Computing in Communication Networks: From Theory to Practice

Contents
List of contributors
About the editors
Preface from the editors
Acknowledgments
Acronyms
Part 6 Examples
1 On the need of computing in future communication networks
	1.1 Evolution of communication networks
		1.1.1 The telephone networks: circuit-switched
		1.1.2 The Internet: packet-switched
		1.1.3 The cellular communication networks
	1.2 The 5G communication system
		1.2.1 The 5G Atom core: use cases
			1.2.1.1 Connected autonomous cars
			1.2.1.2 Industry 4.0
			1.2.1.3 Agriculture
			1.2.1.4 Energy grid
			1.2.1.5 Tactile Internet
		1.2.2 First tier: the technical requirements
			1.2.2.1 Latency and jitter
			1.2.2.2 Throughput
			1.2.2.3 Resilience
			1.2.2.4 Security
			1.2.2.5 Massiveness
			1.2.2.6 Heterogeneity
			1.2.2.7 Energy consumption
			1.2.2.8 Technical requirements per use case
				Technical requirements: connected autonomous cars
				Technical requirements: Industry 4.0
				Technical requirements: Agriculture 4.0
				Technical requirements: energy grids
				Technical requirements: tactile Internet
		1.2.3 Second tier: the concepts
			1.2.3.1 New air interface concept
			1.2.3.2 Mesh
			1.2.3.3 Multipath communication and multiconnectivity
			1.2.3.4 Content delivery networks
			1.2.3.5 Information-centric networks
			1.2.3.6 Network slicing
			1.2.3.7 Mobile edge cloud
		1.2.4 Third tier: the softwarization technologies
			1.2.4.1 Software-defined radio
			1.2.4.2 Software-defined networks
			1.2.4.3 Network function virtualization
			1.2.4.4 Service function chaining
		1.2.5 Fourth tier: innovation and novelties
			1.2.5.1 Block chaining
			1.2.5.2 Machine learning
			1.2.5.3 Network coding
			1.2.5.4 Compressed sensing
	1.3 Softwarization: the game changer for network operators
2 Standardization activities for future communication networks
	2.1 Introduction
	2.2 Standardization in telecommunications
	2.3 Standardization of future generation networks
		2.3.1 3GPP standardization
		2.3.2 ETSI standardization
		2.3.3 ITU-T standardization
		2.3.4 IETF/IRTF standardization
Part 7 Extensions
3 Network slicing
	3.1 Introduction
	3.2 Network slice: concept and life cycle
	3.3 Network slicing architectures
		3.3.1 Single owner, single controller
		3.3.2 Single owner, multiple tenants - SDN proxy
		3.3.3 Multiple owners, tenants
	3.4 Network slicing examples
4 Mobile edge cloud
	4.1 Introduction
	4.2 Mobile edge cloud
		4.2.1 Similar concepts
		4.2.2 Characteristics
		4.2.3 Key enablers
		4.2.4 General architecture
	4.3 MANO frameworks
	4.4 MEC example implementations
		4.4.1 Tron demonstrator
		4.4.2 Ball sorting machine
		4.4.3 Ambulance demonstrator
		4.4.4 Seamless migration for autonomous cars
5 Content distribution
	5.1 Introduction
	5.2 Content delivery networks
		5.2.1 Content distribution
		5.2.2 Request routing
	5.3 Information-centric networking
		5.3.1 Operation primitives and packet types
		5.3.2 Content naming
		5.3.3 In-network caching
		5.3.4 Node architecture and packet handling
		5.3.5 Content-based security
Part 3 Enabling technologies
6 Software-defined networks
	6.1 Networking in today's Internet
	6.2 The road to SDN
		6.2.1 What is software-defined networking?
		6.2.2 Architecture
		6.2.3 SDN use cases
			6.2.3.1 Maintenance dry-out
			6.2.3.2 Traffic scheduling and predictability
			6.2.3.3 Service function chaining
			6.2.3.4 User handover
			6.2.3.5 Network access control
	6.3 Technologies and standards
		6.3.1 SDN controllers
		6.3.2 SDN switches
		6.3.3 OpenFlow
			6.3.3.1 Flow table
			6.3.3.2 Classifiers and actions
			6.3.3.3 Workflow of OpenFlow
		6.3.4 P4
		6.3.5 NETCONF
7 Network function virtualization
	7.1 Introduction
	7.2 Network function virtualization
	7.3 NFV-SDN architectures
	7.4 Programmable protocol stack
	7.5 Virtualization of RAN and BBU splitting
Part 8 Tools
8 Machine learning
	8.1 Introduction
	8.2 Supervised learning
		8.2.1 Problem formulation
		8.2.2 Supervised learning workflow
			8.2.2.1 Feature encoding
				Label encoding
				One-hot encoding
			8.2.2.2 Commonly used distance measures
				Mean squared error
				Categorical cross-entropy
			8.2.2.3 Error minimization: gradient descent
				Gradient descent
				Stochastic gradient descent
				Minibatch gradient descent
			8.2.2.4 Predicting probability distributions: SoftMax
			8.2.2.5 Overfitting vs. underfitting
				Underfitting
				Overfitting
				L1 regularization
				L2 regularization
				Early stopping
		8.2.3 Linear and logistic regression
			8.2.3.1 Linear regression
				Optimal solution
			8.2.3.2 Logistic regression
		8.2.4 Support vector machines
			8.2.4.1 Linear separation
			8.2.4.2 Linear separation with margin
			8.2.4.3 Nonlinear separation
		8.2.5 Decision trees
			8.2.5.1 Training a decision tree: the CART algorithm
				Split (im)purity
		8.2.6 Artificial neural networks
			8.2.6.1 Artificial Neural Network (ANN) fundamentals
			8.2.6.2 Layers
			8.2.6.3 Training with backpropagation
			8.2.6.4 Best practices, new trends
		8.2.7 Convolutional neural networks
			8.2.7.1 Convolutional layers
			8.2.7.2 Pooling layers
			8.2.7.3 Residual (skip) connections
	8.3 Intermission
	8.4 Reinforcement learning
		8.4.1 Finite Markov decision processes
		8.4.2 Q-learning
		8.4.3 The exploration vs. exploitation dilemma
			8.4.3.1 The ε-greedy policy
			8.4.3.2 The upper confidence bound policy
		8.4.4 Deep Q-learning
9 Network coding
	9.1 Interflow network coding - the basics
		9.1.1 The butterfly network
		9.1.2 Alice and Bob topology
		9.1.3 The X topology
		9.1.4 The cross topology
	9.2 Intraflow network coding - now it gets interesting
		9.2.1 How to create coded packets
			A note on practical hands-on in Python
				9.2.1.1 Coding a packet with a binary field size
				9.2.1.2 Coding a packet with a larger field size
				9.2.1.3 Recoding coded packets
		9.2.2 RLNC and the butterfly
		9.2.3 Impact of the coding parameters
			9.2.3.1 Overhead due to linear dependencies
			9.2.3.2 Computational complexity
			9.2.3.3 Overhead due to the coding coefficients
		9.2.4 The potential of recoding
10 Compressed sensing
	10.1 Compressed sensing theory
		10.1.1 Problem formulation
		10.1.2 Mathematical background
			10.1.2.1 Basis and frame of a vector space
				Basis
				Example
				Frame
			10.1.2.2 Norms
			10.1.2.3 Orthogonal matrices
			10.1.2.4 Matrix decomposition
			10.1.2.5 Kronecker product
		10.1.3 Sparse and compressible signals
		10.1.4 Measurement matrix design
			10.1.4.1 Mutual coherence
			10.1.4.2 Null space property
			10.1.4.3 Restricted isometry property
	10.2 Basic reconstruction algorithms
		10.2.1 Convex relaxation
		10.2.2 Greedy algorithms
			10.2.2.1 Greedy pursuits
				Orthogonal Matching Pursuit (OMP)
			10.2.2.2 Thresholding
		10.2.3 Message passing
		10.2.4 Reconstruction strategies discussion
	10.3 Sparse representation
		10.3.1 Well-known transforms
		10.3.2 Sparsifying dictionary/dictionary learning
			10.3.2.1 K-SVD algorithm
	10.4 Distributed compressed sensing
		10.4.1 Joint sparsity models
			10.4.1.1 Sparse common component + innovations (JSM-1)
			10.4.1.2 Common sparse supports model (JSM-2)
			10.4.1.3 Nonsparse common component + sparse innovations (JSM-3)
		10.4.2 DCS reconstruction algorithms
	10.5 Compressed sensing for communications
		10.5.1 Compressed sensing for WSN
		10.5.2 Kronecker compressed sensing
			10.5.2.1 Kronecker product sparsifying bases
			10.5.2.2 Kronecker product measurement matrices
Part 5 Building the testbed
11 Mininet: an instant virtual network on your computer
	11.1 Introduction
	11.2 Mininet workflow
		11.2.1 Create a network topology
		11.2.2 Interact with a network
		11.2.3 Programmable network with SDN
	11.3 Demystifying Mininet
		11.3.1 Resource management and isolation
			11.3.1.1 Linux NS
			11.3.1.2 Linux Cgroups
		11.3.2 Configurable data plane
			11.3.2.1 Linux virtual ethernet pairs (veth pairs)
			11.3.2.2 Linux traffic control
			11.3.2.3 Virtual switch
	11.4 Create a tiny topology from scratch
12 Docker: containerize your application
	12.1 Introduction to Docker
	12.2 Containers vs virtual machines
	12.3 Management, orchestration and external tools
		12.3.1 Kubernetes
		12.3.2 Docker Swarm
	12.4 Getting started with Docker
		12.4.1 Basic commands
			12.4.1.1 Docker images
			12.4.1.2 Docker containers
		12.4.2 Building an image - Dockerfile
		12.4.3 Services and stacks
		12.4.4 Docker Swarm
13 ComNetsEmu: a lightweight emulator
	13.1 Introduction
	13.2 ComNetsEmu in a nutshell
		13.2.1 Test environment management
		13.2.2 Application container management
	13.3 Examples for getting started
		13.3.1 Echo server
		13.3.2 Docker-in-Docker for resource limitation
Part 1 Future communication networks and systems
14 Realizing network slicing
	14.1 Network slicing in Mininet
		14.1.1 Introduction
		14.1.2 Link capacity slicing
	14.2 Network slicing in ComNetsEmu
		14.2.1 Example 1: topology slicing
			14.2.1.1 Implementation
			14.2.1.2 Validation
		14.2.2 Example 2: service slicing
			14.2.2.1 Implementation
			14.2.2.2 Validation
		14.2.3 Example 3: SDN proxy-based slicing
			14.2.3.1 Implementation
			14.2.3.2 Validation
15 Realizing mobile edge clouds
	15.1 Introduction
	15.2 Mechanisms and practical implementation
		15.2.1 Without SDN/NFV technologies
		15.2.2 With SDN/NFV technologies
	15.3 ComNetsEmu experimentation
	15.4 Emulation results
		15.4.1 Latency measurement results on SDN controller
		15.4.2 Latency measurement at client side
16 Machine learning for routing
	16.1 Introduction
	16.2 Fitting reinforcement learning to routing
		16.2.1 Designing state and action space
		16.2.2 Reward
		16.2.3 Exploration
	16.3 Example
		16.3.1 Setup
		16.3.2 Running the example
		16.3.3 Discussion
		16.3.4 Changing parameters
17 Machine learning for flow compression
	17.1 Introduction
	17.2 The compression oracle
	17.3 The O2SC library
		17.3.1 Examples of predefined oracles
		17.3.2 Defining oracles using machine learning
	17.4 Examples
	17.5 The interactive environment
18 Machine learning for congestion control
	18.1 Introduction
	18.2 Characterizing congestion
	18.3 Congestion window
	18.4 Designing the agent
	18.5 Example with ComNetsEmu
	18.6 Exercises
		18.6.1 Exercise 1
		18.6.2 Exercise 2
		18.6.3 Exercise 3
19 Machine learning for object detection
	19.1 Introduction
	19.2 Distributed YOLO with compression
		19.2.1 Distributed YOLO: VNF and server
		19.2.2 Model split
		19.2.3 Inside YOLO
		19.2.4 Feature map compression
	19.3 Examples
		19.3.1 Infinite forwarding VNF
		19.3.2 Limited forwarding VNF
20 Network coding for transport
	20.1 Introduction
	20.2 Network coding as virtualized network function
		20.2.1 Virtualization approaches
		20.2.2 Coding the traffic
	20.3 Multihop recoding example
	20.4 Adaptive redundancy example
		20.4.1 Delivery probability of packets
		20.4.2 Running the example
		20.4.3 Example results
21 Network coding for storage
	21.1 Introduction
	21.2 Distributed storage
	21.3 Network coding in distributed storage
	21.4 Running the example
		21.4.1 Uncoded repair
		21.4.2 Simple network code with replication
		21.4.3 Network coding with recoding
22 In-network compressed sensing
	22.1 Introduction
	22.2 Point-to-point scenario
		22.2.1 Using DCT for data sparsification
		22.2.2 Using a trained dictionary for data sparsification
	22.3 Single-cluster scenario
		22.3.1 Using DCT for data sparsification
		22.3.2 Using a trained dictionary for data sparsification
			22.3.2.1 Overcomplete dictionary robustness
	22.4 Next steps
23 Security for mobile edge cloud
	23.1 Introduction
	23.2 Network segmentation
		23.2.1 Concepts
		23.2.2 Implementation
		23.2.3 nftables
	23.3 Network isolation exercise
		23.3.1 Blacklisting and whitelisting
		23.3.2 Stateful filtering
		23.3.3 Chains and jumps
	23.4 Secure network tunnels
		23.4.1 Concepts
		23.4.2 Implementation
		23.4.3 Wireguard
	23.5 Secure network tunnel exercise
		23.5.1 Man-in-the-middle
		23.5.2 Tunnel network
Part 2 Concepts
24 Connecting to the outer world
	24.1 Introduction
	24.2 Connecting ComNetsEmu to the Internet
		24.2.1 Manual host configuration
			24.2.1.1 Checking connectivity and NIC of the host
			24.2.1.2 Running an example network
			24.2.1.3 Connecting the guest interface to the OVS bridge
			24.2.1.4 Update IP addresses on the hosts
		24.2.2 Using NAT service
		24.2.3 Using DNS resolution
	24.3 Connecting different test bed VMs
	24.4 Exercises
		24.4.1 Exercise 1
		24.4.2 Exercise 2
25 Integrating time-sensitive networking
	25.1 Introduction
	25.2 IEEE802.1AS - if timing matters
	25.3 Different shapes of packets - IEEE802.1Qav and IEEE802.1Qbv
		25.3.1 Credit-based shaper
		25.3.2 Time-aware shaper
	25.4 IEEE802.1Qci - you shall not pass!
	25.5 IEEE802.1Qbu, IEEE802.3br - filling the gaps
	25.6 Hands-on: time-sensitive queueing in the new Linux kernel 5.2
		25.6.1 ComNetsEmu setup
		25.6.2 Using the TAS simulator
		25.6.3 Preparing the TAS
		25.6.4 Measurement and results
26 Integrating software-defined radios
	26.1 Introduction
	26.2 Basic principles
		26.2.1 What is programmable in SDR?
		26.2.2 Design considerations
		26.2.3 Design constraints
	26.3 Software stacks
		26.3.1 Universal Software Radio Peripheral (USRP)
		26.3.2 GNU radio
	26.4 Examples
		26.4.1 Setup
		26.4.2 OFDM transceiver exercise
			26.4.2.1 Execution
			26.4.2.2 Results and analysis
		26.4.3 Latency measurement exercise
			26.4.3.1 Execution
			26.4.3.2 Results and analysis
Part 4 Innovation track
27 Networking tools
	27.1 Connectivity testing - ping
	27.2 Basic network administration - iproute2
		27.2.1 ip addr
		27.2.2 ip link
		27.2.3 ip route
	27.3 Traffic generation - iPerf
	27.4 Process monitoring - htop
	27.5 Network traffic manipulation - TC
	27.6 Traffic monitoring - tcpdump/Wireshark
		27.6.1 tcpdump
		27.6.2 Wireshark
			27.6.2.1 Main features
			27.6.2.2 Installation
			27.6.2.3 User interface
	27.7 Rapid Python prototyping - Jupyter
	27.8 Hands-on example to tie all tools together
Bibliography
Index