Details

Fog Computing


Fog Computing

Theory and Practice
Wiley Series on Parallel and Distributed Computing 1. Aufl.

von: Assad Abbas, Samee U. Khan, Albert Y. Zomaya

115,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 28.04.2020
ISBN/EAN: 9781119551775
Sprache: englisch
Anzahl Seiten: 608

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Beschreibungen

<p><b>Summarizes the current state and upcoming trends within the area of fog computing</b></p> <p>Written by some of the leading experts in the field, <i>Fog Computing: Theory and Practice </i>focuses on the technological aspects of employing fog computing in various application domains, such as smart healthcare, industrial process control and improvement, smart cities, and virtual learning environments. In addition, the Machine-to-Machine (M2M) communication methods for fog computing environments are covered in depth.</p> <p>Presented in two parts—Fog Computing Systems and Architectures, and Fog Computing Techniques and Application—this book covers such important topics as energy efficiency and Quality of Service (QoS) issues, reliability and fault tolerance, load balancing, and scheduling in fog computing systems. It also devotes special attention to emerging trends and the industry needs associated with utilizing the mobile edge computing, Internet of Things (IoT), resource and pricing estimation, and virtualization in the fog environments.</p> <ul> <li>Includes chapters on deep learning, mobile edge computing, smart grid, and intelligent transportation systems beyond the theoretical and foundational concepts</li> <li>Explores real-time traffic surveillance from video streams and interoperability of fog computing architectures</li> <li>Presents the latest research on data quality in the IoT, privacy, security, and trust issues in fog computing</li> </ul> <p><i>Fog Computing: Theory and Practice</i> provides a platform for researchers, practitioners, and graduate students from computer science, computer engineering, and various other disciplines to gain a deep understanding of fog computing.</p>
<p>List of Contributors xxiii</p> <p>Acronyms xxix</p> <p><b>Part I Fog Computing Systems and Architectures </b><b>1</b></p> <p><b>1 Mobile Fog Computing </b><b>3<br /></b><i>Chii Chang, Amnir Hadachi, Jakob Mass, and Satish Narayana Srirama</i></p> <p>1.1 Introduction 3</p> <p>1.2 Mobile Fog Computing and Related Models 5</p> <p>1.3 The Needs of Mobile Fog Computing 6</p> <p>1.3.1 Infrastructural Mobile Fog Computing 7</p> <p>1.3.2 Land Vehicular Fog 9</p> <p>1.3.3 Marine Fog 11</p> <p>1.3.4 Unmanned Aerial Vehicular Fog 12</p> <p>1.3.5 User Equipment-Based Fog 13</p> <p>1.4 Communication Technologies 15</p> <p>1.4.1 IEEE 802.11 15</p> <p>1.4.2 4G, 5G Standards 16</p> <p>1.4.3 WPAN, Short-Range Technologies 17</p> <p>1.4.4 LPWAN, Other Medium- and Long-Range Technologies 18</p> <p>1.5 Nonfunctional Requirements 18</p> <p>1.5.1 Heterogeneity 20</p> <p>1.5.2 Context-Awareness 23</p> <p>1.5.3 Tenant 25</p> <p>1.5.4 Provider 27</p> <p>1.5.5 Security 29</p> <p>1.6 Open Challenges 31</p> <p>1.6.1 Challenges in Land Vehicular Fog Computing 31</p> <p>1.6.2 Challenges in Marine Fog Computing 32</p> <p>1.6.3 Challenges in Unmanned Aerial Vehicular Fog Computing 32</p> <p>1.6.4 Challenges in User Equipment-based Fog Computing 33</p> <p>1.6.5 General Challenges 33</p> <p>1.7 Conclusion 35</p> <p>Acknowledgment 36</p> <p>References 36</p> <p><b>2 Edge and Fog: A Survey, Use Cases, and Future Challenges 43<br /></b><i>Cosmin Avasalcai, Ilir Murturi, and Schahram Dustdar</i></p> <p>2.1 Introduction 43</p> <p>2.2 Edge Computing 44</p> <p>2.2.1 Edge Computing Architecture 46</p> <p>2.3 Fog Computing 47</p> <p>2.3.1 Fog Computing Architecture 49</p> <p>2.4 Fog and Edge Illustrative Use Cases 50</p> <p>2.4.1 Edge Computing Use Cases 50</p> <p>2.4.2 Fog Computing Use Cases 54</p> <p>2.5 Future Challenges 57</p> <p>2.5.1 Resource Management 57</p> <p>2.5.2 Security and Privacy 58</p> <p>2.5.3 Network Management 61</p> <p>2.6 Conclusion 61</p> <p>Acknowledgment 62</p> <p>References 62</p> <p><b>3 Deep Learning in the Era of Edge Computing: Challenges and Opportunities </b><b>67<br /></b><i>Mi Zhang, Faen Zhang, Nicholas D. Lane, Yuanchao Shu, Xiao Zeng, Biyi Fang, Shen Yan, and Hui Xu</i></p> <p>3.1 Introduction 67</p> <p>3.2 Challenges and Opportunities 68</p> <p>3.2.1 Memory and Computational Expensiveness of DNN Models 68</p> <p>3.2.2 Data Discrepancy in Real-world Settings 70</p> <p>3.2.3 Constrained Battery Life of Edge Devices 71</p> <p>3.2.4 Heterogeneity in Sensor Data 72</p> <p>3.2.5 Heterogeneity in Computing Units 73</p> <p>3.2.6 Multitenancy of Deep Learning Tasks 73</p> <p>3.2.7 Offloading to Nearby Edges 75</p> <p>3.2.8 On-device Training 76</p> <p>3.3 Concluding Remarks 76</p> <p>References 77</p> <p><b>4 Caching, Security, and Mobility in Content-centric Networking </b><b>79<br /></b><i>Osman Khalid, Imran Ali Khan, Rao Naveed Bin Rais, and Assad Abbas</i></p> <p>4.1 Introduction 79</p> <p>4.2 Caching and Fog Computing 81</p> <p>4.3 Mobility Management in CCN 82</p> <p>4.3.1 Classification of CCN Contents and their Mobility 83</p> <p>4.3.2 User Mobility 83</p> <p>4.3.3 Server-side Mobility 84</p> <p>4.3.4 Direct Exchange for Location Update 84</p> <p>4.3.5 Query to the Rendezvous for Location Update 84</p> <p>4.3.6 Mobility with Indirection Point 84</p> <p>4.3.7 Interest Forwarding 85</p> <p>4.3.8 Proxy-based Mobility Management 85</p> <p>4.3.9 Tunnel-based Redirection (TBR) 86</p> <p>4.4 Security in Content-centric Networks 88</p> <p>4.4.1 Risks Due to Caching 90</p> <p>4.4.2 DOS Attack Risk 90</p> <p>4.4.3 Security Model 91</p> <p>4.5 Caching 91</p> <p>4.5.1 Cache Allocation Approaches 91</p> <p>4.5.2 Data Allocation Approaches 93</p> <p>4.6 Conclusions 101</p> <p>References 101</p> <p><b>5 Security and Privacy Issues in Fog Computing </b><b>105<br /></b><i>Ahmad Ali, Mansoor Ahmed, Muhammad Imran, and Hasan Ali Khattak</i></p> <p>5.1 Introduction 105</p> <p>5.2 Trust in IoT 107</p> <p>5.3 Authentication 109</p> <p>5.3.1 Related Work 109</p> <p>5.4 Authorization 113</p> <p>5.4.1 Related Work 114</p> <p>5.5 Privacy 117</p> <p>5.5.1 Requirements of Privacy in IoT 118</p> <p>5.6 Web Semantics and Trust Management for Fog Computing 120</p> <p>5.6.1 Trust Through Web Semantics 120</p> <p>5.7 Discussion 123</p> <p>5.7.1 Authentication 124</p> <p>5.7.2 Authorization 125</p> <p>5.8 Conclusion 130</p> <p>References 130</p> <p><b>6 How Fog Computing Can Suppor Latency/Reliability-sensitive IoT Applications: An Overview and a Taxonomy of State-of-the-art Solutions </b><b>139<br /></b><i>Paolo Bellavista, Javier Berrocal, Antonio Corradi, Sajal K. Das, Luca Foschini, Isam Mashhour Al Jawarneh, and Alessandro Zanni</i></p> <p>6.1 Introduction 139</p> <p>6.2 Fog Computing for IoT: Definition and Requirements 142</p> <p>6.2.1 Definitions 142</p> <p>6.2.2 Motivations 144</p> <p>6.2.3 Fog Computing Requirements When Applied to Challenging IoTs Application Domains 148</p> <p>6.2.4 IoT Case Studies 152</p> <p>6.3 Fog Computing: Architectural Model 154</p> <p>6.3.1 Communication 154</p> <p>6.3.2 Security and Privacy 156</p> <p>6.3.3 Internet of Things 156</p> <p>6.3.4 Data Quality 156</p> <p>6.3.5 Cloudification 157</p> <p>6.3.6 Analytics and Decision-Making 157</p> <p>6.4 Fog Computing for IoT: A Taxonomy 158</p> <p>6.4.1 Communication 159</p> <p>6.4.2 Security and Privacy Layer 165</p> <p>6.4.3 Internet of Things 170</p> <p>6.4.4 Data Quality 173</p> <p>6.4.5 Cloudification 179</p> <p>6.4.6 Analytics and Decision-Making Layer 183</p> <p>6.5 Comparisons of Surveyed Solutions 189</p> <p>6.5.1 Communication 189</p> <p>6.5.2 Security and Privacy 191</p> <p>6.5.3 Internet of Things 193</p> <p>6.5.4 Data Quality 194</p> <p>6.5.5 Cloudification 195</p> <p>6.5.6 Analytics and Decision-Making Layer 197</p> <p>6.6 Challenges and Recommended Research Directions 198</p> <p>6.7 Concluding Remarks 201</p> <p>References 202</p> <p><b>7 Harnessing the Computing Continuum for Programming Our World </b><b>215<br /></b><i>Pete Beckman, Jack Dongarra, Nicola Ferrier, Geoffrey Fox, Terry Moore, Dan Reed, and Micah Beck</i></p> <p>7.1 Introduction and Overview 215</p> <p>7.2 Research Philosophy 217</p> <p>7.3 A Goal-oriented Approach to Programming the Computing Continuum 219</p> <p>7.3.1 A Motivating Continuum Example 219</p> <p>7.3.2 Goal-oriented Annotations for Intensional Specification 221</p> <p>7.3.3 A Mapping and Run-time System for the Computing Continuum 222</p> <p>7.3.4 Building Blocks and Enabling Technologies 224</p> <p>7.4 Summary 228</p> <p>References 228</p> <p><b>8 Fog Computing for Energy Harvesting-enabled Internet of Things </b><b>231<br /></b><i>S. A. Tegos, P. D. Diamantoulakis, D. S. Michalopoulos, and G. K. Karagiannidis</i></p> <p>8.1 Introduction 231</p> <p>8.2 System Model 232</p> <p>8.2.1 Computation Model 233</p> <p>8.2.2 Energy Harvesting Model 235</p> <p>8.3 Tradeoffs in EH Fog Systems 238</p> <p>8.3.1 Energy Consumption vs. Latency 238</p> <p>8.3.2 Execution Delay vs. Task Dropping Cost 239</p> <p>8.4 Future Research Challenges 240</p> <p>Acknowledgment 241</p> <p>References 241</p> <p><b>9 Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control </b><b>245<br /></b><i>Delaram Amiri, Arman Anzanpour, Iman Azimi, Amir M. Rahmani, Pasi Liljeberg, Nikil Dutt, and Marco Levorato</i></p> <p>9.1 Introduction 245</p> <p>9.2 Background 247</p> <p>9.3 Related Topics 249</p> <p>9.4 Design Challenges 250</p> <p>9.5 IoT System Architecture 251</p> <p>9.5.1 Fog Computing and its Benefits 252</p> <p>9.6 Fog-assisted Runtime Energy Management in Wearable Sensors 253</p> <p>9.6.1 Computational Self-Awareness 255</p> <p>9.6.2 Energy Optimization Algorithms 255</p> <p>9.6.3 Myopic Strategy 258</p> <p>9.6.4 MDP Strategy 259</p> <p>9.7 Conclusions 263</p> <p>Acknowledgment 264</p> <p>References 264</p> <p><b>10 Latency Minimization Through Optimal Data Placement in Fog Networks </b><b>269<br /></b><i>Ning Wang and Jie Wu</i></p> <p>10.1 Introduction 269</p> <p>10.2 RelatedWork 272</p> <p>10.2.1 Long-Term and Short-Term Placement 272</p> <p>10.2.2 Data Replication 272</p> <p>10.3 Problem Statement 273</p> <p>10.3.1 Network Model 273</p> <p>10.3.2 Multiple Data Placement with Budget Problem 274</p> <p>10.3.3 Challenges 274</p> <p>10.4 Delay Minimization Without Replication 275</p> <p>10.4.1 Problem Formulation 275</p> <p>10.4.2 Min-Cost Flow Formulation 276</p> <p>10.4.3 Complexity Reduction 277</p> <p>10.5 Delay Minimization with Replication 279</p> <p>10.5.1 Hardness Proof 279</p> <p>10.5.2 Single Request in Line Topology 279</p> <p>10.5.3 Greedy Solution in Multiple Requests 280</p> <p>10.5.4 Rounding Approach in Multiple Requests 282</p> <p>10.6 Performance Evaluation 285</p> <p>10.6.1 Trace Information 285</p> <p>10.6.2 Experimental Setting 285</p> <p>10.6.3 Algorithm Comparison 286</p> <p>10.6.4 Experimental Results 287</p> <p>10.7 Conclusion 289</p> <p>Acknowledgement 289</p> <p>References 290</p> <p><b>11 Modeling and Simulation of Distributed Fog Environment Using FogNetSim++ </b><b>293<br /></b><i>Tariq Qayyum, Asad Waqar Malik, Muazzam A. Khan, and Samee U. Khan</i></p> <p>11.1 Introduction 293</p> <p>11.2 Modeling and Simulation 294</p> <p>11.3 FogNetSim++: Architecture 296</p> <p>11.4 FogNetSim++: Installation and Environment Setup 298</p> <p>11.4.1 OMNeT++ Installation 298</p> <p>11.4.2 FogNetSim++ Installation 300</p> <p>11.4.3 Sample Fog Simulation 300</p> <p>11.5 Conclusion 305</p> <p>References 305</p> <p><b>Part II Fog Computing Techniques and Applications </b><b>309</b></p> <p><b>12 Distributed Machine Learning for IoT Applications in the Fog </b><b>311<br /></b><i>Aluizio F. Rocha Neto, Flavia C. Delicato, Thais V. Batista, and Paulo F. Pires</i></p> <p>12.1 Introduction 311</p> <p>12.2 Challenges in Data Processing for IoT 314</p> <p>12.2.1 Big Data in IoT 315</p> <p>12.2.2 Big Data Stream 318</p> <p>12.2.3 Data Stream Processing 319</p> <p>12.3 Computational Intelligence and Fog Computing 322</p> <p>12.3.1 Machine Learning 322</p> <p>12.3.2 Deep Learning 326</p> <p>12.4 Challenges for Running Machine Learning on Fog Devices 328</p> <p>12.4.1 Solutions Available on the Market to Deploy ML on Fog Devices 331</p> <p>12.5 Approaches to Distribute Intelligence on Fog Devices 334</p> <p>12.6 Final Remarks 340</p> <p>Acknowledgments 341</p> <p>References 341</p> <p><b>13 Fog Computing-Based Communication Systems for Modern Smart Grids </b><b>347<br /></b><i>Miodrag Forcan and Mirjana Maksimović</i></p> <p>13.1 Introduction 347</p> <p>13.2 An Overview of Communication Technologies in Smart Grid 349</p> <p>13.3 Distribution Management System (DMS) Based on Fog/Cloud Computing 356</p> <p>13.4 Real-time Simulation of the Proposed Feeder-based Communication Scheme Using MATLAB and ThingSpeak 359</p> <p>13.5 Conclusion 366</p> <p>References 367</p> <p><b>14 An Estimation of Distribution Algorithm to Optimize the Utility of Task Scheduling Under Fog Computing Systems </b><b>371<br /></b><i>Chu-ge Wu and Ling Wang</i></p> <p>14.1 Introduction 371</p> <p>14.2 Estimation of Distribution Algorithm 372</p> <p>14.3 Related Work 373</p> <p>14.4 Problem Statement 374</p> <p>14.5 Details of Proposed Algorithm 376</p> <p>14.5.1 Encoding and Decoding Method 376</p> <p>14.5.2 uEDA Scheme 377</p> <p>14.5.3 Local Search Method 378</p> <p>14.6 Simulation 378</p> <p>14.6.1 Comparison Algorithm 378</p> <p>14.6.2 Simulation Environment and Experiment Settings 379</p> <p>14.6.3 Compared with the Heuristic Method 381</p> <p>14.7 Conclusion 383</p> <p>References 383</p> <p><b>15 Reliable and Power-Efficient Machine Learning in Wearable Sensors </b><b>385<br /></b><i>Parastoo Alinia and Hassan Ghasemzadeh</i></p> <p>15.1 Introduction 385</p> <p>15.2 Preliminaries and Related Work 386</p> <p>15.2.1 Gold Standard MET Computation 386</p> <p>15.2.2 Sensor-based MET Estimation 387</p> <p>15.2.3 Unreliability Mitigation 388</p> <p>15.2.4 Transfer Learning 388</p> <p>15.3 System Architecture and Methods 389</p> <p>15.3.1 Reliable MET Calculation 390</p> <p>15.3.2 The Reconfigurable MET Estimation System 392</p> <p>15.4 Data Collection and Experimental Procedures 394</p> <p>15.4.1 Exergaming Experiment 394</p> <p>15.4.2 Treadmill Experiment 395</p> <p>15.5 Results 396</p> <p>15.5.1 Reliable MET Calculation 396</p> <p>15.5.2 Reconfigurable Design 402</p> <p>15.6 Discussion and Future Work 404</p> <p>15.7 Summary 405</p> <p>References 406</p> <p><b>16 Insights into Software-Defined Networking and Applications in Fog Computing </b><b>411<br /></b><i>Osman Khalid, Imran Ali Khan, and Assad Abbas</i></p> <p>16.1 Introduction 411</p> <p>16.2 OpenFlow Protocol 414</p> <p>16.2.1 OpenFlow Switch 414</p> <p>16.3 SDN-Based Research Works 416</p> <p>16.4 SDN in Fog Computing 419</p> <p>16.5 SDN in Wireless Mesh Networks 421</p> <p>16.5.1 Challenges in Wireless Mesh Networks 421</p> <p>16.5.2 SDN Technique in WMNs 421</p> <p>16.5.3 Benefits of SDN in WMNs 423</p> <p>16.5.4 Fault Tolerance in SDN-based WMNs 424</p> <p>16.6 SDN in Wireless Sensor Networks 424</p> <p>16.6.1 Challenges in Wireless Sensor Networks 424</p> <p>16.6.2 SDN in Wireless Sensor Networks 425</p> <p>16.6.3 Sensor Open Flow 426</p> <p>16.6.4 Home Networks Using SDWN 426</p> <p>16.6.5 Securing Software Defined Wireless Networks (SDWN) 426</p> <p>16.7 Conclusion 427</p> <p>References 427</p> <p><b>17 Time-Critical Fog Computing for Vehicular Networks </b><b>431<br /></b><i>Ahmed Chebaane, Abdelmajid Khelil, and Neeraj Suri</i></p> <p>17.1 Introduction 431</p> <p>17.2 Applications and Timeliness Guarantees and Perturbations 434</p> <p>17.2.1 Application Scenarios 434</p> <p>17.2.2 Application Model 436</p> <p>17.2.3 Timeliness Guarantees 436</p> <p>17.2.4 Benchmarking Vehicular Applications Concerning Timeliness Guarantees 437</p> <p>17.2.5 Building Blocks to Reach Timeliness Guarantees 440</p> <p>17.2.6 Timeliness Perturbations 441</p> <p>17.3 Coping with Perturbation to Meet Timeliness Guarantees 443</p> <p>17.3.1 Coping with Constraints 443</p> <p>17.3.2 Coping with Failures 448</p> <p>17.3.3 Coping with Threats 448</p> <p>17.4 Research Gaps and Future Research Directions 449</p> <p>17.4.1 Mobile Fog Computing 449</p> <p>17.4.2 Fog Service Level Agreement (SLA) 450</p> <p>17.5 Conclusion 451</p> <p>References 451</p> <p><b>18 A Reliable and Efficient Fog-Based Architecture for Autonomous Vehicular Networks </b><b>459<br /></b><i>Shuja Mughal, Kamran Sattar Awaisi, Assad Abbas, Inayat ur Rehman, Muhammad Usman Shahid Khan, and Mazhar Ali</i></p> <p>18.1 Introduction 459</p> <p>18.2 Proposed Methodology 461</p> <p>18.3 Hypothesis Formulation 463</p> <p>18.4 Simulation Design 464</p> <p>18.4.1 Results and Discussions 464</p> <p>18.4.2 Hypothesis Testing 467</p> <p>18.5 Conclusions 469</p> <p>References 470</p> <p><b>19 Fog Computing to Enable Geospatial Video Analytics for Disaster-incident Situational Awareness</b> <b>473</b><br /><i>Dmitrii Chemodanov, Prasad Calyam, and Kannappan Palaniappan</i></p> <p>19.1 Introduction 473</p> <p>19.1.1 How Can Geospatial Video Analytics Help with Disaster-Incident Situational Awareness? 473</p> <p>19.1.2 Fog Computing for Geospatial Video Analytics 474</p> <p>19.1.3 Function-Centric Cloud/Fog Computing Paradigm 475</p> <p>19.1.4 Function-Centric Fog/Cloud Computing Challenges 476</p> <p>19.1.5 Chapter Organization 477</p> <p>19.2 Computer Vision Application Case Studies and FCC Motivation 478</p> <p>19.2.1 Patient Tracking with Face Recognition Case Study 478</p> <p>19.2.2 3-D Scene Reconstruction from LIDAR Scans 480</p> <p>19.2.3 Tracking Objects of Interest in WAMI 482</p> <p>19.3 Geospatial Video Analytics Data Collection Using Edge Routing 484</p> <p>19.3.1 Network Edge Geographic Routing Challenges 484</p> <p>19.3.2 Artificial Intelligence Relevance in Geographic Routing 486</p> <p>19.3.3 AI-Augmented Geographic Routing Implementation 487</p> <p>19.4 Fog/Cloud Data Processing for Geospatial Video Analytics Consumption 490</p> <p>19.4.1 Geo-Distributed Latency-Sensitive SFC Challenges 491</p> <p>19.4.2 Metapath-Based Composite Variable Approach 492</p> <p>19.4.3 Metapath-Based SFC Orchestration Implementation 495</p> <p>19.5 Concluding Remarks 496</p> <p>19.5.1 What Have We Learned? 496</p> <p>19.5.2 The Road Ahead and Open Problems 497</p> <p>References 498</p> <p><b>20 An Insight into 5G Networks with Fog Computing </b><b>505<br /></b><i>Osman Khalid, Imran Ali Khan, Rao Naveed Bin Rais, and Asad Waqar Malik</i></p> <p>20.1 Introduction 505</p> <p>20.2 Vision of 5G 507</p> <p>20.3 Fog Computing with 5G Networks 508</p> <p>20.3.1 Fog Computing 508</p> <p>20.3.2 The Need of Fog Computing in 5G Networks 508</p> <p>20.4 Architecture of 5G 508</p> <p>20.4.1 Cellular Architecture 508</p> <p>20.4.2 Energy Efficiency 510</p> <p>20.4.3 Two-Tier Architecture 512</p> <p>20.4.4 Cognitive Radio 512</p> <p>20.4.5 Cloud-Based Architecture 513</p> <p>20.5 Technology and Methodology for 5G 514</p> <p>20.5.1 HetNet 515</p> <p>20.5.2 Beam Division Multiple Access (BDMA) 516</p> <p>20.5.3 Mixed Bandwidth Data Path 516</p> <p>20.5.4 Wireless Virtualization 516</p> <p>20.5.5 Flexible Duplex 518</p> <p>20.5.6 Multiple-Input Multiple-Output (MIMO) 518</p> <p>20.5.7 M2M 519</p> <p>20.5.8 Multibeam-Based Communication System 520</p> <p>20.5.9 Software-Defined Networking (SDN) 520</p> <p>20.6 Applications 521</p> <p>20.6.1 Health Care 521</p> <p>20.6.2 Smart Grid 521</p> <p>20.6.3 Logistic and Tracking 521</p> <p>20.6.4 Personal Usage 521</p> <p>20.6.5 Virtualized Home 522</p> <p>20.7 Challenges 522</p> <p>20.8 Conclusion 524</p> <p>References 524</p> <p><b>21 Fog Computing for Bioinformatics Applications </b><b>529<br /></b><i>Hafeez Ur Rehman, Asad Khan, and Usman Habib</i></p> <p>21.1 Introduction 529</p> <p>21.2 Cloud Computing 531</p> <p>21.2.1 Service Models 532</p> <p>21.2.2 Delivery Models 532</p> <p>21.3 Cloud Computing Applications in Bioinformatics 533</p> <p>21.3.1 Bioinformatics Tools Deployed as SaaS 533</p> <p>21.3.2 Bioinformatics Platforms Deployed as PaaS 535</p> <p>21.3.3 Bioinformatics Tools Deployed as IaaS 535</p> <p>21.4 Fog Computing 537</p> <p>21.5 Fog Computing for Bioinformatics Applications 539</p> <p>21.5.1 Real-Time Microorganism Detection System 541</p> <p>21.6 Conclusion 543</p> <p>References 543</p> <p>Index 547</p>
<p><b>Assad Abbas, PhD,</b> is an Assistant Professor in the Department of Computer Science, COMSATS University Islamabad, Pakistan. He is a member of IEEE and IEEE-Eta Kappa Nu (IEEE-HKN). <p><b>Samee U. Khan, PhD,</b> is the Walter B. Booth Endowed Professor at the North Dakota State University, Fargo, ND, USA, and is on the editorial boards of several leading journals. <p><b>Albert Y. Zomaya, PhD,</b> is the Chair Professor of High Performance Computing & Networking in the School of Computer Science, The University of Sydney. He is also the Director of the Centre for Distributed and High Performance Computing.
<p><b>Summarizes the current state and upcoming trends within the area of fog computing</b> <p>Written by some of the leading experts in the field, <i>Fog Computing: Theory and Practice</i> focuses on the technological aspects of employing fog computing in various application domains, such as smart healthcare, industrial process control and improvement, smart cities, and virtual learning environments. In addition, the Machine-to-Machine (M2M) communication methods for fog computing environments are covered in depth. <p>Presented in two parts—Fog Computing Systems and Architectures, and Fog Computing Techniques and Application—this book covers such important topics as energy efficiency and Quality of Service (QoS) issues, reliability and fault tolerance, load balancing, and scheduling in fog computing systems. It also devotes special attention to emerging trends and the industry needs associated with utilizing the mobile edge computing, Internet of Things (IoT), resource and pricing estimation, and virtualization in the fog environments. <ul> <li>Includes chapters on deep learning, mobile edge computing, smart grid, and intelligent transportation systems beyond the theoretical and foundational concepts</li> <li>Explores real-time traffic surveillance from video streams and interoperability of fog computing architectures</li> <li>Presents the latest research on data quality in the IoT, privacy, security, and trust issues in fog computing</li> </ul> <p><i>Fog Computing: Theory and Practice</i> provides a platform for researchers, practitioners, and graduate students from computer science, computer engineering, and various other disciplines to gain a deep understanding of fog computing.

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