Applying Big Data Analytics with AI into SK Telecom s OSS (TANGO)

Transcription

1 Applying Big Data Analytics with AI into SK Telecom s OSS () Daniel H. Chae, Ph.D, Head of Network Analytics, OSS Tech Lab, Network R&D Centre, SK Telecom 2018 TM Forum 1

2 SK Telecom n No. 1 Telco in South Korea n No. 22 Global Telco in Forbes 2018 n >> 40% market share n >> 26 million subscribers (2017 4Q) World s 1 st LTE-A Pro ( CA) (2017) Wideband LTE-A( CA) (2014) Wideband LTE-A(20+10 CA) (2014) Wideband LTE-A(10+10 CA) (2013) LTE Advanced (2013) 5.76Mbps HSUPA (2007) WCDMA R4 (2003) CDMA x/EV-DO (2000) CDMA (1996) Korea s 1 st LTE-Advanced Pro (2016) LTE (2011) - World s 1 st fastest 1mn subs - World s 4 th largest LTE subs HSPA+ (2010) 2018 TM Forum 2

3 1 AI In Present OSS Unified Single Platform Introduction How SK Telecom is doing on OSS AI Strategy in OSS Use Case For Close Loop Control 2018 TM Forum 3

4 What we expect with AI AI : An intelligent machine as good as a human, but to the degree of far superior ML/DL Enabler General Expectation Automation Performance Improvement Discovery Hidden/New Thing Contents Creation Zero Touch Operation Reducing Expenditure Better Experience New Opportunities Network AI All Telco/IT vendors proclaim Network AI in 5G era TM Forum 4

5 Narrow AI One narrow AI can do very small job. Source: Kim Larsen, AI for Telco s, 5G World Summit 2018 AI Use Cases Source: CB insights TM Forum 5

6 Applying Narrow AI in Telco Operation AI can be applied into many Telco applications, but need to consider ROI x etom : Enhanced Telecom Operation Map (by TM Forum) TM Forum etom v15 and above picture from TM Forum 6

7 1 AI In Present OSS Unified Single Platform Introduction How SK Telecom is doing on OSS AI Strategy in OSS Use Case For Close Loop Control 2018 TM Forum 7

8 Why? Too many silo OSSs before 2018 TM Forum 8

9 (Telco Advanced Next Generation Oss) Executive Officer Network Operator Field Engineer Biz. Partner Module in PF UI/UX O Operation Network(Access-Transport-Core, IOT, NFV) monitoring for operation Application Layer PF PF Engineering Construction Operation Access Transport Core/ICT N/W Data Repository EC Unified Planning and Investment Strategy D Unified DB NW DB Conf. DB NW Statistics Virtualization Zone E2E Inventory Work & TT Management I Big Data Zone O Unified Network Monitoring Analytics Platform Operation Info A I Inventory EC Engineering& Construction A Analytics D Data Warehouse Physical/Logical Network Resource, E2E Topology Management Network(Access-Transport-Core) Planning and Investment Network Big Data E2E Analytics for customer experience management Network big data repository and data discovery Access Network Transport Network Core Network NFV 5G PF Platform Platform for unified OSS 2018 TM Forum 9

10 Roadmap 2018 TM Forum 10

11 1 AI In Present OSS Unified Single Platform Introduction How SK Telecom is doing on OSS AI Strategy in OSS Use Case For Close Loop Control 2018 TM Forum 11

12 Use cases in OSS Network Job Domain Access Transport Core Engineering (Planning) Site-Deploy Optimization Coverage Hole Risk Analysis Cell Radius Analysis (TA Analysis) Wired-line Optimization Site Footprint Optimization IP-Backbone Optimization IDC Power Optimization Traffic Prediction 5G Site Location Indoor Optimization Radio Analysis PRB Usage Prediction Backhaul/Backbone Optimization Updating Equipped node Construction (Deployment) A/T/C End-2-End A/T/C End-2-End Work-flow Analysis & Optimization Cost Analysis & Optimization Operation Train/Subway Site Service Quality Power Analysis & Saving Optimization Operation Chat-bot Dynamic Load Balancing ANR PCI Prediction of Fault & Performance VoC Analysis A/T/C Alarm + CEI Associated RCA T/C Anomaly Detection Service-Path / IP-flow Analysis Customer Experience Management VoC Analysis Traffic Congestion Mitigation PKG Update Analysis Hand-over Timer Optimization Service-stop RCA Management Optimization Customer Exp. CLC Hundreds of use cases in OSS domain 2018 TM Forum 12

13 Strategy of applying AI Gain from AI y Need to consider economics of AI by reducing cost. Acceptable AI Use Cases y=x Should reduce AI cost! Benefit Loss Loss Easy access to big data (with data lake) Sharing computation resource Mitigating computation complexity Efficiency in AI algorithm adoption Quick use case deployment COST for AI (Cost for HW/SW due to data feeding and heavy computation) x 1 Management Use Case 2 Optimization Use Case 3 Customer Exp. Use Case 4 CLC Use Case 2018 TM Forum 13

14 Discovery Process for embracing AI Operational Analytics Commercial System Operational Analytics Data Labeling /Tagging Analyzed Data Discovery Analytics AI-Analytics Use Case Factory Network Big Data Lake (Subscriber/node level) Step 1! Step 2 Define Use Case Preparing Train Data Network Monitoring & Analytics for Assurance / Optimization Collecting Network Raw-Data Combining Network Data with Workforce Management, Alarm Data, Event Data, Operator Tagging (Test-Bed Data) Expansion Staging System Verification Trial Step 5 AI(ML/DL) Library Analytics IDE (Easy Programming) Discovery GPU Staging Deployment Step 3 Step 4 Algorithm /Framework Selection Discovery Hardware (incl. SW) Step 5 Pursuing Goal Pursuing Goal System Operation Use Case Data Insight/Discovery Use Case 2018 TM Forum 14

15 for Central AI Analyze Action Required running-time AU#1: Analytics Use Case Analytics Result MCU#1: Manual Control Use Case Control Result MCU#L: Manual Control Use Case AU#2: Analytics Use Case Required resource Manual Control Use Case Container Migration AU#3: Analytics Use Case AU#3: Analytics Use Case Resource Managing ACU#1: Automatic Control Use Case ACU#2: Automatic Control Use Case Audit For Autonomous Network AU#M: Analytics Use Case Analytics Result ACU#3: Automatic Control Use Case ACU#N: Automatic Control Use Case Control Result Analytics(assurance/fulfillment/operation) Use Case Container Whole Network Data Automatic Control Use Case Container OSS as a Central AI platform Distributed AI Network Network vendor s specific AI algorithm is embedded in the node. Central AI Closed Loop Control High/Full Automation (Rare-touch) 2018 TM Forum 15

16 Use Cases with AI Functions User Interface Data I/O Analyze Analytics Use Cases (Applications) Purpose AI Algorithm Candidates Algorithm type Intelligent Assistant Efficiency for network operator Natural Language Processing DL Estimating input/output data amount of the system Operation efficiency Regression ML Prediction on Time-series data Operation efficiency, Business insight Regression, Seq2Seq Statistics, ML, DL Anomaly Detection (Sick Cell, Silent Cell Detection) Fault Detection and Root Cause Classification of node/traffic characteristics Operation efficiency/automation, best service Operation efficiency/automation, best service Operation efficiency/automation, Business insight Kernel Density Estimation, Seq2Seq, Pattern Matching, Autoencoder, AnoGAN Decision Tree, CART, Random Forest, Ensemble Learning SVM, k-means, k-nn, Random Forest, t- SNE, Neural Network Customer Complaint Analysis Business insight Decision Tree, CART, Neural Network ML, DL Cell Planning Network optimization, best service K-means, Regression ML, DL ML ML, DL Rule, ML, Statistics IP Flow Analysis Operation/management efficiency Regression Statistics, ML Unstructured data analysis Operation efficiency LDA(Latent Dirichlet Allocation) Statistics, ML Action Wired Network Design / Operation Load Balancing / Traffic Optimization Network optimization Network optimization, best service Geo-Clustering, Decision Tree (Network Flow Optimization) Rule, Classification, Deep Reinforcement Learning PCI / Neighbor optimization Network optimization, best service Gradient Descent Rule, ML Rule, ML, DL Mathematics widely used in ML Interference Avoidance Network optimization, best service Rule, Classification Rule, ML Healing(Reset) Operation efficiency/automation, best service Rule, Pattern Matching Rule, ML 2018 TM Forum 16

17 1 AI In Present OSS Unified Single Platform Introduction How SK Telecom is doing on OSS AI Strategy in OSS Use Case For Close Loop Control 2018 TM Forum 17

18 Analytics example of for Central AI OSS as a Central AI platform Analyze Action AU#1: Analytics Use Case Analytics Result MCU#1: Manual Control Use Case Control Result MCU#L: Manual Control Use Case AU#2: Prediction & Anomaly Detection Manual Control Use Case Container Migration AU#3: Analytics Use Case AU#3: Analytics Use Case ACU#1: Automatic Control Use Case ACU#2: Traffic Optimization Audit For Autonomous Network AU#M: Analytics Use Case Analytics Result ACU#3: Automatic Control Use Case ACU#N: Automatic Control Use Case Control Result Analytics(assurance/fulfillment/operation) Use Case Container Automatic Control Use Case Container Whole Network Data Central AI Closed Loop Control Distributed AI Network Network vendor s specific AI algorithm is embedded in the node. Better Experience 2018 TM Forum 18

19 Analyze: Prediction and Anomaly Detection node#n m-th KPI node#n (m+1)-th KPI node#n (m+2)-th KPI node#n M-th KPI 2018 TM Forum 19

20 Analyze: Prediction and Anomaly Detection KPI Trend Pattern Abnormal Pattern Anomaly : Unpredictable pattern within the reference data In general, KPI trends are very consistent with the past data. However, quite different patterns of KPIs happen frequently. Anomaly : Uncommon pattern within the reference data Anomaly Detection is designed for accurate sensing of abnormal pattern automatically TM Forum 20

21 Analyze: Prediction and Anomaly Detection Statistics Degree of Freedom in Algorithm Selection Machine Learning Seq2Seq Deep Learning AnoGAN more algorithms Data & Preprocessing Pre-processing and resampling of the data is performed considering each algorithm Anomaly Detection Modeling Value Difference - Difference of the reference data and present data - Reference : Last week - Time : moving 30 min, sliding 5 min - Algorithm : Histogram Comparison Mean, PDF Differenc e Density Prediction - Difference from the prediction with past data. - Reference : 1 Month - Time : moving 30 min, sliding 5 min - Algorithm : EM Expected Maximization Pattern Prediction - Detecting unpredictable patterns - Reference : 1.5 H - Time : every 5 min - Algorithm : Seq2Seq, LSTM Pattern Representation - Searching represented patterns in the reference - Reference : 2 H - Time : every 2 min - Algorithm : AnoGAN, DCGAN Cons / Pros More Sensitivity Less Computing Resources Uncommon Pattern Less Sensitivity Pattern Prediction Rack of seasonality Time Independency Huge Computing Resources 2018 TM Forum 21

22 Action: Green Traffic Optimization Load Balancing and Power Saving 1 Automatic Load Balancing Colocation Group (per enb + freq Band) 2 Switch Off Low Load (Power Saving) When detected anomaly in resource allocation (usually asymmetric inter-frequency PRB usage) Triggering to make cells even in PRB usage Switch On 2 Automatic Switch Off Load Balancing 3 When detected idle state of a cell 1 Power saving should not affect customer experience. 3 Congestion Detection and Automatic Switch On When detected congestion of active cell 2018 TM Forum 22

23 Action: Green Traffic Optimization Automation Better Experience Detection and Action 2018 TM Forum 23

24 For Network Automation NETWORK AUTOMATION is coming from smart use cases with ANALYTICS and AI TM Forum 24

25 THANK YOU! FOR NO QUESTION, 2018 TM Forum 25