Cognitive Network Control for 5G

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1 Cognitive Network Control for 5G Hemant Kumar Rath Senior Scientist, TCS Research and Innovation, Bangalore/Bhubaneswar India Sept

2 Introduction - 5G Networks 5G Use Cases Managing the Network Cognitive Control Middle Layer based solution Conclusion #

3 Use Cases Vertical Industries Categorization Automated Factories Industry process automation, automated production line, supply chain management, warehouse management Automated Transport Assisted driving, autonomous driving, in-vehicle media Healthcare Energy Insurance Education Robotics Surgery, remote health monitoring Smart grid, smart meter Real-time inspection Remote education, skill development 3

4 Use Cases Vertical Industries Transformation Modern Warehouse Rescue Operation Remote Machine Maintenance Robotic Surgery 4 Modern Industry

5 Use Cases - Categorization emmb mmtc URLLC 5

6 Understanding the Challenges Transformation Challenges Guaranteed communication with mobility support Link, coverage and congestion prediction and handoff between stations Moderate to huge data volume degree stereo cameras! Moderate to low packet error rate to 10-10! Extremely low to moderate latency requirement: 10 msec for applications, µsecs for machine control Cost effective, reliable, stable, scalable and vendor-independent solutions Operational Challenges Quality control, data analytics, coordination, Inventory control support Minimal configuration, per-flow provisioning, parameterization of QoS Application layer prospective - technology and protocol agnostic Solution for Greenfield as well as existing use cases 6

7 5G, the Answer Network Speed 10 GB/s 100 times faster than 4G Mobility beyond 350 km/h Latency 1 ms to support Augmented Reality and Tactile Internet An agreement to set of new requirements for wireless comm. systems that mature beyond 2020 IoT support Must Density Very dense, /km 2 Cost & Energy Low 7

8 Five G s of 5G Gigabit Speed 10 Gbps or more Gigahertz frequencies Above 6 GHz Greater Flexibility Rapidly adapt to a broader range of requirements and demands Gizmos and Gadgets M2M opportunities for billions of things Global Competitions Global race for leadership device manufacturers, service creators, app developers and operators 8

9 5G Realization! Newer Frequency bands are to be considered Mix technology will rule More and more cells required 9

Network Topologies in 5G - what is desired Decoupling of Control and Forwarding plane in the GW Forwarding plane Simplifications, sustainability,

10 Network Topologies in 5G - what is desired Decoupling of Control and Forwarding plane in the GW Forwarding plane Simplifications, sustainability, performance, unified control policy, guaranteed scheduling Centralized control plane latency reduction Three clouds Access, Control and Forwarding clouds 10

Network Topologies in 5G - what is desired Control Function Reconstructions

logic Signaling delay can be reduced Diverse networks can be realized and easy

parameters will be diverse Terminal capability, location and mobility history,

State separated core State information (mobility) and processing logic plane

management can be made possible 4G Mobility management protocols GTP and PMIP

11 Network Topologies in 5G - what is desired Control Function Reconstructions Divide the control plane with more and more logical units Optimized process logic Signaling delay can be reduced Diverse networks can be realized and easy for standardization New Connections and Mobility Management Connection parameters will be diverse Terminal capability, location and mobility history, request type, service feature, etc. State separated core State information (mobility) and processing logic plane can be decoupled State information can be centrally available Dynamic load management can be made possible 4G Mobility management protocols GTP and PMIP should go Unified mobility management protocols needed Mobility as a service, location aware services, etc Improved vertical and horizontal handover 11

5G Network Characteristics (1/2) Access Plane Inter-cell

provisioning Different network topographies Control Plane

On-demand orchestration New Service exposure layer Forward

12 5G Network Characteristics (1/2) Access Plane Inter-cell coordination Improved resource usage Different service provisioning Different network topographies Control Plane Centralized control functioning Globally resource scheduling On-demand orchestration New Service exposure layer Forward Plane Simple gateway functions Distributed deployment Low latency and high data rate 12

Control function reconstruction Mobility Management Network Capacity Exposure Value added services

13 5G Network Characteristics (2/2) Access Plane Mesh and ad-hoc networks Radio resource sharing Customized network and services Awareness and treatment New network technologies Multi-RAT Convergence Control Plane Control function reconstruction Mobility Management Network Capacity Exposure Value added services On-demand Networking Forward Plane Gateway C/U Split Mobile edge contents and computing End-to-end requirements 13

Intelligent access control and management

14 Network Topologies in 5G Key Points On-Demand Networking Network orchestrator plays the major role NFV/SDN brings vritualization Multi-RAT Cooperation Intelligent access control and management Wireless resource management and Dynamic load management Protocol and signaling optimization Multi-node and connections Spectrum sharing can be thought of 14

Network Architecture for 5G Service layer architecture for 5G Micro service based

support: Hybrid network support WiFi, LTE/LTE-A, LAA Service based slicing. vs.

15 Network Architecture for 5G Service layer architecture for 5G Micro service based architecture for auto scaling with access network focus Multi-operator and multi-technology support: Hybrid network support WiFi, LTE/LTE-A, LAA Service based slicing. vs. application based slicing vs. flow based slicing Vertical business services supt. apps with comm. c/s Application aware network slicing, virtualization, cloud management, prediction and control Viz: Automotive Vehicle: Driving Ast (URLLC), Ent. (embb), Sensors (mmtc) 5G Overall Architecture (5GPPP) 15

SDNization Possible Solution Application layer specific solution to manage both the application and lower layer protocols Adaptive

for control and data extended to applications Independent application layer modules to be defined which are to be used as the core for a new

application dependent modules Brings programmability and separation of network with slices same hardware Slicing is possible SDNization

16 SDNization Possible Solution Application layer specific solution to manage both the application and lower layer protocols Adaptive applications and networks Should be able to use for existing and green-field solutions Dual connectivity New Radio can be realized SDN vision for control and data extended to applications Independent application layer modules to be defined which are to be used as the core for a new service Service or application agnostic modules vs. application dependent modules Brings programmability and separation of network with slices same hardware Slicing is possible SDNization requires adaptive applications and networking Application should be flexible to run in different networks & devices Networks should be flexible, scalable and easily configurable No physically separate network for different application/services 16

17 Translating the SDNization (1/2) Understanding the applications/use cases QoS guarantee, compute and communication issues Understanding the network Kind of network, protocols, controllability of the network and protocols Real-time behavior of the network and protocols Capability of the underlying elements Taking a decision Based on the application requirement and network behavior Can be real-time or off-line Deploying the decision Fine tuning the application, controlling the protocols and provisioning the network Deciding the optimal deployment decision based on optimization 17

18 Translating the SDNization (2/2) Network aware Applications Application parameters can be decided based on the service definition Real-time application parameters can be tuned based on the network data Device data also plays a role Telematic and sensed + synthetic data have different QoS requirements Application layer protocol should be interfaced with the cross layer data Data collection/feedback is a key Application-aware Networking Network provisioning can be performed based on the application parameters Provisioning includes scheduling, path selection, routing, congestion control, power control, interference management Modules to be written which can be interfaced with the applications and appropriate commands can be generated to appropriate nodes/links/path 18

19 Orchestration and Management Fully automated and real-time orchestrator is required Should be capable of end-to-end orchestration Key Functionalities Multi-domain service orchestration: spanning across admin, operator and user (!!) domains Automated analytics and end-to-end FCAPS (fault, configuration, accounting, performance and security) support Heterogeneous network and application support Self-aware, self-healing, self-management. Possible architectures today 5GPPP Measure, Analyze, Policy and Execute (MAPE) ETSI NFV Management and Orchestration (MANO) MEF Lifecycle Service Orchestrator (LSO) TMF Zero-touch Orchestration, Operation and Management (ZOOM) 19

20 Cognitive Network Control Overall Architecture API Layer L1-L4 params for cross layer opt Respond Protocol parameters, desired state, iterated parameters SADR Framework Decide SDNization can be achieved through a cognitive control framework Applications Application Sensing KPIs & App params Application Sense Network & Protocols RSSI, Configuration, congestion, route, link state, port & flow parameters Current State: Features, synchronized params, QoE params Network, Protocol and Application state - Logs, Realtime params Analyze Machine Learning Classification Feature Extraction Model Tuning Configuration, Slicing, Virtualization, Power, Route, control SDN NFV Legacy Device & Network Layer Sensing of Application and Networks Sensing can be triggered or automatic 20

Cognitive Network Control Sensing & Analysis Sensing Sensing of device, network and application data Probe agents can be used along with the controller for sensing Type of sensing Active,

issues play a role in collecting the data; all the data may not be available 21 Analysis Derived Analysis Root cause analysis, understanding the fault Predictive & Proactive Analysis Expected problem

21 Cognitive Network Control Sensing & Analysis Sensing Sensing of device, network and application data Probe agents can be used along with the controller for sensing Type of sensing Active, Passive/Indirect Sensing Device & Services Browsing habit, location, CDR Edge & Core KPIs, flow-level, service-level, user-level DC & Cloud Application specific, Security and privacy Note: Regulatory issues play a role in collecting the data; all the data may not be available 21 Analysis Derived Analysis Root cause analysis, understanding the fault Predictive & Proactive Analysis Expected problem and main causes End-to-end big data analysis Event pattern and relationship Market analysis possible!!! Prescriptive Analysis Recommendation for specific action Estimation of future action plan Machine Learning Classification Feature Extraction Model Tuning

22 Cognitive Network Control - Decision Sensed & Analyzed data Model Classification Emulation Simulated Network Topology Input Training To Respond Algorithms/Models Model Validation Output Emulated Nodes Decision Rule based or Cognitive External Real Network Optimal vs. sub-optimal solution Use case analysis is required use of extended emulation module to recreate the scenario and to form the rule or models. These rules or models are to be trained further and appropriate emulation based testing is required for validation Any decision to be deployed (as a part of respond framework) has to be validated in the emulator Along with the observed data (sensed data), synthetic data generated by the emulation are to be used for modeling, training and decision making 22 Applications

23 Cognitive Network Control Our Solution Key Features Real-time sensing of network/application/protocols Cognitive network prediction & provisioning SDNization - SDNized APIs in the south bound APIs support for OpenStack and other Cloud orchestrators Novelties Middle layer based solution Auto sensing, analyzing, prediction & configuration Cognitive control of network and protocol Vendor and technology agnostic solution Heterogeneous network support Emulator based decision making 23

Cognitive Smart Engine Operation 5GPPP Monitor the Traffic (embb, mmtc, URLLC), Mobility, Radio + KPIs Analyze the Achieved/Projected performances, Real-time

24 Cognitive Smart Engine Operation 5GPPP Monitor the Traffic (embb, mmtc, URLLC), Mobility, Radio + KPIs Analyze the Achieved/Projected performances, Real-time learning Plan for new RRM Algos, D2D context, Spectrum usage, dynamic slicing Execute with virtual or physical N/W slicing & orch., SDN & NFV support, Mode selection 24

25 Cognitive Controller Solution Architecture Middle layer based solution Application Cloud based application SDN based network softwarization to realize network slicing Support for HetNets Rule based decision making process Learning modules network, usage, QoS, applications Applications & use cases Decision Respond Network Control Emulation Middle Layer Controller Analyze Sense Applications with all possible traffic type Applications with single traffic type mainly video application, smart city applications (smart meter!) Vertical industries Healthcare, factories, multimedia, automative, energy Network Control Agent Network Devices 25

26 What Can be Achieved? Multi-tenancy, 5GPPP 26

27 Multi-tenancy through Cognitive Control, 5GPPP 27

28 References 1. View on 5G Architecture (5G PPP White Paper), Ver. 2, Jul G-PPP, Living Document on 5G PPP use cases and performance evaluation models, Study on Architecture for Next Generation System Sept G Personal Mobile Internet Beyond What Cellular Did to Telephony, G Fettweis et al., IEEE Communication Magazine, Feb A Study on 5G V2X Deployment (5G PPP White Paper), Ver.1, Feb G Vision, 5G PPP, NGMN Alliance 5G White Paper, Feb G Service-Guaranteed Network Slicing White Paper, Feb G Services and Use Cases, 5G Americas White Paper, Nov Cognitive Network Management for 5G, Robert Mullins et al., Waterford Institute of Technology, Mar GPP TS : NG Application Protocol (NGAP) GPP TR : Study on Energy Efficiency Aspects of 3GPP Standards GPP TS : Architecture enhancements for control and user plane separation of EPC nodes GPP TS : System Architecture for the 5G Systems 15. 5G Network Architecture, A High-Level Perspective, Huawei, Draft Recommendation Y.IMT2020, reqts, Requirements of IMT 2020 network Disclaimer: Some of the figures used in this presentation are taken from most of the above references and these figures are used only for academic purpose and illustration of the concepts. 28

29 Thank you 29

5G Network Softwarization: Key issues and Gap Analysis. Yachen Wang

5G Network Softwarization: Key issues and Gap Analysis Yachen Wang 2015.9 Agenda Requirement, View and Key Issues Consideration of Key Issues Gap Analysis and Potential Work 5G Scenarios & Requirements