The document discusses challenges and opportunities in 5G and beyond networks. It covers expectations of 5G including being open, mobile, programmable, agile, sustainable, scalable, secure and reliable. Virtualization technologies including network functions virtualization, software defined networking, and network slicing are described. Edge and fog computing are discussed as are use cases like augmented reality, smart cities and more. Automation approaches including monitoring, fault detection and security are outlined. The role of edge computing in applications like IoT analytics, video analytics and distributed artificial intelligence is also covered.

1. Challenges and Opportunities in 5G and
Beyond
Professor Tasos Dagiuklas,
SuITE Research Group
Division of Computer Science
London South Bank University
UK
https://www.suitelab.org

2. SuITE Research Group August 2019
Presentation
§ 5G Expectations and Network Evolution
§ Virtualization Technologies Evolution
§ Edge and Fog Computing
§ Automation and Analytics
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3. SuITE Research Group August 2019
Marketing Trends
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Open
Mobile
Programmable
Agile
Sustainable
Scalable
Secure
Reliable
Standardized
Flexibility Robustness
Expectations from 5G Networks
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5. SuITE Research Group August 2019
5G Use Cases
Tactile Internet Augmented Reality
V2X CommunicationsSmart Cities Natural Disaster
Smart Grid
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6. SuITE Research Group August 2019
Network Evolution-(1)
Source: Hans J. Einsiedler, 2018
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7. SuITE Research Group August 2019
Network Evolution-(2)
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8. SuITE Research Group August 2019
The Impact of Softwarization
Ghosh, S., Dagiuklas, T. and Iqbal, M., 2018, December. Energy-Aware IP Routing Over SDN. In
2018 IEEE Global Communications Conference (GLOBECOM).
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9. SuITE Research Group August 2019
Virtual Networking
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Virtual Networking Capabilities
§ Network Address Translation (NAT)
§ NAT Network (NATN)
§ Internal Networking (IN)
§ Bridged Networking (BN)
§ Host-only Networking (HON)
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11. SuITE Research Group August 2019
So#ware
Defined
Networking
(SDN)
API to the data plane!
(e.g., OpenFlow)!
Logically-centralized control!
Switches!
Smart,!
slow!
Dumb,!
fast!
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12. SuITE Research Group August 2019
Unifies
Different
Kinds
of
Boxes
• Router
– Match:
longest
des3na3on
IP
prefix
– Ac3on:
forward
out
a
link
• Switch
– Match:
des3na3on
MAC
address
– Ac3on:
forward
or
flood
• Firewall
– Match:
IP
addresses
and
TCP/UDP
port
numbers
– Ac3on:
permit
or
deny
• NAT
– Match:
IP
address
and
port
– Ac3on:
rewrite
address
and
port
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Network Slicing-(1)
§ Network slicing allows creating multiple virtual
networks over the same physical network
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14. SuITE Research Group August 2019
Network Slicing-(2)
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15. SuITE Research Group August 2019
Presentation
§ 5G Expectations and Network Evolution
§ Virtualization Technologies Evolution
§ Edge and Fog Computing
§ Automation and Analytics
CollaborateCom 2019

16. SuITE Research Group August 201916
Virtualization Evolution
VMware ESX, Microsoft Hyper-V, Xen
Hardware
Hypervisor
VM1 VM2
Type 1 (bare-metal)
Host
Guest
Hardware
OS
Process Hypervisor
VM1 VM2
Type 2 (hosted)
VMware Workstation, Microsoft Virtual PC,
Sun VirtualBox, QEMU, KVM
Host
Guest
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17. SuITE Research Group August 2019
Containers
§ An open source application portability platform where
packages and its dependent component parts into a
hardware isolated container
§ Containers provide a more efficient use of resources
§ It eliminates hypervisor reducing virtualization
overhead
§ Containers are isolated, but share OS and, where
appropriate, bins/libraries
– result is significantly faster deployment, much less
overhead, easier migration, faster restart
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New Application Architectures
Virtual Machines Cloud Native Applications
Server/hypervisor Server/Containers
Dependencies Easy upgrade
Stateful Microservices
Waterfall Agile
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Microservices Characteristics
§ Many smaller (fine grained), clearly
scoped services
– Single Responsibility Principle
– Independently Managed
§ Clear ownership for each service
– Typically need/adopt the “DevOps” model
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20. SuITE Research Group August 2019
Cloud Native Architectures
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VMs versus Containers
VM Dockers
Size
Startup
Integration
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22. SuITE Research Group August 2019
Presentation
§ 5G Expectations and Network Evolution
§ Virtualization Technologies Evolution
§ Edge and Fog Computing
§ Automation and Analytics
CollaborateCom 2019

23. SuITE Research Group August 2019
Many names for a similar concept
Multi-Access Edge
Computing (ETSI)
Open Edge Computing (Cloudlet)
(Intel/IBM)
Micro datacenter
(Microsoft)
Fog Computing
(Cisco)
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24. SuITE Research Group August 2019
Cloud Computing vs Edge Computing
Comparison-(1)
Characteristics Cloud Edge
Target Users Internet Users Mobile Users
Access Fixed-Wireless Wireless
Service Type Global information Limited Localized information
Hardware Scalable storage space Limited storage, wireless
Number of Nodes Small Large
Distance to users Multiple-Hop Single Hop
Deployment Centralized Distributed
Content generator Centrally Anywhere
Content consumption End-Device Anywhere
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25. SuITE Research Group August 2019
Cloud Computing vs Edge Computing
Comparison-(2)
Parameters Cloud Edge
Latency High Low
Jitter High Low
Security Non-locally controllable Locally Controlled
Real-time Interactions Not-supported Supported
Mobility Limited Fully supported
Location Awareness No Yes
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26. SuITE Research Group August 2019
Edge and fog characteristics
Low Capacity High
High Volatility Low
Third
Party
Third
Party
Mobile
Operator
Mobile
Operator
Third
Party
Industry 4.0 RAT Core Networks Internet
Fog Nodes Fog Nodes MEC Nodes MEC Nodes Cloud
NetworkingComputingResourceResource
resourcesresourcesfeaturesownership
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27. SuITE Research Group August 2019
Use Case Scenarios
§ Face recognition
§ Object recognition
§ Activity recognition
§ Augmented reality
§ CDN Caching
§ V2X Communications
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28. SuITE Research Group August 2019
RAN-aware Content Optimization
§ The application exposes accurate cell and subscriber radio interface
information to the content optimizer, enabling dynamic content
optimization, improving QoE, network efficiency and enabling new
service & revenue opportunities.
§ Dynamic content optimization enhances video delivery through
reduced stalling, reduced time-to-start and ‘best’ video quality
Source: MEC ETSI
Bulkan, U. et al, 2018. On the Load Balancing of Edge Computing resources for on-line video delivery.
IEEE Access, 6, pp.73916-73927.
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29. SuITE Research Group August 2019
IoT Data Analytics
§ IoT generates additional messaging on telecoms
networks, requiring gateways to aggregate the
messages
§ Need for an analytics processing capability and a
low latency response time.
Source: MEC ETSI
Ugwuanyi, E.E., Ghosh, S., Iqbal, M. and Dagiuklas, T., 2018. Reliable resource provisioning using
Bankers’ deadlock avoidance algorithm in MEC for industrial IoT. IEEE Access, 6, pp.43327-43335.
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30. SuITE Research Group August 2019
Co-operative MEC
Ugwuanyi, E.E., Ghosh, S., Iqbal, M. and Dagiuklas, T., 2018. Reliable resource provisioning using
Bankers’ deadlock avoidance algorithm in MEC for industrial IoT. IEEE Access, 6, pp.43327-43335.
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31. SuITE Research Group August 2019
Presentation
§ 5G Expectations and Use Cases
§ Virtualization Technologies Evolution
§ Edge and Fog Computing
§ Automation and Analytics
CollaborateCom 2019

32. SuITE Research Group August 2019
Automation in 5G
• User Requirements
• Service Requirements
• Business Goals
Planning &
Design
• Network Topology (e.g. VNF placement)
• Service Chaining
• Resource Adjustment
• Policy Adaptation
• Mobility
Deployment
OAM
Monitoring
&
Fault Detection
• Detect Anomalies in Traffic and the workload
• Fault localization
• Recovery
Security
• DPI
• Security Threats
• Isolation of infected components
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33. SuITE Research Group August 2019
Knowledge Data Networking

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Experimental Edge/Fog Architectures
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35. SuITE Research Group August 2019
Edge/Fog Monitoring
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36. SuITE Research Group August 2019
Containerized VNF Migration-(1)
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37. SuITE Research Group August 2019
Containerized VNF Migration-(2)
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AI Application Requirements on the Edge
• Sensor Analysis
• Activity Recognition
• Stress Analysis
Low Medium High
• Audio and signal
• Speech recognition
• Video analytics
• Object detection &
classification
• Speech synthesis
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§ Conventional AI processing
− Large sophisticated machine learning / deep learning models are
computationally-intensive and are often executed on cloud
backend (with powerful CPUs and GPUs).
§ Distributed AI techniques
− Edge nodes (e.g. MEC, fog) are powerful enough to perform
simple AI processing
− Develop distributed AI techniques where the processing is
distributed over edge computing.
− The edge nodes will run simple machine learning models for
event detections and preliminary analysis.
− The event detection and preliminary analysis results, together
with the relevant sensor data, are sent to the cloud backend for
more detailed processing.
Motivation for AI in the Edge
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40. SuITE Research Group August 2019
Google TPU
§ Edge TPU By Google has
been designed to run
TensorFlow Lite ML
inference at the edge
§ Need for an analytics
processing capability and a
low latency response time.
https://aiyprojects.withgoogle.com/edge-tpu/
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41. SuITE Research Group August 2019
Video Analytics
• Number of People
• Face Recognition
• Behaviour Analytics
• Gender Identification
Edge
Cloud
Gateway
Smart Video Analytics
Video Camera
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42. SuITE Research Group August 2019
Questions
Email: tdagiuklas@lsbu.ac.uk
URL: www.suitelab.org
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