Cisco DNA Center Assurance and Analytics

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2 BRKNMS-2542 Cisco DNA Center Assurance and Analytics Reducing Time to resolution using Big Data and Machine Learning Ramit Kanda, Director PM, Cisco DNA Assurance Shyam Srinivasan, Product Manager, Cisco DNA Assurance

3 Agenda Intent Based Networking and Context Big Data and Relevance to IT Operations Introduction to Cisco DNA Center Assurance Solution Capabilities and Benefits Solution Demos Guest Speaker Key Take Aways BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 3

4 Cisco Webex Teams Questions? Use Cisco Webex Teams (formerly Cisco Spark) to chat with the speaker after the session How Find this session in the Cisco Events Mobile App Click Join the Discussion Install Webex Teams or go directly to the team space Enter messages/questions in the team space cs.co/ciscolivebot#brknms-2542 BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 4

5 Enterprise Trends Driving Digital Transformation 66% Mobility 26B IoT 93% Cloud 100 Security BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 5 * Source: IDC, Worldwide Network Consulting Services Study, 2017, N=502

6 The IT Reality The pressure is on IT IT as a Strategic Business Partner Business Experience Respond fast to new digital initiatives Complexity at Scale Increasing user demands and increased threats Inefficient Workflows Siloed IT operations and workflows

7 Traditional IT may not be able to meet the demand of going digital Level 3 blames huge network outage on human error Telstra blames major network outage on human error Amazon s massive AWS outage was caused by human error BRKNMS-2542

8 The Network. Intuitive. Constantly learning, adapting and protecting. Cisco DNA Center Policy Automation Analytics L E A R N I N G Informed by Context Visibility into correlated and contextual data Who, What, When, Where, How Powered by Intent Translate Business Intent to Network Policy I N T E N T Automate the management and provisioning millions of devices instantly S E C U R I T Y C O N T E X T Intent-based network infrastructure Switching Routers Wireless BRKNMS-2542

9 DNA Center: Enabling the Self-Driving Network

10 Big Data and Relevance to IT Operations

11 Network Quality is a Complex, End-to-End Problem Affects Join/Roam Affects Quality/Throughput Client firmware WAN Uplink usage End-User services Affects Both* Client density AP coverage Configuration WLC Capacity WAN QoS, Routing,... Authentication RF Noise/Interf. Addressing CUCM ISE There are 100+ points of failure Mobile between clients user and app APs Office site Local WLCs What is the problem? WAN Where is the problem? Network services DC How can I fix the problem fast? DHCP Cisco Prime * Both = Join/roam and quality/throughput BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 11

12 IT Challenge: 43% of IT Time spent in Troubleshooting 4x Replication challenge Slow resolution Network operators spend more time collecting data than analyzing while troubleshooting Impossible for IT to troubleshoot if they cannot replicate the issue or see it real time Half of WiFi issues take more than 30min to resolve. 1 McKinsey Study of Network Operations for Cisco 2016 BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 12

13 Network Operations View Point Pain points Too much data Too many alarms Wireless issues are difficult Tool are too complex Can't monitor the way I want Knowledge base Reporting, analytics too difficult Can't change at convenient times Root cause analysis takes too long Needs Actions (manual, automated) Real-time performance Predictive insights, analytics Historical performance Alerts, notifications Geography map Network health scores Crowd sourcing agents Mobile apps 0% 20% 40% 60% Most Desired Sample Size: 30 Interviews BRKNMS-2542 Sample Size: 251 Surveys 2019 Cisco and/or its affiliates. All rights reserved. Cisco Public 13

14 Network Monitoring is a Big Data Problem Big Data is the term for a collection of data sets so large and complex that it becomes difficult to process using traditional data processing tools Multifaceted Sources of Telemetry Structured vs Unstructured Real-Time (Streaming) Vs Batch Processing BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 14

15 Transforming the Network with Big Data Analytics Volume Velocity Data size Terra Bytes per day being analyzed and correlated Variety Data forms Structured, unstructured Telemetry & Contextual Switch, router, AP, IoT sensor, firewall, load balancer, DHCP, DNS Data speed Streaming Low-latency push/pull Veracity Data Accuracy Data from nearest source Contextual information Time reported Action Insight Information Data Extract meaningful insights from data 4Vs of big data Analytics Create value at the right time 5 th V: Value BRKNMS-2542

16 Type of Analytics Prescriptive Predictive Diagnostic Why it Happened? What will Happen in Future? What can be done to address it? Complexity Descriptive What is Happening Now? Value BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 16

17 Introduction to Cisco DNA Center Assurance

18 Cisco DNA Center Assurance Visibility Learn from the network and clients attached to it Automate Recognize changes and inform the self-driving network Proactive Insights See problems before your end users do Predictive Performance Understand how new services will impact service levels Real-Time Troubleshooting Find root cause faster with granular details Full Stack AI Powered Network Analytics Platform

19 Cisco DNA Assurance From network data to business insights Network telemetry contextual data Complex event processing Correlated insights Suggested remediation Traceroute Syslog NetFlow AAA Router DHCP Telnet Sensors CLI OID IPSLA DNS Ping SNMP IPAM MIB Apple ios AppD CMX Complex correlation Steam Processing Metadata extraction Clients Application Baseline Network Everything as a sensor Over 150 actionable insights Clients Applications Wireless Switching Routing BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 19

20 Expansive Telemetry Collection Infrastructure Network Application Data Type: Inventory, Configuration, Capabilities Data Source: APIC- EM Mechanism: Built- in Connector Data Type: Topology Data Source: APIC- EM Mechanism: Built- in Connector Data Type: Flow (Applications Quantitative, DNS ) Data Source: Network Device Mechanism: Push Data Type: Logs, Traps Data Source: Network Device Mechanism: Push Data Type: Users, User Group Data Source: ISE, WLC Mechanism: Pull (API) Data Type: Qualitative Metrics (ART, Perfmon ) Data Source: Router Mechanism: Pull Data Type: Wireless Signaling, Events, Roaming data Data Source: WLC/AP Mechanism: Streaming Data Type: Location Data Source: APIC- EM, CMX Mechanism: Built- in Connector, Pull Data Type: Call Stats (MOS, Loss, Latency, Jitter) Data Source: S4B Mechanism: Push Data Type: End Point Stats Data Source: Apple ios Devices Mechanism: Push Data Type: Flow Path Trace Data Source: APIC- EM Mechanism: Pull Data Type: Synthetic Traffic Data Source: Sensors (APs, HW Sensors) Mechanism: Push Data Type: SNMP Data Source: Network Device Mechanism: Pull Data Type: Policy Data Source: ISE Mechanism: Subscription through PxGrid Data Type: Qualitative Metrics Data Source: vnam Mechanism: Push Data Type: IPAM, Namespaces Data Source: Infoblox, DHCP Mechanism: Pull (API) Data Type: Performance, Connectivity, Service Stats Data Type: Qualitative Metrics Data Source: Network Device Data Source: AppD (IPSLA), Sensors (APs, HW Sensors) Mechanism: Pull Mechanism: BRKNMS-2542 Pull Ramit Kanda 2019 Cisco and/or its affiliates. All rights reserved. Cisco Public 20

21 Streaming Telemetry Export enriched, consistent and concise data with context from network devices for a better user and operator experience Periodic or On-Change Structured Data Scalable Reduced CPU Load BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 21

22 Contextual Property Graph Src: Client SJC4-SW01 Metrics, Logs, Events Network Device App Dst: , 3600 SAP Building 4 2 nd Floor Location Topology Network Graph Userid: Gbaker Group: Employee Type: iphone Identity & Profiling DDI User: George Baker Src ip: Time: 10:32AM Netflow AVC DDI ISE/Radius Topology CMX, DNAC Device BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 22

23 Micro Services based Big Data Architecture Visualize and Act Applications Unified Monitoring IM, EUM, APM Unified Analytics Transaction, Log, User Transform Data Access Store Prepare and Analyze Services APIs & Streaming Data Stores (ELK, Influx, Mongo) Stream and Batch (Flink, Beam, Spark) Dashboard, Reporting, Alerting etc. Time Series, Events/Issues, Logs, Metadata, Flows etc. Event, Graph, Document Baseline, Anomaly, RCA, Replay, Predictive Ingest Collect SYSTEM FLOW Message Broker (Kafka) Collectors ARCHITECURAL BUILDING BLOCKS SNMP, Netflow, Logs, Policy, Streaming Telemetry, AAA, DHCP, DNS, Topology etc. Network Telemetry, Contextual Data TECHNOLOGY COMPONENTS BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 23

24 Solution Capabilities and Benefits

25 Key Capabilities Full Stack Analytics Health as the Lead Performance Indicator Comparative and Predictive Insights Temporal Graph for Time Travel Proactive, Reactive and Realtime Insights BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 25

26 Full Stack Visibility Clients Wireless Wired Services Applications Analyze and Correlate information across the stack for unmatched visibility Network Baselining* and Noise reduction to eliminate alert fatigue * DNAC 1.3 BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 26

27 Full Stack Visibility NETWORKS CLIENTS APPLICATIONS SERVICES DNS 602/ min 536/ min BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 27

28 Time Travel Client Health Score 10/10 Issues & Trends Issue detection RealTime Status Proactive Performance Monitoring Global Insights - 24 hours - 3 hours Now + 3 hours +24 hours Unique Temporal Graph Technology captures network state information 5.4B+ context aware search graph entries created every 24 hours Full contextual state stored for 14 days to allow time travel and recreate problem in data Lowers Mean Time to Resolution and Increases end user productivity and experience BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 28

29 Time Travel BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 29

30 Health as the Lead Indicator Health score is a smart amalgamation of KPIs associated with the performance of Client, Network device, and Applications. - Health scores are colored to reflect the alert level that warrants attention from the network operator. KPI Monitoring Health Monitoring Improved Productivity BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 30

31 Health Score Network, Client, Application Access Point Wireless Client Application Same level of visibility for Wired Client and Network Devices as well

32 Proactive Insights: Network Sensors Wireless: On-Demand or Schedule Perform Tests Wired: Automatic Service Monitoring! C? Access point Known networks B Unknown networks B E C B Fabric edge Fabric control Fabric/default border E E E Sensor Dashboard Global Issues On Boarding Tests Network Tests Application Tests 16+ Sensor Tests IPSLA based service level monitoring for higher productivity and lower Opex Periodic verification of connectivity to fabric control plane, border, edge nodes, and network services such as DHCP, DNS, IPAM, AAA servers Active Sensor AP1800S AP as a Sensor 1800/2800/3800/4800 Network Catalyst 3k/4k/6k/9k ASR 1k ISR4k & CSR AireOS and IOS Controllers BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 32

33 Proactive Insights Wireless Sensors Wired: IPSLA BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 33

34 Reactive Insights: Issues/Anomalies BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 34

35 Reactive & Real-Time Insights Intelligent Capture 63% Cross Stack Innovation AP HW, AireOS, DNAC Users assume the wireless network is the problem Dramatic simplification via single PCAP across multiple APs with zero packet loss during roaming Packet capture is a very difficult task over WiFi 72 hours to minutes Average Time to resolve user issues with Intelligent Capture Live Client Onboarding State Anomaly Triggered PCAPs On- Demand RF Scanning Real- time Client Location On- Demand Wi- Fi App Analytics BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 35

36 Real-Time Wireless client insights Apple and Wi-Fi analytics for ios Device Profile Client shares these details 1. Model: iphone 7 2. OS: ios Wi-Fi analytics Client shares these details 1. BSSID 2. RSSI 3. Channel number 2 Assurance ios client shares the reason (error code) for the disconnect. 3 This partnership with Apple enables any ios 11 client to speak to Cisco DNA Center with client diagnostics. Provides a more comprehensive view of all potential root causes of wireless issues. Support per-device-group policies and analytics Insights into the client s view of the network Clarity into the reliability of connectivity Problems affecting ios clients are likely affecting all wireless clients.

37 Why Machine Learning (ML)? Challenge: Every network has its own characteristics: Vary with time, environment and current state of the network. Very hard to characterize statistically or manually Machine Learning: Learns the characteristics of each network and the relationship of the network state with underlying variables: devices, users, apps, location, time etc. BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 37

38 Comparative and Predictive Insights Context Aware Baselining Separate Normal from Abnormal Reactive Anomaly Detection Surface and Root Cause Complex Issues Anonymized Data Model Training: 16K Radios, 1.2M Hrs Radio traffic, 17M Onboarding attempts seen weekly Proactive Insights Global Patterns and Systemic Issues Comparative Benchmarking Compare Performance to Peers Predictive Analytics Faults and Performance Degradation BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 38

39 Baseline ML Based Anomaly detection Model Baselining Deviation from expected behavior, where behavior is represented using a model that takes into account variety of parameters ( Context such as Network, Client counts, No of active entities...) Top Retailer Fortune 100 IT Shop Offline ML model applied to two independent networks Built using Machine Learning Network Context Aware Use Case Specific BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 39

40 Long On-boarding time for certain access points Avg onboarding Duration # Onboardings Avg. Onboarding Time Anomaly Detection Up Time: 1hr 26m 28sec BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 40

41 Throughput Deviation Insights Insight: Deviation of 1Mbps in the last 4 weeks from 1Mbps to 8Kbps BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 41

42 Peer Comparison Radio Throughput Comparative Analytics BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 42

43 Solution Demos

44 Multi-Tier Hierarchy of Contextual Dashboards Summary Global Health Dashboard Top Level Summary Analyze and Triage Troubleshoot Network Health Dashboard Infrastructure and Network Drill Down Client Health Dashboard End User Experience Monitoring Issues and Insight Dashboard Triaging and Remediation Application Health Dashboard Application Performance Monitoring Sensor Dashboard Proactive Monitoring Synthetic Tests 360 Views Clients/Device/Apps Drilldowns, Real Time Troubleshooting Integrate Data as a Service (API) Dashboard Logs, Metrics, Events, Flows, Historical BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 44

45 Demo Use Cases Full Stack Analytics Client Troubleshooting Device Troubleshooting Visibility: Dashboards providing overview across Network, Clients and Applications Client 360 View: Analytic view on Client Experience Global Issues: Visibility on top issues based on priority Usage and SLA: Comprehensive view on SLA Metrics Onboarding Event Viewer: Track 240+ states for all clients Issue Insight: Troubleshoot specific device issue Time Travel: Contextual Analysis going back up to 14 days Intelligent Capture: Realtime Troubleshooting Guided Remediation: Analyze the root-cause and take corrective action BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 45

46 Solution Customization and Integration

47 Customization Custom Threshold & Baselining Issue Lifecycle & Priority Data Retention

48 Event Notification Event Notification Notification Engine IT4IT Messaging DNA Assurance Issue Detection Issue Publishing DNA Center Platform User defined issues of interest Event trigger settings and notification policy Notification Message Customization API(s) REST Endpoint Engagement ITSM Application Pager (SMS) In-App (Mobile) BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 48

49 Incident Management (ITSM) Full Stack Analytics Time Travel & Graph Insights with Guided IT Process- Incident and Integrations Remediation Management Out of the box integration with Service now for incident management User defined Issues type and priority sent to ITSM for incident reporting Issues enriched with client and device information and suggested actions for remediation Flexible integration with approvals, change review windows / process Guided Remediation, Intuitive IT Service Management

50 Customer Voice San José State University Guest Speaker: Shai Silberman Network Services Director - IT

51 Customer Experience San Jose State University Empowering the Silicon Valley University Profile 33,000 students + 5,000 faculty staff San Jose Public Library Hospitality (4200 Dorm residents) ~400 classrooms Theater / Sports Arena 52 Buildings and 250 data closets Wireless Network 3000 Access Points (3702, 2800, 3800,1815) Wireless Controllers (5520, 3504 and C9800) Authentication: ISE Management: Cisco DNA-C /Prime Infrastructure 24K Concurrent Devices 47K different devices per week 300+ years of sessions airtime in a month Device Diversity, High Density #CLUS 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public

52 Customer Experience San Jose State University Empowering the Silicon Valley Challenges Seamless wireless experience needs to work like a light switch. Wireless is the network - needs to be available 24/7 in Dorms Elephant Memory and Risk to Brand from Social Media due to bad experience Digital Transformation The network based classroom content Proliferation of network devices (IoT) Everything/Everyone is on the wireless network Results Teachers are able to integrate online content into classroom with confidence Students have ubiquitous access to network anywhere on campus IT can proactively manage the network with 360 view of user experience IT Operations Impact of Cisco DNA-Center Visibility into the User Experience across all 7 layers Reduced Alert Fatigue and Troubleshooting time (360 views) DNA Center turned the wireless network maintenance into proactive process resulting in higher confidence Shai Silberman IT Director San Jose State University

53 Key Take Aways Single pane of glass for configuration, change management and assurance Full Stack Analytics for complete and unmatched visibility across wired and wireless Network Time Travel for contextual analysis of past problems and find root cause Actionable Insights with drill down granularity to dramatically reduce troubleshooting time and Opex Sensor based SLA Monitoring to proactively test end user experience and mitigate network inefficiencies Real-Time and Context driven telemetry for faster troubleshooting of client problems BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 54

54 Cisco Webex Teams Questions? Use Cisco Webex Teams (formerly Cisco Spark) to chat with the speaker after the session How Find this session in the Cisco Events Mobile App Click Join the Discussion Install Webex Teams or go directly to the team space Enter messages/questions in the team space cs.co/ciscolivebot#brknms-2542 BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 55

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56 Continue Your Education Demos in the Cisco Showcase Walk-in self-paced labs Meet the engineer 1:1 meetings Related sessions BRKNMS Cisco and/or its affiliates. All rights reserved. Cisco Public 57

57 Thank you

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