Computing in Communication Networks: From Theory to Practice provides comprehensive details and practical implementation tactics on the novel concepts and enabling technologies at the core of the paradigm shift from store and forward (dumb) to compute and forward (intelligent) in future communication networks and systems. The book explains how to create virtualized large scale testbeds using well-established open source software, such as Mininet and Docker. It shows how and where to place disruptive techniques, such as machine learning, compressed sensing, or network coding in a newly built testbed. In addition, it presents a comprehensive overview of current standardization activities.
Specific chapters explore upcoming communication networks that support verticals in transportation, industry, construction, agriculture, health care and energy grids, underlying concepts, such as network slicing and mobile edge cloud, enabling technologies, such as SDN/NFV/ ICN, disruptive innovations, such as network coding, compressed sensing and machine learning, how to build a virtualized network infrastructure testbed on one’s own computer, and more.
1. Introduction - p. 1 2. Chapter 1: On the need of computing in future communication networks - p. 3 3. Chapter 2: Standardization activities for future communication networks - p. 47 4. Part 2: Concepts - p. 59 5. Introduction - p. 61 6. Chapter 3: Network slicing - p. 63 7. Chapter 4: Mobile edge cloud - p. 77 8. Chapter 5: Content distribution - p. 93 9. Part 3: Enabling technologies - p. 103 10. Introduction - p. 105 11. Chapter 6: Software-defined networks - p. 107 12. Chapter 7: Network function virtualization - p. 119 13. Part 4: Innovation track - p. 132 14. Introduction - p. 133 15. Chapter 8: Machine learning - p. 135 16. Chapter 9: Network coding - p. 169 17. Chapter 10: Compressed sensing - p. 197 18. Part 5: Building the testbed - p. 215 19. Introduction - p. 217 20. Chapter 11: Mininet: an instant virtual network on your computer - p. 219 21. Chapter 12: Docker: containerize your application - p. 231 22. Chapter 13: ComNetsEmu: a lightweight emulator - p. 245 23. Part 6: Examples - p. 256 24. Introduction - p. 257 25. Chapter 14: Realizing network slicing - p. 259 26. Chapter 15: Realizing mobile edge clouds - p. 277 27. Chapter 16: Machine learning for routing - p. 289 28. Chapter 17: Machine learning for flow compression - p. 297 29. Chapter 18: Machine learning for congestion control - p. 313 30. Chapter 19: Machine learning for object detection - p. 325 31. Chapter 20: Network coding for transport - p. 339 32. Chapter 21: Network coding for storage - p. 351 33. Chapter 22: In-network compressed sensing - p. 361 34. Chapter 23: Security for mobile edge cloud - p. 371 35. Part 7: Extensions - p. 385 36. Introduction - p. 387 37. Chapter 24: Connecting to the outer world - p. 389 38. Chapter 25: Integrating time-sensitive networking - p. 401 39. Chapter 26: Integrating software-defined radios - p. 413 40. Part 8: Tools - p. 427 41. Introduction - p. 429 42. Chapter 27: Networking tools - p. 431 43. Bibliography - p. 457 44. Index - p. 473
Computing in Communication Networks: From Theory to Practice provides comprehensive details and practical implementation tactics on the novel concepts and enabling technologies at the core of the paradigm shift from store and forward (dumb) to compute and forward (intelligent) in future communication networks and systems. The book explains how to create virtualized large scale testbeds using well-established open source software, such as Mininet and Docker. It shows how and where to place disruptive techniques, such as machine learning, compressed sensing, or network coding in a newly built testbed. In addition, it presents a comprehensive overview of current standardization activities.
Specific chapters explore upcoming communication networks that support verticals in transportation, industry, construction, agriculture, health care and energy grids, underlying concepts, such as network slicing and mobile edge cloud, enabling technologies, such as SDN/NFV/ ICN, disruptive innovations, such as network coding, compressed sensing and machine learning, how to build a virtualized network infrastructure testbed on one’s own computer, and more.
1. Introduction - p. 1 2. Chapter 1: On the need of computing in future communication networks - p. 3 3. Chapter 2: Standardization activities for future communication networks - p. 47 4. Part 2: Concepts - p. 59 5. Introduction - p. 61 6. Chapter 3: Network slicing - p. 63 7. Chapter 4: Mobile edge cloud - p. 77 8. Chapter 5: Content distribution - p. 93 9. Part 3: Enabling technologies - p. 103 10. Introduction - p. 105 11. Chapter 6: Software-defined networks - p. 107 12. Chapter 7: Network function virtualization - p. 119 13. Part 4: Innovation track - p. 132 14. Introduction - p. 133 15. Chapter 8: Machine learning - p. 135 16. Chapter 9: Network coding - p. 169 17. Chapter 10: Compressed sensing - p. 197 18. Part 5: Building the testbed - p. 215 19. Introduction - p. 217 20. Chapter 11: Mininet: an instant virtual network on your computer - p. 219 21. Chapter 12: Docker: containerize your application - p. 231 22. Chapter 13: ComNetsEmu: a lightweight emulator - p. 245 23. Part 6: Examples - p. 256 24. Introduction - p. 257 25. Chapter 14: Realizing network slicing - p. 259 26. Chapter 15: Realizing mobile edge clouds - p. 277 27. Chapter 16: Machine learning for routing - p. 289 28. Chapter 17: Machine learning for flow compression - p. 297 29. Chapter 18: Machine learning for congestion control - p. 313 30. Chapter 19: Machine learning for object detection - p. 325 31. Chapter 20: Network coding for transport - p. 339 32. Chapter 21: Network coding for storage - p. 351 33. Chapter 22: In-network compressed sensing - p. 361 34. Chapter 23: Security for mobile edge cloud - p. 371 35. Part 7: Extensions - p. 385 36. Introduction - p. 387 37. Chapter 24: Connecting to the outer world - p. 389 38. Chapter 25: Integrating time-sensitive networking - p. 401 39. Chapter 26: Integrating software-defined radios - p. 413 40. Part 8: Tools - p. 427 41. Introduction - p. 429 42. Chapter 27: Networking tools - p. 431 43. Bibliography - p. 457 44. Index - p. 473
