Convergence of Cloud with AI for Big Data Analytics E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Advances in Learning Analytics for Intelligent Cloud-IoT Systems
• Sprache: Englisch

Seitenzahl: 600

Veröffentlichungsjahr: 2023

Beschreibung

CONVERGENCE of CLOUD with AI for BIG DATA ANALYTICS This book covers the foundations and applications of cloud computing, AI, and Big Data and analyses their convergence for improved development and services. The 17 chapters of the book masterfully and comprehensively cover the intertwining concepts of artificial intelligence, cloud computing, and big data, all of which have recently emerged as the next-generation paradigms. There has been rigorous growth in their applications and the hybrid blend of AI Cloud and IoT (Ambient-intelligence technology) also relies on input from wireless devices. Despite the multitude of applications and advancements, there are still some limitations and challenges to overcome, such as security, latency, energy consumption, service allocation, healthcare services, network lifetime, etc. Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation details all these technologies and how they are related to state-of-the-art applications, and provides a comprehensive overview for readers interested in advanced technologies, identifying the challenges, proposed solutions, as well as how to enhance the framework. Audience Researchers and post-graduate students in computing as well as engineers and practitioners in software engineering, electrical engineers, data analysts, and cyber security professionals.

Leseprobe

Table of Contents

Cover

Series Page

Title Page

Copyright Page

Preface

1 Integration of Artificial Intelligence, Big Data, and Cloud Computing with Internet of Things

1.1 Introduction

1.2 Roll of Artificial Intelligence, Big Data and Cloud Computing in IoT

1.3 Integration of Artificial Intelligence with the Internet of Things Devices

1.4 Integration of Big Data with the Internet of Things

1.5 Integration of Cloud Computing with the Internet of Things

1.6 Security of Internet of Things

1.7 Conclusion

References

2 Cloud Computing and Virtualization

2.1 Introduction to Cloud Computing

2.2 Virtualization

2.3 Conclusion

References

3 Time and Cost-Effective Multi-Objective Scheduling Technique for Cloud Computing Environment

3.1 Introduction

3.2 Literature Survey

3.3 Cloud Computing and Cloudlet Scheduling Problem

3.4 Problem Formulation

3.5 Cloudlet Scheduling Techniques

3.6 Cloudlet Scheduling Approach (CSA)

3.7 Simulation Results

3.8 Conclusion

References

4 Cloud-Based Architecture for Effective Surveillance and Diagnosis of COVID-19

4.1 Introduction

4.2 Related Work

4.3 Research Methodology

4.4 Survey Findings

4.5 Conclusion and Future Scope

References

5 Smart Agriculture Applications Using Cloud and IoT

5.1 Role of IoT and Cloud in Smart Agriculture

5.2 Applications of IoT and Cloud in Smart Agriculture

5.3 Security Challenges in Smart Agriculture

5.4 Open Research Challenges for IoT and Cloud in Smart Agriculture

5.5 Conclusion

References

6 Applications of Federated Learning in Computing Technologies

6.1 Introduction

6.2 Advantages of Federated Learning

6.3 Conclusion

References

7 Analyzing the Application of Edge Computing in Smart Healthcare

7.1 Internet of Things (IoT)

7.2 Edge Computing

7.3 Edge Computing and Real Time Analytics in Healthcare

7.4 Edge Computing Use Cases in Healthcare

7.5 Future of Healthcare and Edge Computing

7.6 Conclusion

References

8 Fog-IoT Assistance-Based Smart Agriculture Application

8.1 Introduction

Conclusion and Future Scope

References

9 Internet of Things in the Global Impacts of COVID-19

9.1 Introduction

9.2 COVID-19 – Misconceptions

9.3 Global Impacts of COVID-19 and Significant Contributions of IoT in Respective Domains to Counter the Pandemic

9.4 Conclusions

References

10 An Efficient Solar Energy Management Using IoT-Enabled Arduino-Based MPPT Techniques

10.1 Introduction

10.2 Impact of Irradiance on PV Efficiency

10.3 Design and Implementation

10.4 Result and Discussions

10.5 Conclusions

References

11 Axiomatic Analysis of Pre-Processing Methodologies Using Machine Learning in Text Mining

11.1 Introduction

11.2 Text Pre-Processing – Role and Characteristics

11.3 Modern Pre-Processing Methodologies and Their Scope

11.4 Text Stream and Role of Clustering in Social Text Stream

11.5 Social Text Stream Event Analysis

11.6 Embedding

11.7 Description of Twitter Text Stream

11.8 Experiment and Result

11.9 Applications of Machine Learning in IoT (Internet of Things)

11.10 Conclusion

References

12 APP-Based Agriculture Information System for Rural Farmers in India

12.1 Introduction

12.2 Motivation

12.3 Related Work

12.4 Proposed Methodology and Experimental Results Discussion

12.5 Conclusion and Future Work

References

13 SSAMH – A Systematic Survey on AI-Enabled Cyber Physical Systems in Healthcare

13.1 Introduction

13.2 The Architecture of Medical Cyber-Physical Systems

13.3 Artificial Intelligence-Driven Medical Devices

13.4 Certification and Regulation Issues

13.5 Big Data Platform for Medical Cyber-Physical Systems

13.6 The Emergence of New Trends in Medical Cyber-Physical Systems

13.7 Eminence Attributes and Challenges

13.8 High-Confidence Expansion of a Medical Cyber-Physical Expansion

13.9 Role of the Software Platform in the Interoperability of Medical Devices

13.10 Clinical Acceptable Decision Support Systems

13.11 Prevalent Attacks in the Medical Cyber-Physical Systems

13.12 A Suggested Framework for Medical Cyber-Physical System

13.13 Conclusion

References

14 ANN-Aware Methanol Detection Approach with CuO-Doped SnO

2

in Gas Sensor

14.1 Introduction

14.2 Network Architectures

References

15 Detecting Heart Arrhythmias Using Deep Learning Algorithms

15.1 Introduction

15.2 Motivation

15.3 Literature Review

15.4 Proposed Approach

15.5 Experimental Results of Proposed Approach

15.6 Conclusion and Future Scope

References

16 Artificial Intelligence Approach for Signature Detection

16.1 Introduction

16.2 Literature Review

16.3 Problem Definition

16.4 Problem Definition

16.5 Result Analysis

16.6 Conclusion

References

17 Comparison of Various Classification Models Using Machine Learning to Predict Mobile Phones Price Range

17.1 Introduction

17.2 Materials and Methods

17.3 Application of the Model

17.4 Results and Comparison

17.5 Conclusion and Future Scope

References

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Recent Artificial Intelligence based Internet of Things applicatio...

Table 1.2 Table of differences between the internet of things and artificial...

Table 1.3 Comparisons of big data applications.

Table 1.4 Characteristics of the internet of things and cloud computing.

Table 1.5 Internet of Things security requirements.

Chapter 2

Tabel 2.1 Timeline of cloud computing [3].

Table 2.2 List of services in major cloud vendors.

Table 2.3 Existing scheduling algorithms.

Table 2.4 Configuration settings for simulation.

Chapter 3

Table 3.1 Cloudlet length.

Table 3.2 Scheduling different cloudlets on three VMs.

Table 3.3 Scheduling 100 cloudlets on different numbers of VMs.

Table 3.4 Scheduling 500 cloudlets on different numbers of VMs.

Table 3.5 Scheduling 1000 cloudlets on different numbers of VMs.

Table 3.6 Scheduling 2000 cloudlets on different numbers of VMs.

Table 3.7 Scheduling large numbers of cloudlets on different numbers of VMs....

c04

Table 4.1 Reliability test for factors in relevance to Cloud-based solution ...

Table 4.2 KMO and Bartlett’s test.

Chapter 5

Table 5.1 Comparative study of smart agriculture and IoT and cloud related p...

Chapter 7

Table 7.1 Cloud vs. fog vs. edge.

Chapter 8

Table 8.1 Difference between cloud fog and edge computing.

Chapter 10

Table 10.1 Detailed description of Solarex MSX-60 panel.

Table 10.2 Component specifications for the BUCK converter.

Table 10.3 Various features of ATmega328P.

Table 10.4 Comparison of simulated MPPs with measured power and efficiencies...

Table 10.5 An assessment of the proposed method to various MPPT techniques....

Chapter 11

Table 11.1 Summary of text pre-processing approaches.

Table 11.2 Summary of collected statistics.

Chapter 12

Table 12.1 Energy analysis when the application is used in smartphone.

Chapter 13

Table 13.1 Classification of MCPS architecture.

Chapter 14

Table 14.1 Variation of sensitivity with concentration for SnO-

2

based 1% Cu...

Table 14.2 Compare different Sensitivity of different transfer function in ...

Table 14.3 Different types of error in different transfer network function ...

Table 14.4 Compare different sensitivity of different network transfer func...

Table 14.5 Different types of error in different transfer function for Leve...

Chapter 15

Table 15.1 Summary of existing work for heart disease.

Table 15.2 Technique used with their advantages and issues.

Table 15.3 Summary of future work over existing work.

Table 15.4 Beat annotations

Table 15.5 Training performance of DNN, CNN and LSTM.

Table 15.6 Testing performance of DNN, CNN and LSTM.

Table 15.7 Comparison of proposed algorithms with existing in context of ac...

Chapter 16

Table 16.4.2.1 Algorithm for the preprocessing of the images.

Table 16.4.2.2 Algorithm for the histogram of gradients.

Chapter 17

Table 17.1 Some of the dataset values.

Table 17.2 Comparison of various machine learning models.

List of Illustrations

Chapter 1

Figure 1.1 Flow diagram of AI, big data, and cloud computing integrated with...

Figure 1.2 Integration architecture of AI in IoT.

Figure 1.3 Integration architecture of big data with internet of things devi...

Figure 1.4 Architecture of cloud computing with internet of things.

Chapter 2

Figure 2.1 Generic architecture of cloud computing [4].

Figure 2.2 Market-oriented architecture of cloud computing [5].

Figure 2.3 Service and deployment models in cloud computing [6].

Figure 2.4 Virtual machine architecture.

Figure 2.5 Different levels for implementation of virtualization [7].

Figure 2.6 System architecture of task scheduling algorithm.

Figure 2.7 Calculation of makespan.

Figure 2.8 Calculation of energy consumption.

Chapter 3

Figure 3.1 Managing and provisioning of task request to the cloud resources....

Figure 3.2 Components of cloud computing environment.

c04

Figure 4.1 Proposed cloud-based network for management of COVID-19.

Figure 4.2 Working steps for the proposed cloud-based network.

Figure 4.3 An open-source factor analysis for analyzed 13 factors using Cron...

Figure 4.4 Survey summary to validate the proposed scenario of cloud-enabled...

Figure 4.5 Experimental setup for the proposed cloud-based network.

Figure 4.6 Point-to-point throughput received via proposed cloud-based archi...

Chapter 5

Figure 5.1 IoT and cloud applications in smart agriculture.

Figure 5.2 Cyberattacks on IoT applications in smart agriculture.

Figure 5.3 Open research challenges in smart agriculture.

Chapter 6

Figure 6.1 Cloud–MEC network architecture [2].

Figure 6.2 Privacy-protected federated learning framework for edge network c...

Chapter 7

Figure 7.1 Internet of Things.

Figure 7.2 D-to-D communication model.

Figure 7.3 D-to-C communication model.

Figure 7.4 D-to-G communication model.

Figure 7.5 Layered architecture of IoT.

Figure 7.6 IoT protocol stack.

Figure 7.7 FAR-EDGE reference architecture.

Figure 7.8 Intel-SAP joint reference architecture.

Figure 7.9 Integrated architecture for IoT and edge.

Figure 7.10 Fog computing architecture.

Figure 7.11 Automated edge computing framework.

Figure 7.12 Integrated edge computing and blockchain architecture.

Chapter 8

Figure 8.1 Basics of fog computing.

Figure 8.2 Fog, cloud, and edge comparison.

Figure 8.3 Merits of fog computing.

Figure 8.4 Fog computing with IoT.

Figure 8.5 Fog computing in agriculture.

Figure 8.6 Example of fog computing in healthcare.

Figure 8.7 Example of fog computing in smart cities.

Figure 8.8 Application of fog computing in education.

Figure 8.9 Model of smart agriculture.

Chapter 9

Figure 9.1 Global impacts of COVID-19 on different domains.

Figure 9.2 Global impact of COVID-19 on economic activity [54].

Figure 9.3 Impact of COVID-19 on global education (as on June 23, 2020 [58])...

Figure 9.4 IoT framework for managing the educational process in distance le...

Figure 9.5

NO

2

emissions before and after lockdown in Eastern China [67].

Chapter 10

Figure 10.1 A solar cell representation diagram.

Figure 10.2 The solar panel model under proteus.

Figure 10.3 The I-V and P-V curves in Solarex MX-60 PV panel.

Figure 10.4 Circuit diagram of the designed buck converter.

Figure 10.5 The proposed MPP tracker’s schematic design.

Figure 10.6 The PV power and voltage under varying solar irradiance.

Figure 10.7 Output characteristics curve while tracking the MPP at different...

Chapter 11

Figure 11.1 General depiction of embedding.

Figure 11.2 Types of embedding.

Figure 11.3 Depiction of latent semantic indexing (LSI).

Figure 11.4 Representation of folded recurrent neural network (RNN).

Figure 11.5 Description of unfolded recurrent neural network (RNN).

Figure 11.6 General depiction of continuous bag of words (CBOW).

Figure 11.7 General depiction of Skip-Gram.

Figure 11.8 Representation of clustering.

Chapter 12

Figure 12.1 Cloud-based agriculture system.

Figure 12.2 Sequence diagram of Mobile App computation.

Figure 12.3 Energy analysis graph with app engine and through smartphone.

Figure 12.4 Virtual machine specification used for testing application.

Figure 12.5 Virtual database formats in cloud real-time database.

Figure 12.6 Backend module for app engine.

Figure 12.7 Cloud platform hosting app engine in cloud.

Figure 12.8 Cloud platform hosting app engine in cloud.

Figure 12.9 User interface layout app.

Figure 12.10 SHA1 and MD5 key for securing project in cloud.

Chapter 13

Figure 13.1 Big data processing of MCPS.

Figure 13.2 Various data security requirements for protecting data in MCPS....

Figure 13.2 Framework of medical physical systems.

Chapter 14

Figure 14.1 Structure of a typical neuron.

Figure 14.2 Artificial neural network model.

Figure 14.3 Affine transformation produced by the presence of a bias.

Figure 14.4 Threshold function.

Figure 14.5 Common non-linear function.

Figure 14.6 Gaussian activity function.

Figure 14.7 Feedforward ANN topology.

Figure 14.8 Feedforward ANN topology.

Figure 14.9 Response of 1% CuO-doped SnO

2

based think film gas sensor on exp...

Figure 14.10 Gradient descent backpropagation with adaptive learning rate al...

Figure 14.11 Results of validation performance in logsin network transfer fu...

Figure 14.12 Results of regression logsin transfer function.

Figure 14.13 Results of validation performance in purelin network transfer f...

Figure 14.14 Results of regression Purelin network transfer function.

Figure 14.15 Results of validation performance in tansin network transfer fu...

Figure 14.16 Results of regression tansin transfer function.

Figure 14.17 Response of 1% CuO-doped SnO

2

based thick film gas sensor on ex...

Figure 14.18 Graph between concentration and error in different transfer fun...

Figure 14.19 Levenberg–Marquardt feed forward propagation neural network at ...

Figure 14.20 Results of validation performance in logsin network transfer fu...

Figure 14.21 Results of regression logsin transfer function.

Figure 14.22 Results of regression logsin transfer function.

Figure 14.23 Results of regression Purelin transfer function.

Figure 14.24 Results of regression Purelin transfer function.

Figure 14.25 Results of regression tansin transfer function.

Figure 14.26 Response of 1% CuO-doped SnO

2

based thick film gas sensor on ex...

Figure 14.27 Graph between concentration and error in different transfer fun...

Chapter 15

Figure 15.1 Dataflow diagram.

Figure 15.2 Proposed architecture.

Figure 15.3 DNN architecture.

Figure 15.4 CNN architecture.

Figure 15.5 Structure of LSTM network.

Figure 15.6 ECG signal vs. time index of abnormal beats.

Figure 15.7 AUC vs. number training Pts.

Figure 15.8 ROC curve of DNN and CNN.

Figure 15.9 ROC curve of LSTM.

Figure 15.10 ROC curve of DNN, CNN, and LSTM.

Chapter 16

Figure 16.1 Flow chart for the entire process.

Figure 16.2 Overall architecture of the signature recognition system contain...

Figure 16.4.1.1 Block diagram for the signature verification.

Figure 16.4.1.2 Data flow process.

Figure 16.5.1 Training, testing phase of the data set and the grey scale and...

Figure 16.5.2 Features of the desired object are saved.

Figure 16.5.3 Intensity and the density observation on the basis of the imag...

Chapter 17

Figure 17.1 Two different classes using SVM.

Figure 17.2 Logistic regression curve and its equation.

Figure 17.3 KNN implementation steps.

Figure 17.4 Implementation steps.

Figure 17.5 Flowchart to predict mobile phone price range.

Figure 17.6 Decision tree confusion matrix.

Figure 17.7 Gaussian Naive Bayes confusion matrix.

Figure 17.8 Support vector machine confusion matrix.

Figure 17.9 Logistic regression confusion matrix.

Figure 17.10 KNN confusion matrix.

Figure 17.11 Accuracy of different models.

Figure 17.12 R2 score of different model.

Guide

Cover Page

Series Page

Title Page

Copyright Page

Preface

Table of Contents

Begin Reading

Index

Also of Interest

WILEY END USER LICENSE AGREEMENT

Pages

ii

iii

iv

xv

xvi

xvii

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

328

329

331

332

333

334

335

336

338

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

421

422

423

424

425

427

428

429

Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Advances in Learning Analytics for Intelligent Cloud-IoT Systems

Series Editors: Dr. Souvik Pal ([email protected]) and Dr. Dac-Nhuong Le ([email protected])

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Convergence of Cloud with AI for Big Data Analytics

Foundations and Innovation

Edited by

Danda B. RawatLalit K AwasthiValentina Emilia BalasMohit Kumar

and

Jitendra Kumar Samriya

This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

Wiley Global Headquarters111 River Street, Hoboken, NJ 07030, USA

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant- ability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.

Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-90488-5

Cover image: Pixabay.ComCover design by Russell Richardson

Preface

This book was written to discuss the milestones in the development of three recent domains in computer science engineering—Cloud Computing, Artificial Intelligence and Big Data Analytics—and to analyse the convergence of cloud computing with artificial intelligence for big data analytics. Despite the fact that all three domains work separately, they can be linked in interesting ways. However, even though AI and big data can be easily linked, because AI needs a huge amount of data to train the model, they still suffer from a data storage issue. This drawback can be addressed with the help of cloud computing, which makes it possible to provide on- demand services to the client in terms of computer resources, such as storage and computing power, without the need for user management. This book aims to provide the scope of research on the discussed technologies.

Structure of the Book

The 17 chapters of the book cover the intertwining concepts of three key levels that are of interest to the scientific community:

Artificial Intelligence

Big Data

Cloud Computing

A chapter-wise breakdown of the contents of the book follows:

Chapter 1

discusses the integration of artificial intelligence, big data and cloud computing with the internet of things (IoT).

Chapter 2

discusses cloud computing and virtualization.

Chapter 3

presents a time and cost-effective multi-objective scheduling technique for cloud computing environment.

Chapter 4

discusses cloud-based architecture for effective surveillance and diagnosis of COVID-19.

Chapter 5

presents smart agriculture applications using cloud and the IoT.

Chapter 6

presents applications of federated learning in computing technologies.

Chapter 7

analyzes the application of edge computing in smart healthcare.

Chapter 8

discusses a smart agriculture application using Fog-IoT.

Chapter 9

presents a systematic study of the global impact of COVID-19 on the IoT.

Chapter 10

discusses efficient solar energy management using IoT-enabled Arduino-based MPPT techniques.

Chapter 11

presents an axiomatic analysis of pre-processing methodologies using machine learning in text mining from the perspective of social media in the IoT.

Chapter 12

presents an app-based agriculture information system for rural farmers in India.

Chapter 13

provides a systematic survey on AI-enabled cyber-physical systems in healthcare.

Chapter 14

discusses an artificial neural network (ANN) aware methanol detection approach with CuO-doped SnO

2

in gas sensor.

Chapter 15

describes how to detect heart arrhythmias using deep learning algorithms.

Chapter 16

presents an artificial intelligence approach for signature detection.

Chapter 17

compares various classification models using machine learning to predict the price range of mobile phones.

Acknowledgment

Writing this book has been a rewarding experience, which was enhanced by the tremendous effort of a team of very dedicated contributors. We would like to thank the authors for their respective chapters and also express our thanks to the list of editors who provided suggestions to improve content delivery. All feedback was considered, and there is no doubt that some of the content was influenced by their suggestions. We especially would like to thank the publisher, who believed in the content and provided a platform to reach the intended audience. Finally, we are thankful to our families for their continued support. Without them, the book would not have been possible.

The Editors

October 2022

1Integration of Artificial Intelligence, Big Data, and Cloud Computing with Internet of Things

Jaydip Kumar

Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow (UP), India

Abstract

The Internet of Things (IoT) provides to the client an effective technique for communicating with the Web world through ubiquitary object enabled networks. The rapid progress in IoT connected devices creates a huge amount of data in a second from personal and industrial devices. This information should be utilized to help business and functional objectives. Thus, there is an urgent requirement for adopting cloud computing, big data, and artificial intelligence techniques to enable storage, analytics, and decision making. In this article, we focused our consideration to integrate Cloud Computing, Big data and Artificial Intelligence technique with the Internet of Things devices. Cloud computing, Big Data, Artificial Intelligence, and IoT are different techniques that are already part of our life. Their adoption and uses are expected to make them more comprehensive and make them essential components of the future Internet. The Internet of Things (IoT) is a system of interconnected gadgets, digital or mechanical machines that are given exceptional identifiers and the capacity to move information over an organization without expecting human-to-human or human-to-pc collaboration.

Keywords: Artificial intelligence, big data, cloud computing, Internet of Things

1.1 Introduction

With the wide-spread discovery of techniques in the current digital era, increasing physical entities are interconnected to the Internet of Things (IoT) devices. In recent years IoT technologies are applied with different techniques such as Artificial Intelligence, Big Data, and Cloud Computing. Artificial Intelligence (AI) is a technique which has the ability to compute a huge amount of task that is usually done by a human. Artificial Intelligence uses different learning techniques to facilitate automatic rules and regulations for decision making. Artificial intelligence is divided into two different modules such as learning module and predicate module [1]. The learning module is used for effective data collection, training, and data modeling. And the predicate module is used to take action on the current situation. The flow and storage of exponentially increasing data are easily managed by Artificial Intelligence (AI). The integration of cloud computing and IoT are also two different technologies that assume a vital part in our daily life. Cloud computing and the Internet of Things are merged together is expected to break both current and future internet which we called as new paradigm CloudIoT [2]. In the era of the internet which plays a fundamental role in cloud computing, it seems to be represented as a medium or the platform through which many different cloud computing services are accessible or delivered its services. If you are thinking that the internet as a virtual “space” for connecting users from over the globe, it is like a cloud, sharing information by using the internet. Cloud computing is the trending technology in the daily life of everyone which provides on-demand web services such as networking devices, data storage, servers, and applications. It provides higher flexibility and cost efficiency while users try to use cloud computing resources and applications. The different number of connected devices has already exceeded the number of users on the earth. This is due to exponential increase of connected devices rapidly increasing huge amount of data as well. The storage of data locally and temporarily will not be possible to access different devices which are connected to each other. There is a need to be centrally storage space which is provided by cloud storage [3]. And the intense invention in the Internet of Things (IoT) technologies, the Big Data technique has critical data analytics tools which bring the knowledge within the IoT devices to make the better purpose of IoT systems and support critical decision making. Big Data has been divided into five fundamental bases such as volume, variety, velocity, veracity, and value. The volume indicates the size of the data. And the different types of data from different sources are known as variety. The real-time data collection is known as velocity, and veracity is the uncertainty of data and the value which shows the benefits of different industrial and academic fields [4]. The combination of IoT and Big Data has created opportunities to develop complex systems for different industries such as healthcare, smart city, military and agriculture, education, etc. The flow diagram of AI, Big data, and cloud computing integrated with the Internet of Things is given below in Figure 1.1.

Figure 1.1 Flow diagram of AI, big data, and cloud computing integrated with Internet of Things.

1.2 Roll of Artificial Intelligence, Big Data and Cloud Computing in IoT

Internet of Things (IoT) is an interconnection of various devices which are connected to each other through the internet and exchange information. These IoT devices generate a huge amount of information [5]. Artificial Intelligence (AI) uses the decision-making support system to provide data flow and storage in IoT networks. The integration of artificial intelligence (AI) with the Internet of Things (IoT) techniques will generate extraordinary value-creation opportunities. The IoT devices with AI enabled the rise of a “factory of the future” [6]. This increases the efficiency, turnaround, and waiting time and reduces the cost. The IoT with AI is used in different fields such as 3D printing, Robotics, the food industry, manufacturing, logistics, and supply chain management. These fields create lots of information in a regular mode which is centrally stored in cloud computing. It can be said that the cloud with IoT will be the future of the next generation of the internet. However, the cloud computing services are fully dependent on cloud service providers but IoT technologies are based on diversity [7]. Cloud computing reduces the cost of the use of applications and their services for users. It also simplifies the flow of Internet of Things data capturing and processing and also provides fast and cheapest cost integration, installation, and deployment. And without Big Data analytic applications, the huge amount of data generated by the IoT devices creates an overhead for any business. Due to this any organization must know how to handle this massive amount of data that is collected by the IoT devices. Fetching accurate data is not a problem for any organization; the challenge is to get the necessary skills in the analytical analysis field to deal with big data [8].

1.3 Integration of Artificial Intelligence with the Internet of Things Devices

For addressing any problem AI needs to two-step process which is shown in Figure 1.2. A set of AI models has been created in the first stage. The models are created by the machine learning algorithm with a set of training data. These trained data are processed by the natural language documents or by the encoding of human expertise [14]. The models are invented in different categories like neural networks, decision trees, and inference rules. The models use the inferences from the Internet of Things sensor’s input data and guide the operations of the system [9, 18]. There are lots of work have been completed with the integration of Artificial intelligence and Internet of Things. We have mainly surveyed previous works on the personal and industrial applications such as attendance monitoring system, human activity and presence in hospitality, agricultural applications, hospital, human stress monitoring [15, 21]. The short review of IoT applications domains are given below Table 1.1 and the difference between the AI and IoT are given below in Table 1.2.

Figure 1.2 Integration architecture of AI in IoT.

Table 1.1 Recent Artificial Intelligence based Internet of Things applications.

Problem

Techniques

Data

Wearable devices

Decision tree, logistic regression

Health data

Human attendance system

Random forest, decision tree etc.

Images

Smart meter operation

Bayesian network, naïve Bayes, decision tree, random forest

Meter reading data

Parking space detection

Clustering algorithms

Camera data

Human stress detection

SVM, logistic regression

Pulse waveform

Table 1.2 Table of differences between the internet of things and artificial intelligence.

Based on

Internet of things

Artificial intelligence

Connection type

A set of interconnecting devices over the network

Interconnection and machine independent is not needed

Capability

Capabilities of the devices are known prior

The capabilities never be predicted of machine

Interaction

Interaction of human between the devices is needed

Interaction of human between the devices is not needed

Future scope

Interaction of human between the devices is needed

Machine can learn and start reacting more than human

Instruction need

Instruction needed to IoT devices

Machines can learn from experiences

Dependency

IoT devices cannot work without artificial intelligence

Artificial intelligence is not dependent on IoT devices

Applications

Smart home, smart city, medical, water monitoring etc.

Fraud prevention, voice assistant, personalized shopping, AI-powered assistants etc.

1.4 Integration of Big Data with the Internet of Things

The generations of the huge amount of data are collected from the Internet of Things devices and its sensors leads to an exponential incremental in data. This data needs to manage, processed, and analyzed by the organization [10] which is shown in Figure 1.3. IoT technology is a major source of Big Data which motivates the organization to deploy Big Data technology and its applications to acquire the needs of IoT technologies. The collected data from the IoT devices is managed, processed, and analyzed by the big data. Big Data provides a framework for data transmission and processing which support IoT data and virtualization. The provided data become deep and more complicated to be stored and analyzed by conventional technology. So the broad consensus is that the big data and Internet of Things techniques are highly interconnected [11]. The architecture of Big Data integrated with the Internet of Things is given below in Figure 1.3 and the comparative study of big data applications is given below [17] in Table 1.3.

1.5 Integration of Cloud Computing with the Internet of Things

The combination of cloud computing and IoT are efficient for every business or user. This enhanced the efficiency of every task and reduced the setup cost for applications, servers, storage spaces, etc. The different cloud provider provides cloud services pay as requirements model, where cloud user can pay for the particular services used [22]. When an organization needs to collect a huge amount of data from the IoT device sensors, each sensor needs a large amount of computation power and storage space. This problem is solved by the combination of Cloud computing and Internet of Things techniques, in which the IoT sensors uses the cloud resources and store the collected information in a centralized manner [12]. The architecture of cloud computing with the Internet of Things is given below in Figure 1.4. Cloud computing and the Internet of Things contain different characteristics which are compulsory for each other which is shown in Table 1.4. Due to these characteristics, the integration of Cloud computing and IoT gives an excellent solution to real-world problems [13].

Figure 1.3 Integration architecture of big data with internet of things devices.

Table 1.3 Comparisons of big data applications.

Applications

Sources

Characteristics

Healthcare

Patient and laboratory data, gene expression data, risks and emotions of user’s data.

Text, structured, images, videos, hypertext

Government Sectors

Governmental defense data, scientific and technological data, education data, treasury, power and energy records, employment records.

Text, images, videos, audios

Retails and customer products

Sales and purchasing records, marketing, accounting, inventory and feedback data.

Text, audio, videos, hyperlinks, emotional symbols

Agriculture

Weather information, agricultural drones and videos and images, soil’s map and fertilities data, global positioning system (GPS) records.

Text, number, images videos

Banking sectors

Transactional information, customer’s information, policies, loans, accounts, financial information.

Text, numbers, images

Transportation

Transport information, tracking devices information, GPS information, accidental information, vehicles and products information.

Text, numbers, images, videos, hyperlinks, audios

Figure 1.4 Architecture of cloud computing with internet of things.

Table 1.4 Characteristics of the internet of things and cloud computing.

Cloud computing

Internet of things

Characteristics

Give procedure to manage

Producer

Big Data

Provide virtually unlimited

Limited

Storage

Provide virtually unlimited

Limited

Computational services

Centralized services

Widespread

Displacement

Comprehensive

Limited

Reachability

Mode of service providing

Point of convergence

Internet role

The integration of Cloud computing and Internet of Things can be divides in three categories such as cloud platform, infrastructure and IoT middleware. Cloud computing removes the IoT limitations and provided opportunities for the business and it is managed by the cloud infrastructure. The IoT provides interconnection between the IoT devices and Cloud platform for data exchange.

1.6 Security of Internet of Things

The security challenges of IoT technologies are directly related to their applications. The aim of the IoT technique is to provide the combination of both physical and digital world in a single ecosystem which created the new intelligent era of web world [16, 23]. IoT provides huge business and opportunities for organizations such as energy, healthcare, government and other sectors. Due to this enhancement of IoT techniques, it suffers from different security issues which are more challenging to secure its data and applications [19]. The IoT devices faced different security issues such as privacy of data, authorization, verification, access control, data storage management, etc. The few security challenges are given below in Table 1.5. In IoT, the wireless communication channel involves radio communications, transmitters, and receivers for the information exchange between two IoT devices. So this communication channel suffers from different security threats and attacks such as man-in-middle attacks, communication signal loss, hacking of data, Denial of service attacks, protocol tunneling [20].

Table 1.5 Internet of Things security requirements.

Security requirements

Description

IoT security properties

Availability

The resources of the IoT devices are readily available.

IoT resources must be attack free from the denial of service (DoS) attacks.

Integrity

It is accuracy, consistency of data and its services, trustworthiness of IoT life cycle.

The security algorithms must be capable of detecting modification and manipulation of data breaches on IoT devices.

Confidentially

The protection from the unauthorized users and access.

Authentication

It authenticate that the user is genuine user.

The security technique must be capable of verification and authentication of user.

Authorization

Prevented the illegal uses of IoT devices or resources.

Only authorized or legal user can access the network

Access Control

Provided management and prevent unauthorized user and access to IoT resources and data.

Ensured that the IoT devices are verified and authorized to access the IoT data.

1.7 Conclusion

The Internet of Things is a wide field and contains incredible and different variety of applications. The main aim of IoT is to provide a facility of exchange information and synergic performance between devices and peoples via global machine-to-machine (M2M) networks. Due to M2M network, exchange of information between the devices creates an exponential amount of data. It is impossible to manage or keep secure these generated personal or organizational data in local devices. The IoT techniques need centrally storage space for personal or industrial data. To avoid these problems, in this paper we have introduced the integration of Cloud Computing, Big Data and Artificial Intelligence techniques with the Internet of Things devices. The integration of cloud computing with IoT techniques stores the information centrally generated by the IoT devices. And the collected data which is centrally stored in cloud computing is managed, processed, and analyzed by the Big Data Techniques. To extract the high volume of IoT data in real-time, processing needs machine learning and AI algorithms.

References

1. Guo, K., Lu, Y., Gao, H., Cao, R., Artificial intelligence-based semantic inter-net of things in a user-centric smart city.

Sensors

, 18, 5, 1341, 2018.

2. Botta, A., De Donato, W., Persico, V., Pescapé, A., Integration of cloud computing and Internet of Things: A survey.

Future Gener. Comput. Syst.

, 56, 684–700, 2016.

3. Aazam, M., Khan, I., Alsaffar, A.A., Huh, E.N., Cloud of things: Integrating internet of things and cloud computing and the issues involved, in:

Proceedings of 2014 11th International Bhurban Conference on Applied Sciences & Technology (IBCAST)

, Islamabad, Pakistan, 14th-18th January, 2014, IEEE, pp. 414–419, 2014.

4. Ge, M., Bangui, H., Buhnova, B., Big data for internet of things: A survey.

Future Gener. Comput. Syst.

, 87, 601–614, 2018.

5. Mohamed, E., The relation of artificial intelligence with internet of things: A survey.

J. Cybersecur. Inf. Manage.

, 1, 1, 30–24, 2020.

6. Ramadoss, T.S., Alam, H., Seeram, R., Artificial intelligence and Internet of Things enabled circular economy.

Int. J. Eng. Sci.

, 7, 9, 55–63, 2018.

7. Atlam, H.F., Alenezi, A.

et al.

, Integration of cloud computing with internet of things: Challenges and open issues, in:

2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)

, IEEE, pp. 670–675, 2017.

8. Alansari, Z., Anuar, N.B.

et al.

, Challenges of internet of things and big data integration, in:

International Conference for Emerging Technologies in Computing

, Springer, Cham, pp. 47–55, 2018.

9. Calo, S.B., Touna, M.

et al.

, Edge computing architecture for applying AI to IoT, in:

2017 IEEE International Conference on Big Data (Big Data)

, IEEE, pp. 3012–3016, 2017.

10. Nasrulla, K. and Anitha K., Perception of relationship between Big Data and Internet of Things,

International Journal of Advanced Research in Computer and Communication Engineering

, 4, 12, 264–267, DOI 10.17148/ IJARCCE.2015.41261.

11. Al-Gumaei, K., Schuba, K., Friesen, A.

et al.

, A survey of internet of things and big data integrated solutions for industries 4.0, in:

2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA)

, vol. 1, IEEE, pp. 1417–1424, 2018.

12. Kaur, C., The cloud computing and Internet of Things (IoT).

Int. J. Sci. Res. Sci., Eng. Technol.

, 7, 1, 19–22, 2020.

13. Malik, A. and Om, H., Cloud computing and internet of things integration: Architecture, applications, issues, and challenges, in:

Sustainable Cloud and Energy Services

, pp. 1–24, Springer, Cham, 2018.

14. Sepasgozar, S., Karimi, R.

et al.

, A systematic content review of artificial intelligence and the internet of things applications in smart home.

Appl. Sci.

, 10, 9, 3074, 2020.

15. Yashodha, G., Rani, P.P.

et al.

, Role of artificial intelligence in the Internet of Things–A review, in:

IOP Conference Series: Materials Science and Engineering

, vol. 1055, No. 1, IOP Publishing, p. 012090, 2021.

16. Abiodun, O.I., Abiodun, E.O.

et al.

, A review on the security of the internet of things: Challenges and solutions.

Wireless Pers. Commun.

, 119, 3, 2603– 2637, 2021.

17. Bendre, M.R. and Thool, V.R., Analytics, challenges and applications in big data environment: A survey.

J. Manage. Anal.

, 3, 3, 206–239, 2016.

18. Tien, J.M., Internet of things, real-time decision making, and artificial intelligence.

Ann. Data Sci.

, 4, 2, 149–178, 2017.

19. Kouicem, D.E., Bouabdallah, A., Lakhlef, H., Internet of things security: A top-down survey.

Comput. Networks

, 141, 199–221, 2018.

20. Alaba, F.A., Othman, M.

et al.

, Internet of things security: A survey.

J. Netw. Comput. Appl.

, 88, 10–28, 2017.

21. Cui, L., Yang, S., Chen, F.

et al.

, A survey on application of machine learning for Internet of Things.

Int. J. Mach. Learn. Cybern.

, 9, 8, 1399–1417, 2018.

22. Kumar, J., Cloud computing security issues and its challenges: A comprehensive research.

Int. J. Recent Technol. Eng

, 8, 1, 10–14, 2019.

23. Kumar, J. and Saxena, V., Asymmetric encryption scheme to protect cloud data using paillier-cryptosystem.

Int. J. Appl. Evol. Comput. (IJAEC)

, 12, 2, 50–58, 2021.

Note

Email

: [email protected]

2Cloud Computing and Virtualization

Sudheer Mangalampalli1*, Pokkuluri Kiran Sree2, Sangram K. Swain3 and Ganesh Reddy Karri1

1 School of Computer Science & Engineering, VIT-AP University, Amravati, AP, India

2 Department of Computer Science & Engineering, Shri Vishnu Engineering College For Women, Bhimavaram, AP, India

3 Department of CSE, Centurion University of Technology and Management, Odisha, India

Abstract

Cloud Computing is one of the revolutionized paradigms in the IT industry, which can provide wide variety of services pay-as-you go model to all the customers in different domains like IT industry, Health, education, entertainment etc. These services are provisioned to the user based on the SLA between cloud user and provider virtually. Hypervisors are used to enable the virtualization and to spin up VMs in the cloud paradigm. There are different levels at which virtualization can be implemented, In this book chapter, we are discussing about the overview of cloud computing, different service models, deployment models and different virtualization techniques used for cloud paradigm. For effectiveness of any cloud computing paradigm, a task scheduler is necessary to get seamless services from cloud paradigm. Therefore, in this chapter we have proposed a task scheduling algorithm which uses priorities of tasks and VMs. For this algorithm we have used a nature inspired algorithm chaotic social spider algorithm to model task scheduling algorithm and simulated on CloudSim simulator. Finally, it was compared with existing algorithms PSO and CS and proposed approach is outperformed over existing algorithms with respect to makespan and energy consumption.

Keywords: Cloud computing, Service level agreement (SLA), virtualization, hypervisor, service models, deployment models

2.1 Introduction to Cloud Computing

2.1.1 Need of Cloud Computing

Cloud computing is a rapid growing model which is required in many of the fields like healthcare, finance, Education, Government services, Entertainment, Business and not limited to the domains which we have specified here. Initially all the organizations were invested huge amount on their IT infrastructure setup to provide their corresponding services to the customers. They have to invest a lot of up-front investment on the IT infrastructure, which can increase huge burden on the organization and procuring, deploying physical infrastructure is a challenging task, and it takes a huge amount of time for any organization. Physical infrastructure in terms of compute, storage, and network for any organization is limited and it is not scalable. So, the need of cloud computing comes into the point where the resources to the cloud users can be provisioned on demand with the concept named as virtualization. Scalability is also one of the important parameter where in the on premises environment it is not possible to scale up and down the resources on demand, as physical resources are limited. Therefore, with these main limitations like upfront investment and scalability in the existing on premises infrastructures the need for the cloud computing arises in every industry in these days. The below subsection describes about the history of cloud computing.

2.1.2 History of Cloud Computing

Initially in the earlier days in 2010, many of the people thought cloud means huge databases, servers and some may thought this technology actually came from real clouds [2] but it is not true. Cloud Computing is not started in this recent era but it was already existed in the different forms, as we could not confine cloud to a particular architecture either i.e. centralized architecture or distributed architecture. The history [3] of the evolvement of cloud with timelines and is given in the below table in a detailed way in the below table.

From Table 2.1, we can identify cloud computing paradigm was existed years ago and users using the paradigm but no one identifies that this model is cloud computing. When the vendors, which provides infrastructure, and software services on the cloud platforms comes into the picture then everybody is looking at cloud platform as a new model but it is already existed with the base known as virtualization. The above we have given the brief history of cloud computing. The next subsection discusses about the definition of cloud computing.

Tabel 2.1 Timeline of cloud computing [3].

Year

Timeline and history of evolvement of cloud

1950

Herb Grosch assumption about operation of Computer terminals using Data Centers.

1960

John McCarthy opinion about computations as public utility

1966

Douglas Parkhill gave his explanation about characteristics of cloud computing

1969

Development of UNIX

1970

Development of Internet

1990

Era of Internet begins

1991

General use of Internet Started

1995

Online Auction website eBay was developed and amazon was also evolved in the same year.

1999

Salesforce developed a cloud platform, which is a Software-as-a-Service.

2006

Amazon started AWS, which is initially started as infrastructure-as-a-Service.

2008

Eucalyptus, Open Nebula were developed which are private cloud platforms.

2.1.3 Definition of Cloud Computing

In this section, we have given the definition for cloud computing which was defined by the NIST. Initially there is no standard definition for cloud computing but after the use of this paradigm by many of the companies NIST [1] in the year 2011 has given a definition for Cloud Computing based on certain characteristics. According to NIST, it was defined as “On demand network access to a shared pool of configurable computational resources which can gives seamless access of services to the users”.

2.1.4 Different Architectures of Cloud Computing

In this section, we will discuss about architecture of Cloud Computing. Generally Cloud architecture needs a browser application i.e. to be in any of machine – desktop, laptop, or any device which supports the browser and it should be connected to the Internet and which is again connected again at the backend at the virtual infrastructure which is resided in physical host and which in turn resided in the datacenters. Architecture of Cloud Computing basically divided into two types: i. Generic Architecture and ii. Market oriented Architecture.

2.1.4.1 Generic Architecture of Cloud Computing

This architecture about the generalized version of Cloud Computing and which consists of the components in the below architecture.

The above Figure 2.1 represents Generic architecture of Cloud Computing which consists of three layers [4] i.e. Front end, Network and Infrastructure layers. Front end consists of an application which can be runs from a web browser in any of the device i.e. desktop, laptop, and mobile. Network layer consists of a network which connects both Frontend and backend infrastructure and finally the backend component and finally infrastructure layer consists of virtual machines which are resided in the physical hosts and which in turn resided in datacenters. The above architecture is a generalized architecture which can gives the overview of cloud computing.

Figure 2.1 Generic architecture of cloud computing [4].

2.1.4.2 Market Oriented Architecture of Cloud Computing

This architecture is about the Market Oriented Architecture represented in Figure 2.2, it is a customized architecture for different cloud vendors based on the needs of the customer, and thereby cloud vendor will render the services required for the cloud users. Many of the cloud vendors named as AWS, Azure and Google cloud they are providing the required custom-ized architecture for the users based on the Service Level Agreement made between user and provider. Initially in this architecture, user requests are submitted in the cloud console or interface. On behalf of the user, broker will take these requests and submits them to the task manager where it consists of SLA resource allocator which examines requests made by the user and based on the agreement made by the user and vendor it will allocate resource by dispatching that request through dispatcher to VMs resided in Physical hosts which in turn resided in datacenters. It also consists of Accounting and pricing modules to calculate the price for all the services used by the user and automatic pricing will be calculated by the pricing module. It consists of a VM Monitor module which will track the availability of VMs i.e. how many of the VMs are utilized and how many of the VMs are free. These details will be tracked through VM monitor module. For every request given by the user and examined by SLA resource allocator and before assigning it to a virtual machine VM monitor will track the availability of VMs and then based on that it will assign that task to corresponding VM. All these VMs are running in the cloud enabled by a component called hypervisor through which virtual environment will be enabled for the VMs. The below figure represents Market oriented architecture of Cloud Computing. We have presented the architectures of cloud computing in a detailed way. Many of the users chooses different cloud services based on the need of the application and customized version of the cloud platform and services will be provided to the customers so the generic architecture is to assumed as an ideal version and in real time market oriented architecture is to be used by the customers. Now in the below section, we have to discuss about the various applications of cloud computing in different domains.

Figure 2.2 Market-oriented architecture of cloud computing [5].

2.1.5 Applications of Cloud Computing in Different Domains

There are many kinds of applications where cloud computing is used for different purposes. In this sub section some of the applications of cloud computing in different domains are discussed. These are some of the domains mentioned below.

Healthcare

Education

Entertainment

Government

Transportation

2.1.5.1 Cloud Computing in Healthcare

In these days, maintaining the patient data manually in the hospitals is a challenging job. Most of the patient health records are in the electronic format and to store these records like test reports, X-rays and scanning reports is easier through cloud storage and to store them in a huge manner it is not that much easier by using standard storage mechanisms. Using Cloud Computing, doctor and patient can easily access the records even if they are sitting remotely. Many of the hospitals and health insurance vendors also maintain their records in cloud for easier processing of insurance for the patients. In this way, Cloud Computing technology is used in health-care domain.

2.1.5.2 Cloud Computing in Education

Pandemic shifted the normal situation into a new normal. To handle this current situation, all the educational institutions are using different tools to teach the students and even for conducting the meetings and other activities. Cloud Computing plays a major role in education domain as many of the organizations uses variety of tools i.e. Microsoft teams from Azure cloud, Google meet from Google cloud and many of the tools are running in the cloud, which can gives greater scalability and flexibility to engage the students in education domain.

2.1.5.3 Cloud Computing in Entertainment Services

This technology gives us a lot of scope to use it in various domains. It can also be used in entertainment where lot of online platforms named as OTT services can be run with the support of cloud computing at the backend. It is difficult to run these services with on premises infrastructure as millions of the people around the world can subscribe these services so there is a chance to get the scalability issue when they run on the on-premises environment. Therefore, these OTT vendors have to use this technology to render their services to the customers with high range scalability by providing seamless services to the users.

2.1.5.4 Cloud Computing in Government Services

Many of the government sector departments also using Cloud Computing technology for running their virtual infrastructure in Cloud. For example, DBS bank runs their total infrastructure on AWS cloud therefore which can reduces their upfront infrastructure cost. It is important for the countries which consist of more dense population and if the people need to use the services in online then there is a definite need to use cloud computing as the technology in many of the Government services.

2.1.6 Service Models in Cloud Computing

In cloud computing, all the components like compute, storage, and network will be rendered as a service to the users. The major services provided by the cloud are mentioned as below.

Infrastructure-as-a-service

Platform-as-a-service

Software-as-a-service

There are many other kind of services like Database-as-a-service, Machine learning-as-a-service and IOT-as-a-service and not limited to these but many more services are available in the various cloud platforms in the below Table 2.2. In this chapter, we briefly discusses about the main services which leverages the services to the users of cloud computing.

Infrastructure-as-a-service: This is the primary layer or service model in cloud computing which can provide virtual infrastructure to the users through which they can get VM instances virtually i.e. compute services through which huge computational tasks or requests can be handled on demand without any upfront investment cost. This layer also provides virtual storage, which can give the advantage of scalability to the corresponding user to store their data in cloud when compared with the on premises environment.

Platform-as-a-service: In cloud computing, there is a chance to develop our own applications on top of the cloud platform which leverages services to the cloud user by enabling the platform to develop their applications on cloud platform with the use of REST API services and different middle-ware software.

Table 2.2 List of services in major cloud vendors.

Name of the cloud environment

Infrastructure-as-a-service

Platform-as-a-service

Software-as-a-service1

Amazon Web Services

EC2, S3, EBS, EFS

Elastic BeanStalk

SNS, Email service

Google Cloud Platform

Compute Engine, Storage and database

Developer tools, APP Engine

Gmail, Google Drive

Microsoft Azure

Azure Virtual machines, Blob Storage, Disks, Files.

Web Apps, Developer Services, Integration services etc.

Microsoft email, one drive

Figure 2.3 Service and deployment models in cloud computing [6].

Software-as-a-service: