5G Network Slicing Management for Challenged Network Scenarios

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1 5G Network Slicing Management for Challenged Network Scenarios Industry keynote 12th Workshop on Challenged Networks, ACM CHANTS, October 20, 2017 Henning Sanneck E2E Mobile Network Solutions, Nokia Bell Labs Research, Munich, Germany with contributions from C. v. Hardenberg, C. Mannweiler, M. Naseer-ul-Islam, C. Sartori, C. Schmelz et al. 1 Public

2 Outline 5G Conclusions 5G Network Slicing 5G Network Slicing Management 5G RAN 5G RAN Management 2

3 5G: just yet another G? Prof. Carle, TUM Dr. Tsvetkov, TUM Prof. Stadler, KTH 3

4 5G Cellular Networks New user demands with extremely diverse requirements Devices 1.5 GB/day Smart Factories 1 PB/day Billions of sensors connected Autonomous driving 1ms latency 4

4k Video streaming Haptic VR 1Mbps 100kbps 10kbps Things Sensors Chatbots SD Video streaming Cloud-assisted driving Remote

5 Bandwidth 5G: Diversity of Use Cases - The 4 Key Business Value Dimensions 10Gbps Video 360 video (free viewpoint) Centralized RAN VR/AR 1Gbps 360 VR (hi-res) Autonomous vehicles 100Mbps Remote training Virtual RAN 10Mbps 360 video (lo-res) 4k Video streaming Haptic VR 1Mbps 100kbps 10kbps Things Sensors Chatbots SD Video streaming Cloud-assisted driving Remote control vehicles Electric grid control System Control 1kbps Home Sensors 5 10s 1s 100ms 10ms 1ms 100us 10us Core Cloud Latency Edge Cloud

6 5G challenged network scenario: Industrial Internet / Industry 4.0 Resilient, secure low-latency communication Manufacturing and process automation Resilient, secure low-latency comms Critical comms Wireline connections today >90% Low / deterministic latency <1ms; % reliability Inherent security by dedicated network slices Single company network for all kinds of industrial applications Intrusion detection AR-enhanced maintenance Public MNO slice Overall costs for greenfield 2-5 times lower Break even for wireline replacement 1 year # of sensors = Payback period Reconfiguration cycle = Payback period Business case Removing cost of cabling installation and maintenance Less reconfiguration time Less production capacity overprovisioning Benefits 6

7 Slicing

5G Network Slicing Optimized service delivery for heterogeneous use cases Multiple independent network instances on one physical network Slicing across radio, transport, core / edge and central

8 5G Network Slicing Optimized service delivery for heterogeneous use cases Multiple independent network instances on one physical network Slicing across radio, transport, core / edge and central clouds Cloud scalability and efficiency Autonomous driving Self service portal Industry Flexibility to meet diverse requirements Automotive Health monitoring Health Factory 8 Full automation / self-organization

9 Different use case requirements different slice characteristics Latency Challenge need LOW HIGH Mobile Broadband HIGH 9 SMF: Session Management Function UPF: User Plane Function

10 RAN Features Requirements 5G RAN: Requirements and Features Services UHD video VR Ultra broadband VR URLLC Drones V2X I4.0 Several 100 MHz spectrum High Frequency deployments Reduce latency to milliseconds Reliability New spectrum New spectrum Instant response At L1 or higher layers Front haul split, HARQ Flexibility, New Radio (NR) Carrier Aggregation, NR-LTE Dual Connectivity Support for Beamforming, Self Backhaul Shorter TTI, RRC-INACTIVE, Zero latency Handover, SRB/DRB duplication, HARQ Flexibility Multi-Cell Coordination: (CoMP), NR Multi-Connectivity, SRB/DRB duplication 10 SRB/DRB: Signaling / Data Radio Bearer

5G RAN structure macro = potential site for data center / aggregation small cells RAN / Core network functions New radio small cells RRHs tree edge cloud New

macro small cells star x100.000 small cells chain macro macro ring macro x10.000 macro sites edge cloud pre-aggregation x1.

11 5G RAN structure macro = potential site for data center / aggregation small cells RAN / Core network functions New radio small cells RRHs tree edge cloud New radio MuLTEfire 4G WiFi Heterogenous environment: -Multi-RAT, multi-layer -Small cell cloud RAN -Physical and virtualized Network Functions -Multi-vendor 11 macro small cells star x small cells chain macro macro ring macro x macro sites edge cloud pre-aggregation x1.000 pre-aggregation sites Core network functions Distance and latency to radio access increases Very flexible, but also very complex RAN structure x100 aggregation sites central gateways x10 central gateways

12 5G RAN Management Addressing the challenges Ultra Dense Small Cells Cloudified RAN & Core Multiconnectivity (MC) URLLC network service Multi-service Network CP CP Distribution Flexible VNF relocation / reconfiguration Centralization Multi-RAT / layer Low latency radio, edge cloud Slices Distributed decisions; Management hierarchy / aggregation Combined management of physical / virtualized infrastructure MC-aware, management incl. aggregation of PM/FM data NM data resolution; prognostic diagnosis; combined network resilience & selfhealing Intra- / inter-slice management; Separation (& sharing) of knowledge; Embedded analytics 12 Hybrid NM

Unlimited Risk: complexity of the network infrastructure (dense small cells, mixed physical /

Ubiquitous high Characteristic: Scale (# users, # applications) Manifestation: network usage

13 5G RAN Management: Opportunity vs. Risk Opportunity: ubiquitous, unlimited connectivity for a wide range of services Capacity Unlimited Risk: complexity of the network infrastructure (dense small cells, mixed physical / virtualized infrastructure) Cost prohibitive $ $ $ Coverage viable $ Network complexity Ubiquitous high Characteristic: Scale (# users, # applications) Manifestation: network usage data Characteristic: Scale (# cells, # (V)NFs) Manifestation: network operation data Cognition: drive opportunity, limit risk by mastering data mastering complexity 13

Cognition & Self- Organization Cellular macro network Tightly planned, infrequent physical topology changes, automated operation Single operator Single vendor equipment per OAM domain 5G Cellular

networks? Self-organization is a process where the organization (constraint, redundancy) of a system spontaneously increases, i.e., without this increase being controlled by the environment or an encompassing or otherwise external system.

14 Cognition & Self- Organization Cellular macro network Tightly planned, infrequent physical topology changes, automated operation Single operator Single vendor equipment per OAM domain 5G Cellular Heterogeneous Network Some parts only coarsely planned, frequent virtual topology changes, highly automated operation Multi-tenant (shared infra) Multi-vendor per domain 14 applied to infrastructure networks? Self-organization is a process where the organization (constraint, redundancy) of a system spontaneously increases, i.e., without this increase being controlled by the environment or an encompassing or otherwise external system. (F. Heylighen, Principia Cybernetica Web, 1997) Ad-hoc / mesh network Uncoordinated deployment, frequent physical topology changes, autonomous operation Only node operator Open environment, standardized protocols between nodes

5G RAN Management verticals IoT support D2D

latency Multi-hop Low cost Ultra-dense small

Location information Low power consumption

15 5G RAN Management verticals IoT support D2D Selfbackhauling Virtualized Network Functions Cognitive Network Management System (multi-vendor, multi-tenant) Optimization Low latency Multi-hop Low cost Ultra-dense small cells New radio New radio MuLTEfire 4G WiFi small cells macro Troubleshooting / Healing Configuration Analytics Policy High reliability Location information Low power consumption (Trained) telco-centric knowledge models & context network data 15 (challenged) network scenarios

16 5G Network (Slicing) Management Communication Service Mgmt. (embb, mmtc, cmtc) Analytics Engine Knowledge Sharing / Isolation Network Slice Mgmt. Preparation, LCM / (re-)configuration Anomaly Detection Diagnosis Healing action Scaling Policy Engine Objectives / Intent Verification Load balancing / traffic steering Coverage and Capacity Optimization Scaling in/out, down/up Management Policies Machine Learning Mobility robustness (MRO) Anomaly Detection Diagnosis Healing Anomaly Detection Diagnosis Healing Coordination Neighbour relationship setup (ANR) (Big) data acquisition and distribution Resource ID allocation (beam/cell ID/RS) OAM connectivity / interface setup workflow Network Function Chaining LCM / (re-)configuration, (re-)placement Cognitive NM functions MDT / location Radio resources (beams, cells): PNFs Cloud resources 16 Licensed spectrum Unlicensed 3GPP VNFs Apps

Communication Service Management has not been covered by 3GPP standards in the past TMForum and ITIL provide

17 5G Network Slicing Management: system architecture Inter- Slice Mgmt. Communication Service Management OAM domain Network Slice Management PNF NM EM NFVO VNFM VNF-App VNF-Plat NFV domain Communication Service Management has not been covered by 3GPP standards in the past TMForum and ITIL provide (high-level) industry specifications Network Slice management is umbrella functionality Across radio / core / transport Across infrastructure providers VNFs and PNFs Across vendors 17

18 Depth of control and entry levels of 3rd party MNO Option 1 Web Service 3 rd party (e.g., vertical) Communication Service Mgmt. BSS Network Slice Management Network Slice Mgmt. BSS Option 2 Network Management Network Orchestration Element Element Manager Manager VNF VNF Manager Network Slice 1 Network Slice 2 18

19 Depth of control and entry levels of 3rd party 3 rd party (e.g., vertical) MNO Communication Service Mgmt. Web Service BSS Network Slice Management Network Slice Mgmt. BSS Network Management Network Orchestration NM Network Orchestration Element Element Manager Manager VNF VNF Manager Element Manager VNF Manager Network Slice 1 Option 3 Network Slice 2 19

20 Network Slice Lifecycle 3GPP Lifecycle of a Network Slice Instance (NSI) Design Preparation Preprovision Network environment preparation Instantiation, Configuration and Activation Instantiation/ Configuration Activation Supervision Reporting Run-time Modificatio n Decommissioning De-activation Termination NSI Automated Configuration NSI Automated Optimization / Healing NSI Automated Re-Configuration Feedback for template optimization 20

Run-time optimization, reconfiguration Example of NF Overload detection CSMF Trigger SLA modification negotiation CSMF Beyond SLA limits for the slice NSI NSI Within SLA limits review subnet

21 Run-time optimization, reconfiguration Example of NF Overload detection CSMF Trigger SLA modification negotiation CSMF Beyond SLA limits for the slice NSI NSI Within SLA limits review subnet provisioning Modify subnet policy NSMF Beyond current subnet provisioning for the slice NSSI NSSI NSSI NF Overload (e.g. increasing connected users within allowed limits for the slice Scaling or New Instance Instantiation NSSMF Each layer monitors and checks possible actions within its scope according to SLA. Otherwise escalate to next higher level 21

22 5G Cognitive NM function design Impacts of Multi-Tenancy and Multi-Service Cell-Specific Function Instance Differentiate input (performance metrics) for different slices Coordination among requirements of different slices Cognitive NM function policy update when slice is (de)activated Slice-Specific Function Instance Multiple instances per cell Coordination between different function instances of same type operating on the same cell Parameters adjustable per UE Parameters adjustable only at cell level Coordinator Performance Metrics CF1 Configuration Performance Metrics CF1-c CF1-b CF1-a Configuration Network Slices Operating within a Cell Network Slices Operating within a Cell 22 CF: Cognitive Function

5G Cognitive NM functions: example CCO / verification Observed effect in real network data: Cell B up-tilt 2 Cell A shows increased values of KPI BLER in the HSUPA MAC layer

with hierarchical network intelligence : CCO (tilt optimization) function plus verification function with wider network view Cell C up-tilted (CCO) Shared RET Cell B up-tilted

23 5G Cognitive NM functions: example CCO / verification Observed effect in real network data: Cell B up-tilt 2 Cell A shows increased values of KPI BLER in the HSUPA MAC layer Cause: Coverage and Capacity Optimization (CCO) algorithm triggered Cell C up-tilt Cell B forced to up-tilt due to being on the same RET module as Cell C Example case solved with hierarchical network intelligence : CCO (tilt optimization) function plus verification function with wider network view Cell C up-tilted (CCO) Shared RET Cell B up-tilted (forced) Tilt changes applied; Start of assessment process. Hidden performance effect Thur. Fri. Sat Sun Cell A -Increased HSUPA BLER -Neighbor of Cell B -Not neighbor of Cell C 23

24 5G Network Slicing Management for Challenged Network Scenarios Conclusions 5G addresses some challenged network scenarios (factory, UAV, disaster response, V2X) 5G Network characteristics (ultra dense, cloudified, multi-service / -tenant) impose new RAN operability challenges Functional: per service- / tenant- instrumentation and dynamic operation (multiplicity of virtual network configurations) data: higher resolution of measurements; new external sources / context higher degree of autonomy in management Architectural: new building blocks slicing management, analytics & policy engine higher degree of distribution, cooperation / coordination and abstraction Cognitive NM functions to master network data mastering network complexity 24

25 5G Network Slicing Management for Challenged Network Scenarios Conclusions 5G Network Slicing Management Different levels of control by tenant required (high-level vertical vs. MVNO) Slice run-time optimization / healing slice-aware Cognitive Functions managing Physical and Virtual NFs Coordination across different slice s requirements Enable the management of diverse service types in diverse network scenarios Research challenges Dynamic slice instantiation and management (for unpredicted events) Slice management knowledge sharing & isolation Cognitive Function placement 5G URLLC management: instrumentation, prognostic diagnosis 25

26 Nokia & Challenged Networks today Nokia Saving Lives An Innovation and Non-Profit Initiative

Nokia Saving Lives https://networks.nokia.

Center HD/IR/Thermal Cameras Loudspeaker / Microphone Gas Sensor

International Crisis Management Organization (Mission Training,

LTE LTE RF BTS PaCo Portable LTE Ultra Compact Network (on pole,

Connectivity / PaCo Flight management Rescue Apps Data Storage

27 Nokia Saving Lives UAV swarm Portable Control Center HD/IR/Thermal Cameras Loudspeaker / Microphone Gas Sensor Delivery System LTE data and control connection Local NGO / International Crisis Management Organization (Mission Training, Storage, deployment) Local Operator (Frequency License) Handsets LTE LTE RF BTS PaCo Portable LTE Ultra Compact Network (on pole, drone, balloon) LTE Nokia Operational Team/ Deployment Connectivity / PaCo Flight management Rescue Apps Data Storage Video streaming 3D map creation Object detection/people count Flight management automation 27 Nokia 2017

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Addressing 5G Network Management Challenges with Machine Learning

Addressing 5G Network Management Challenges with Machine Learning 23. ITG Fachtagung Mobilkommunikation, 17.5.2018 Henning Sanneck, Christian Mannweiler, Janne Ali-Tolppa, Levente Bodrog, Szilard Kocsis,