009 ), shows six phases of computing paradigms, from dummy terminals/mainframes, to PCs, networking computing, to grid and cloud computing.

In phase 1, many users shared powerful mainframes using dummy terminals. In phase 2, stand-alone PCs became powerful enough to meet the majority of users’ needs. In phase 3, PCs, laptops, and servers were connected together through local networks to share resources and increase performance. In phase 4, local net- works were connected to other local networks forming a global network such as the Internet to utilize remote applications and resources. In phase 5, grid comput- ing provided shared computing power and storage through a distributed computing

B. Furht ( B ) Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA e-mail: bfurht@fau.edu

4 B. Furht Phases

1. Mainframe Computing

User

Mainframe 2. PC

3. Network Server Computing

4. Internet Server Computing

5. Grid Computing

6. Cloud Cloud Computing

User

PC

Fig. 1.1 Six computing paradigms – from mainframe computing to Internet computing, to grid computing and cloud computing (adapted from Voas and Zhang ( 2009 ))

system. In phase 6, cloud computing further provides shared resources on the Internet in a scalable and simple way.

Comparing these six computing paradigms, it looks like that cloud computing is a return to the original mainframe computing paradigm. However, these two paradigms have several important differences. Mainframe computing offers finite computing power, while cloud computing provides almost infinite power and capac- ity. In addition, in mainframe computing dummy terminals acted as user interface devices, while in cloud computing powerful PCs can provide local computing power and cashing support.

1.1.1 Layers of Cloud Computing

Cloud computing can be viewed as a collection of services, which can be presented as a layered cloud computing architecture, as shown in Fig. 1.2 [Jones XXXX]. The

1 Cloud Computing Fundamentals 5 Fig. 1.2 Layered architecture

of Cloud Computing (adapted Application from Jones)

SaaS

Platform PaaS

Infrastructure IaaS

Virtualization

Servers and Storage dSaaS

services offered through cloud computing usually include IT services referred as to SaaS (Software-as-a-Service), which is shown on top of the stack. SaaS allows users to run applications remotely from the cloud.

Infrastructure-as-a-service (IaaS) refers to computing resources as a service. This includes virtualized computers with guaranteed processing power and reserved bandwidth for storage and Internet access.

Platform-as-a-Service (PaaS) is similar to IaaS, but also includes operating sys- tems and required services for a particular application. In other words, PaaS is IaaS with a custom software stack for the given application.

The data-Storage-as-a-Service (dSaaS) provides storage that the consumer is used including bandwidth requirements for the storage. An example of Platform-as-aService (PaaS) cloud computing is shown in Fig. 1.3 [“Platform as a Service,” http://www.zoho.com/creator/paas.html ]. The PaaS pro- vides Integrated Development Environment (IDE) including data security, backup and recovery, application hosting, and scalable architecture.

According to Chappell ( 2008 ) there are three categories of cloud services, as illustrated in Fig. 1.4 . Figure 1.4a shows the cloud service SaaS, where the entire application is running in the cloud. The client contains a simple browser to access the application. A well-known example of SaaS is salesforce.com.

Figure 1.4b illustrates another type of cloud services, where the application runs on the client; however it accesses useful functions and services provided in the cloud. An example of this type of cloud services on the desktop is Apple’s iTunes.

6 B. Furht

Business Users

Business Applications

IDE

Developers Integrated Development Environment

Cloud

Fig. 1.3 The concept of Platform-as-a-Service, Zoho Creator (adapted from “Platform as a Service,” http://www.zoho.com/creator/paas.html )

a) Software_as_a_Service

b) Attached Services

Cloud Platform

Cloud

Cloud

Application Application

Extra Functions

Platform

On- Browser/ premises

Developers Fig. 1.4 The categories of cloud services (adopted from Chappell ( 2008 ))

Users

Users

The desktop application plays music, while the cloud service is used to purchase

a new audio and video content. An enterprise example of this cloud service is Microsoft Exchange Hosted Services. On-premises Exchange Server is using added services from the cloud including spam filtering, archiving, and other functions.

1 Cloud Computing Fundamentals 7 Finally, Fig. 1.4c shows a cloud platform for creating applications, which is used

by developers. The application developers create a new SaaS application using the cloud platform.

1.1.2 Types of Cloud Computing

There are three types of cloud computing (“Cloud Computing,” Wikipedia, http://en.wikipedia.org/wiki/Cloud_computing ): (a) public cloud, (b) private cloud,

and (c) hybrid cloud, as illustrated in Fig. 1.5 .

Fig. 1.5 Three types of cloud computing

Public External

Cloud

Hybrid Public External

Public

Private Internal

User

User User

In the public cloud (or external cloud) computing resources are dynamically pro- visioned over the Internet via Web applications or Web services from an off-site third-party provider. Public clouds are run by third parties, and applications from different customers are likely to be mixed together on the cloud’s servers, storage systems, and networks.

Private cloud (or internal cloud) refers to cloud computing on private networks. Private clouds are built for the exclusive use of one client, providing full control over data, security, and quality of service. Private clouds can be built and managed by a company’s own IT organization or by a cloud provider.

A hybrid cloud environment combines multiple public and private cloud mod- els. Hybrid clouds introduce the complexity of determining how to distribute applications across both a public and private cloud.

8 B. Furht

1.1.3 Cloud Computing Versus Cloud Services

In this section we present two tables that show the differences and major attributes of cloud computing versus cloud services (Jens, 2008 ). Cloud computing is the IT foundation for cloud services and it consists of technologies that enable cloud

services. The key attributes of cloud computing are shown in Table 1.1 . In Key attributes of cloud services are summarized in Table 1.2 (Jens, 2008 ).

Table 1.1 Key Cloud Computing Attributes (adapted from Jens ( 2008 ))

Attributes

Description

Infrastructure systems It includes servers, storage, and networks that can scale as

per user demand.

Application software It provides Web-based user interface, Web services APIs, and a rich variety of configurations. Application development and

It supports the development and integration of cloud deployment software

application software.

System and application It supports rapid self-service provisioning and configuration management software

and usage monitoring.

IP networks They connect end users to the cloud and the infrastructure

components.

Table 1.2 Key Attributes of Cloud Services (adapted from Jens ( 2008 ))

Attributes

Description

Offsite. Third-party provider In the cloud execution, it is assumed that third-party provides services. There is also a possibility of in-house cloud service delivery.

Accessed via the Internet Services are accessed via standard-based, universal network access. It can also include security and quality-of-service options.

Minimal or no IT skill required There is a simplified specification of requirements. Provisioning

It includes self-service requesting, near real-time deployment, and dynamic and fine-grained scaling. Pricing

Pricing is based on usage-based capability and it is

fine-grained.

User interface User interface include browsers for a variety of devices and

with rich capabilities.

System interface System interfaces are based on Web services APIs providing a standard framework for accessing and integrating among cloud services.

Shared resources Resources are shared among cloud services users; however via configuration options with the service, there is the ability to customize.

1 Cloud Computing Fundamentals 9

1.2 Enabling Technologies

Key technologies that enabled cloud computing are described in this section; they include virtualization, Web service and service-oriented architecture, service flows and workflows, and Web 2.0 and mashup.

1.2.1 Virtualization

The advantage of cloud computing is the ability to virtualize and share resources among different applications with the objective for better server utilization.

Figure 1.6 shows an example Jones]. In non-cloud computing three independent platforms exist for three different applications running on its own server. In the cloud, servers can be shared, or virtualized, for operating systems and applications resulting in fewer servers (in specific example two servers).

Fig. 1.6 An example of virtualization: in non-cloud

Application computing there is a need for

three servers; in the cloud

Application computing, two servers are

Application

OS 2

used (adapted from Jones)

OS 1

Server Y OS 3

Server X

Server Z

Application Application

Server A

Server B Cloud

Virtualization technologies include virtual machine techniques such as VMware and Xen, and virtual networks, such as VPN. Virtual machines provide virtual- ized IT-infrastructures on-demand, while virtual networks support users with a customized network environment to access cloud resources.

10 B. Furht

1.2.2 Web Service and Service Oriented Architecture

Web Services and Service Oriented Architecture (SOA) are not new concepts; however they represent the base technologies for cloud computing. Cloud services are typically designed as Web services, which follow industry standards including WSDL, SOAP, and UDDI. A Service Oriented Architecture organizes and man- ages Web services inside clouds (Vouk, 2008 ). A SOA also includes a set of cloud services, which are available on various distributed platforms.

1.2.3 Service Flow and Workflows

The concept of service flow and workflow refers to an integrated view of service- based activities provided in clouds. Workflows have become one of the important areas of research in the field of database and information systems (Vouk, 2008 ).

1.2.4 Web 2.0 and Mashup

Web 2.0 is a new concept that refers to the use of Web technology and Web design to enhance creativity, information sharing, and collaboration among users (Wang, Tao,

RSS Feed Server

Portal Database Server

Mashup

Mapping

FAT Client Server Web 1.0

User Interface

Gadget

Components

Server 1 Gadget

Gadget phone

Repository

Server n

Fig. 1.7 Cloud computing architecture uses various components at different levels (adapted from

1 Cloud Computing Fundamentals 11 & Kunze, 2008 ). On the other hand, Mashup is a web application that combines data

from more than one source into a single integrated storage tool. Both technologies are very beneficial for cloud computing.

Figure 1.7 shows a cloud computing architecture, adapted from Hutchinson and Ward ( 2009 ), in which an application reuses various components. The components in this architecture are dynamic in nature, operate in a SaaS model, and leverage SOA. The components closer to the user are smaller in nature and more reusable. The components in the center contain aggregate and extend services via mashup servers and portals.Data from one service (such as addresses in a database) can be mashed up with mapping information (such as Yahoo or Google maps) to produce an aggregated view of the information.

1.3 Cloud Computing Features

Cloud computing brings a number of new features compared to other computing paradigms (Wang et al., 2008 ; Grossman, 2009 ). There are briefly described in this section.

• Scalability and on-demand services Cloud computing provides resources and services for users on demand. The resources are scalable over several data centers.

• User-centric interface Cloud interfaces are location independent and can be accesses by well established interfaces such as Web services and Internet browsers.

• Guaranteed Quality of Service (QoS) Cloud computed can guarantee QoS for users in terms of hardware/CPU performance, bandwidth, and memory capacity.

• Autonomous system The cloud computing systems are autonomous systems managed transparently to users. However, software and data inside clouds can be automatically recon- figured and consolidated to a simple platform depending on user’s needs.

• Pricing Cloud computing does not require up-from investment. No capital expenditure is required. Users pay for services and capacity as they need them.

1.3.1 Cloud Computing Standards

Cloud computing standards have not been yet fully developed; however a num-

12 B. Furht Table 1.3 Cloud computing standards (“Cloud Computing,” Wikipedia, http://en.wikipedia.org/

wiki/Cloud_computing ) Communications: HTTP, XMPP

Security: OAuth, OpenID, SSL/TLS Applications

Syndication: Atom

Client

Browsers: AJAX Offline:’ HTML5

Implementations

Virtualization: OVF

Platform Solution stacks: LAMP Service

Data: XML, JSON Web services: REST

of cloud computing (“Cloud Computing,” Wikipedia, http://en.wikipedia.org/wiki/ Cloud_computing ). Table 1.3 illustrates several of these open standards, which are currently used in cloud computing.

1.3.2 Cloud Computing Security

One of the critical issues in implementing cloud computing is taking virtual machines, which contain critical applications and sensitive data, to public and shared cloud environments. Therefore, potential cloud computing users are concerned about the following security issues (“Cloud Computing Security,” Third Brigade, www.cloudreadysecurity.com .).

• Will the users still have the same security policy control over their applications and services? • Can it be proved to the organization that the system is still secure and meets SLAs? • Is the system complaint and can it be proved to company’s auditors?

In traditional data centers, the common approaches to security include perime- ter firewall, demilitarized zones, network segmentation, intrusion detection and prevention systems, and network monitoring tools.

The security requirements for cloud computing providers begins with the same techniques and tools as for traditional data centers, which includes the application of

a strong network security perimeter. However, physical segmentation and hardware- based security cannot protect against attacks between virtual machines on the same server. Cloud computing servers use the same operating systems, enterprise and Web applications as localized virtual machines and physical servers. Therefore, an attacker can remotely exploit vulnerabilities in these systems and applications. In addition, co-location of multiple virtual machines increases the attack surface and risk to MV-to-VM compromise. Intrusion detection and prevention systems need to

be able to detect malicious activity in the VM level, regardless of the location of the

1 Cloud Computing Fundamentals 13 VM within the virtualized cloud environment (“Cloud Computing Security,” Third

Brigade, www.cloudreadysecurity.com .).

In summary, the virtual environments that deploy the security mechanisms on virtual machines including firewalls, intrusion detection and prevention, integrity monitoring, and log inspection, will effectively make VM cloud secure and ready for deployment.

1.4 Cloud Computing Platforms

Cloud computing has great commercial potential. According to market research firm IDC, IT cloud services spending will grow from about $16B in 2008 to about $42B in 2012 and to increase its share of overall IT spending from 4.2% to 8.5%.

Table 1.4 presents key players in cloud computing platforms and their key offerings.

Table 1.4 Key Players in Cloud Computing Platforms (adapted from Lakshmanan ( 2009 ))

Cloud computing

Year of

Key offerings Amazon. com

Company platform

launch

AWS (Amazon

Infrastructure as a service (Storage, Web Services)

Computing, Message queues, Datasets, Content distribution)

Application platform as a service (.Net, SQL data services) Google

Microsoft Azure

Google App.

Web Application Platform as a Engine

service (Python run time environment)

IBM Blue Cloud

Virtualized Blue cloud data center Salesforce.com

Force.com

Proprietary 4GL Web application framework as an on Demand platform

Table 1.5 compares three cloud computing platforms, Amazon, Google, and Microsoft, in terms of their capabilities to map to different development models and scenarios (“Which Cloud Platform is Right for You?,” www.cumulux.com .).

1.4.1 Pricing

Pricing for cloud platforms and services is based on three key dimensions: (i) storage, (ii) bandwidth, and (iii) compute.

Storage is typically measured as average daily amount of data stored in GB over

a monthly period.

14 B. Furht Table 1.5 Cloud Computing Platforms and Different Scenarios (adapted from “Which Cloud

Platform is Right for You?,” www.cumulux.com .) (1) Scenario

On-premise application unchanged in the cloud Characteristics Multiple red legacy, java or .NET based application Amazon

Threat the machine as another server in the data center and do the necessary changes to configuration Google

Needs significant refactoring of application and data logic for existing Java application Microsoft

If existing app is ASP.NET application, then re-factor data, otherwise refactoring effort can be quite significant depending on the complexity

(2) Scenario Scalable Web application

Characteristics Moderate to high Web application with a back-end store and load balancing Amazon

Threat the machine instance as another server in the data center and do the necessary changes to configuration. But scalability and elasticity is manual configuration

Google Use dynamically scalable features of AppEngine and scripting technologies to build rich applications Microsoft

Build scalable Web applications using familiar .NET technologies. Scaling up/down purely driven by configuration.

(3) Scenario Parallel processing computational application Characteristics Automated long running processing with little to no user interaction. Amazon

Need to configure multiple machine instances depending on the scale needed and manage the environments. Google

Platform has minimal built-in support for building compute heavy applications. Certain application scenarios, such as image manipulation, are easier to develop with built-in platform features.

Microsoft With worker roles and storage features like Queues and blobs, it is easy to build a compute heavy application that can be managed and controlled for scalability and elasticity.

(4) Scenario

Application in the cloud interacts with on-premise data

Characteristics Cloud based applications interacting with on-premise apps for managing

transactions of data Amazon

Applications in EC2 server cloud can easily be configured to interact with applications running on premise. Google

No support from the platform to enable this scenario. Possible through each app using intermediary store to communicate. Microsoft

From features like Service Bus to Sync platform components it is possible to build compelling integration between the two environments.

(5) Scenario Application in the cloud interacts with on-premise application Characteristics On-premise applications Amazon

Applications in EC2 server cloud can easily be configured to interact with applications running on premise. Google

No support from the platform to enable this scenario. Possible through each app using intermediary store to communicate. Microsoft

From features like Service Bus to Sync platform components it is possible to build compelling integration between the two environments.

1 Cloud Computing Fundamentals 15 Bandwidth is measured by calculating the total amount of data transferred

in and out of platform service through transaction and batch processing. Generally, data transfer between services within the same platform is free in many platforms.

Compute is measured as the time units needed to run an instance, or application, or machine to servicing requests. Table 6 compares pricing for three major cloud computing platforms.

In summary, by analyzing the cost of cloud computing, depending on the appli- cation characteristics the cost of deploying an application could vary based on the selected platform. From Table 1.6 , it seems that the unit pricing for three major plat- forms is quite similar. Besides unit pricing, it is important to translate it into monthly application development, deployments and maintenance costs.

Table 1.6 Pricing comparison for major cloud computing platforms (adapted from “Which Cloud Platform is Right for You?,” www.cumulux.com .)

Resource UNIT

Google Microsoft Stored data

Amazon

$0.15 $0.15 Storage transaction

GB per month

$0.10 Outgoing bandwidth

Per 10 K requests

$0.12 $0.15 Incoming bandwidth

GB $0.10 – $0.17

$0.10 $0.10 Compute time

GB $0.10

Instance Hours

1.4.2 Cloud Computing Components and Their Vendors

The main elements comprising cloud computing platforms include com- puter hardware, storage, infrastructure, computer software, operating systems, and platform virtualization. The leading vendors providing cloud comput-

ing components are shown in Table 1.7 (“Cloud Computing,” Wikipedia, http://en.wikipedia.org/wiki/Cloud_computing ).

Table 1.7 The leading vendors of cloud computing components

Cloud computing components

Vendors

Computer hardware

Dell, HP, IBM, Sun

Storage

Sun, EMC, IBM

Infrastructure Cisco, Juniper Networks, Brocade Communication Computer software

3tera. Eucalyptus. G-Eclipse. Hadoop Operating systems

Solaris, AIX, Linux (Red Hat, Ubuntu) Platform virtualization

Citrix, VMWare, IBM, Xen, Linux KVM, Microsoft, Sun xVM

16 B. Furht

1.5 Example of Web Application Deployment

In this section we present an example how the combination of virtualization and on of self service facilitate application deployment (Sun Microsystems, 2009 ). In this example we consider a two-tier Web application deployment using cloud, as

illustrated in Fig. 1.8 .

Library Deploy

Configure Pattern

Load Load

Load

Balancer Balancer Balancer

Web service Web service Web service Database

APACHE APACHE MySQL

APACHE

Database Appl. Web service

Database

MySQL Storage APACHE

MySQL

Fig. 1.8 Example of the deployment of an application into a two-tier Web server architecture using cloud computing (adapted from Sun Microsystems ( 2009 ))

The following steps comprise the deployment of the application: • The developer selects a load balancer, Web server, and database server appliances

from a library of preconfigured virtual machine images. • The developer configures each component to make a custom image. The load balancer is configured, the Web server is populated with its static content by uploading it to the storage cloud, and the database server appliances are populated with dynamic content for the site.

• The developer than layers custom code into the new architecture, in this way making the components meet specific application requirements. • The developer chooses a pattern that takes the images for each layer and deploys them, handling networking, security, and scalability issues.

The secure, high-availability Web application is up and running. When the appli- cation needs to be updated, the virtual machine images can be updated, copied across the development chain, and the entire infrastructure can be redeployed.

In this example, a standard set of components can be used to quickly deploy an application. With this model, enterprise business needs can be met quickly, with- out the need for the time-consuming, manual purchase, installation, cabling, and configuration of servers, storage, and network infrastructure.

1 Cloud Computing Fundamentals 17 Small and medium enterprises were the early adopters to cloud computing.

However, there are recently a number of examples of cloud computing adoptions in the large enterprises. Table 1.8 illustrates three examples of cloud computing use in the large enterprises (Lakshmanan, 2009 ).

Table 1.8 Cloud computing examples in large enterprises

Enterprise Scenario

Benefits Eli Lilly

Usage

R&D High

Quick deployment time Performance

Amazon server and

at a lower cost. Computing

storage cluster for

drug discovery analysis and modeling.

New York Times Data Conversion Conversion of archival Rapid provisioning and

articles (3 million)

higher elasticity on into new data formats

the infrastructure using Amazon elastic

resources.

compute services.

Pitney Bowes B2B Application

Hosted model mail

Flexibility at a lower printing application

cost and new business for clients. Uses MS

opportunity.

Azure.net and SQL services for the hosted model option (2009 Go live).

1.6 Cloud Computing Challenges

In summary, the new paradigm of cloud computing provides a number of benefits and advantages over the previous computing paradigms and many organizations are adopting it. However, there are still a number of challenges, which are currently addressed by researchers and practitioners in the field (Leavitt, 2009 ). They are briefly presented below.

1.6.1 Performance

The major issue in performance can be for some intensive transaction-oriented and other data-intensive applications, in which cloud computing may lack adequate performance. Also, users who are at a long distance from cloud providers may experience high latency and delays.

1.6.2 Security and Privacy

Companies are still concerned about security when using cloud computing. Customers are worried about the vulnerability to attacks, when information and

18 B. Furht critical IT resources are outside the firewall. The solution for security assumes that

that cloud computing providers follow standard security practices, as described in Section 1.3.2 .

1.6.3 Control

Some IT departments are concerned because cloud computing providers have a full control of the platforms. Cloud computing providers typically do not design platforms for specific companies and their business practices.

1.6.4 Bandwidth Costs

With cloud computing, companies can save money on hardware and software; how- ever they could incur higher network bandwidth charges. Bandwidth cost may be low for smaller Internet-based applications, which are not data intensive, but could significantly grow for data-intensive applications.

1.6.5 Reliability

Cloud computing still does not always offer round-the-clock reliability. There were cases where cloud computing services suffered a few-hours outages.

In the future, we can expect more cloud computing providers, richer services, established standards, and best practices. In the research arena, HP Labs, Intel, and Yahoo have launched the distributed Cloud Research Test Bad, with facilities in Asia, Europe, and North America, with the objective to develop innovations including cloud computing specific chips. IBM has launched the Research Computing Cloud, which is an on-demand, globally accessible set of computing resources that support business processes.

1.7 Cloud Computing in the Future

In summary, cloud computing is definitely a type of computing paradigm/architecture that will remain for a long time to come. In the near future, cloud computing can emerge in various directions. One possible scenario for the future is that an enterprise may use a distributed hybrid cloud as illustrated

in Fig. 1.9 . According to this scenario, the enterprise will use the core applications on its private cloud, while some other applications will be distributed on several private clouds, which are optimized for specific applications.

1 Cloud Computing Fundamentals 19

Public Cloud Public Cloud Amazon AMS

MS Azure

High Computing and Failover Infrastructure

B2B Applications

Private Cloud Public Cloud Google App Engine

Core Consumer Applications

Applications

Enterprise

Fig. 1.9 Distributed hybrid cloud architecture (adapted from Lakshmanan ( 2009 ))

References

Chappell, D. (August 2008). A short introduction to cloud platforms: An enterprise-oriented view. San Francisco, CA: Chappel and Associates. Grossman, R. L. (March/April 2009). The case for cloud computing. IEEE ITPro, 23–27. Hutchinson, C., & Ward, J. (March/April 2009). Navigation the next-generation application

architecture. IEEE ITPro, 18–22. Jens, F. (September 2008). Defining

and cloud computing . http://blogs.idc.com/ie/?p=190 . Jones, M. T. Cloud computing with linux. www.ibm.com/developerworks/linux/library/l-cloud- computing . Lakshmanan, G. (April 2009). Cloud computing – Relevance to enterprise. Infosys White Paper. Leavitt, N. (January 2009). Is cloud computing really ready for prime time? IEEE Computer,

cloud services

15–20. Sun Microsystems (June 2009). Introduction to cloud computing architecture. White Paper, Sun Microsystems. Voas, J., & Zhang, J. (March/April 2009). Cloud computing: New wine or just a new bottle? IEEE ITPro , 15–17. Vouk, M. A. (June 2008). Cloud computing – Issues, research and implementations. Proceedings of the ITI 30th International Conference on Information Technology Interfaces, Cavtat, Croatia , 31–40.

Wang, L., Tao, J., & Kunze, M. (2008). Scientific cloud computing: Early definition and experi- ence. Proceedings of the 10th IEEE International Conference on High Performance Computing and Communications, Austin, TX , 825–830.

Chapter 2 Cloud Computing Technologies and Applications

Jinzy Zhu

2.1 Cloud Computing: IT as a Service