Cisco Connect Toronto 2018 DNA assurance

1. Tim Szigeti Principal Engineer—Technical Marketing October 18, 2018 The Shortest Path to Network Innocence DNA Assurance

2. Agenda © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public • Business Requirements • Context • Learning • User Requirements • Technology Requirements • DNA Assurance • Client Assurance • Network Assurance • Application Assurance • Machine Learning • Summary 2

3. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Network Quality is a Complex, End-to-End Problem APs Local WLCs Network services DCOffice site ISE Mobile clients CUCM Client firmware AP coverage WAN Uplink usage End-User services RF Noise/Interf. Cisco Prime™ Configuration AuthenticationWLC Capacity WAN Client density DHCP Addressing WAN QoS, Routing, ... There are 100+ points of failure between user and app What is the problem? Where is the problem? How can I fix the problem fast? 3

4. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public . 1 McKinsey Study of NetworkOperationsfor Cisco – 2016 IT Challenge: 43% of IT Time spent in Troubleshooting 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 Most network quality issues take hours to either resolve or to prove the network innocent 4x Replication challenge Slow resolution 4

5. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Business Objective of Analytics and Assurance https://www.cisco.com/c/en/us/solutions/enterprise-networks/dna-analytics-assurance.html 5

6. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential • Business Requirements • Intent • Context • Learning • User Requirements • Technology Requirements • DNA Assurance • Client Assurance • Network Assurance • Application Assurance • Machine Learning • Summary Agenda S E C U R I T Y CONTEXT L E A R N ING I N T E NT

7. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential The Challenge of Context: Transforming Data into Actionable Insights Data Insight Information Action Create value at the right time Extract meaningful insights from data Volume Data size • TB per day • Streaming telemetry, NetFlow, Syslog, SNMP, logs Velocity Data speed • Firehose • Streaming, low-latency push/pull Variety Data forms • Structured, unstructured • Switch, router, AP, IoT sensor, firewall, load balancer, DHCP, DNS Veracity Data trustworthiness • Quality, validity • Internal, partner, public Analytics 7

8. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Transforming Data into Actionable Insights Example Data Insight Information Action WebExhas 63 ms of jitter WebExApplication Health Score is 70% WebExis not being marked on the WLAN Enable Fastlane for WebEx 8

11. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential DDI NetFlow AVC Topology Location Device Analytics Engine User: George BakerGroup: Marketing Contextual Correlation Example ISEISE M AC: B8:8D:12:36:15:22 M AC: 60:F4:45:78:96:9F

12. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential DDI NetFlow AVC Topology Location Device Analytics Engine User: George BakerGroup: Marketing Contextual Correlation Example ISEISE DDI Source IP: 1.1.1.2 M AC: B8:8D:12:36:15:22 M AC: 60:F4:45:78:96:9F

13. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential DDI NetFlow AVC Topology Location Device Analytics Engine User: George BakerGroup: Marketing Contextual Correlation Example ISEISE DDI D est IP: 2.2.2.2 D est Port: 80 ? D est IP: 3.2.2.2 D est Port: 80 ? NetFlow Source IP: 1.1.1.2 M AC: B8:8D:12:36:15:22 M AC: 60:F4:45:78:96:9F

14. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential DDI Netflow AVC Topology Location Device Analytics Engine User: George BakerGroup: Marketing Contextual Correlation Example ISEISE DDI D est IP: 2.2.2.2 D est Port: 80 NetFlow D est IP: 3.2.2.2 D est Port: 80 AVC ? ?Source IP: 1.1.1.2 M AC: B8:8D:12:36:15:22 M AC: 60:F4:45:78:96:9F

15. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential TopologyTopology Location Device Analytics Engine D est IP: 2.2.2.2 D est Port: 80 D est IP: 3.2.2.2 D est Port: 80 User: George BakerGroup: Marketing Topology Contextual Correlation Example DDI Netflow AVC Location Device ISEISE DDI NetFlow AVC Source IP: 1.1.1.2 M AC: B8:8D:12:36:15:22 M AC: 60:F4:45:78:96:9F

16. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential M AC: 60:F4:45:78:96:9FLocation Device Analytics Engine D est IP: 2.2.2.2 D est Port: 80 D est IP: 3.2.2.2 D est Port: 80 User: George BakerGroup: Marketing Location Building 24 1st Floor Contextual Correlation Example TopologyTopologyTopology DDI Netflow AVC ISEISE DDI NetFlow AVC Source IP: 1.1.1.2 M AC: B8:8D:12:36:15:22

17. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential M AC: 60:F4:45:78:96:9F Device Analytics Engine D est IP: 2.2.2.2 D est Port: 80 D est IP: 3.2.2.2 D est Port: 80 User: George BakerGroup: Marketing Building 24 1st Floor Device No Layer 2 QoS marking for Webex Contextual Correlation Example LocationLocation TopologyTopologyTopology DDI Netflow AVC ISEISE DDI NetFlow AVC Source IP: 1.1.1.2 M AC: B8:8D:12:36:15:22

18. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential© 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential • Business Requirements • Context • Learning • User Requirements • Technology Requirements • DNA Assurance • Client Assurance • Network Assurance • Application Assurance • Summary Agenda S E C U R I T Y CONTEXT L E A R N ING I N T E NT

19. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential What is Machine Learning? • Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed to do so • The process of learning begins with observations of data, and looking for patternswithin the data so as to make increasingly better correlations, inferences and predictions • The primary aim is to allow these systems to learn automatically without human intervention or assistance and adjust actions accordingly

20. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Reasons for Data Patterns • Coincidence • Correlation • Causation Statistics 101: Correlationdoes not necessary mean Causation

21. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Machine Learning Algorithms build their models using hundreds of inputs APs WAN Local WLCs Network Services DCOffice Site ISE DHCP Mobile Clients CUCM RF & EDCA behavioral metrics,.. Queuing, Dropping, WRED behavioral metrics… Device type, OS release, behavioral metrics, ... WAN & core network metrics .. Application metrics, user feedback, failure rate, ... ... and more

22. Agenda © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public • Business Requirements • Intent • Context • Learning • User Requirements • Technology Requirements • DNA Assurance • Client Assurance • Network Assurance • Application Assurance • Machine Learning • Summary 22

23. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Design Thinking (DT) Case Study: GE MRI • Best technology of its time • most accurate • most comprehensive • safest • Terrible initial user- experience, especially with children • 80% ofpediatric patients had to be sedated when undergoing MRI scans • Doctor quote: “The worst part of my day is when I have to give a child an MRI.”

24. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public DT Case Study: GE MRI • Getting intimately familiar with end-users (specifically pediatric patients) led to the insight: • These sick children miss out on a lot of adventures • Revamped user-experience (no change to tech)  results: • <5% of pediatric patients had to be sedated when undergoing MRI scans • Doctor quote: “The BEST part of my day is when I have to give a child an MRI and I get to dress up like a pirate!” 2424

43. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Collect relevant metrics Self-Healing Network Architectural Requirements #1: Instrumentation EM App Servers Sensors 4343

44. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Categorize metrics by degrees of relevance Self-Healing Network Architectural Requirements #2: On-Device Analytics 4444

45. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Upload critical metrics off the device to collector(s) (optimally via model-based streaming-telemetry) Self-Healing Network Architectural Requirements #3: Telemetry EM Collector 4545

46. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Provision long-term storage, retrieval and representationof network metrics and events Self-Healing Network Architectural Requirements #4: Scalable Storage 4646

47. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Identify anomalies and trends Self-Healing Network Architectural Requirements #5: Analytics Engine 4747

48. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Analyze all data points and permutations for cognitive and predictive analytics Self-Healing Network Architectural Requirements #6: Machine Learning 4848

49. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Identify root cause of issues by contextually correlating data Self-Healing Network Architectural Requirements #7: Guided Troubleshooting EM Analytics Engine 4949

50. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Self-Healing Network Architectural Requirements #8: Self-Remediation Present actionable insights to the operator Solicit input to remediate the root cause Present a self-remediation option EM Analytics EngineEM Network Controller Do you want to take the recommended action? Yes No Do you want to take the recommended action? Yes NoAlwaysAlways 5050

52. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public DNA Software Capabilities Cloud Service Management Automation Analytics Virtualization DNA-Ready Physical andVirtual infrastructure Security Cisco DNA Architecture 5252

53. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public DNA Center A single pane-of-glass for Design, Provision, Policy and Assurance DNA CenterAppliance EM NDP NDP: Network Data Platform (Analytics Engine) EM NCP NCP Network Controller Platform (Network Controller) Automation Analytics 5353

54. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public DNA Assurance Everything as a Sensor 150+ Actionable Insights Client | Applications | Wireless | Switching | Routing Network Telemetry Contextual Data Complex Event Processing Correlated Insights Guided Remediation IPAM CMX AppD IPSLA SNMP OID Telnet DNS MIB Ping CLI DHCP Wireless AAA Syslog Router Netflow Traceroute Metadata extraction Complex correlation Steam Processing 001110101100110 1010110010 00101101 0110100 1101101 00101101 10101100110 101011000110011 Clients Baseline Application Network 54

59. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Client Onboarding Network Coverage & Capacity Network Device Monitoring Application Performance Sensor Association failures Authentication failures IP address failure Client Exclusion Excessive on-boarding time Excessive authentication time Excessive IP addressing time AAA, DHCP reachability Coverage hole AP License Utilization Client Capacity Radio Utilization Availability Crash, AP Join Failure High Availability CPU, Memory utilization Flapping AP, Hung Radio Pow er supply failures Throughput analysis Roaming pattern analysis Sticky client Slow roaming Excessive roaming RF, Roaming pattern Dual band clients prefer 2.4GHz Excessive interference Client Experience Web: HTTP & HTTPS Email: POP3, IMAP, Outlook Web Access File Transfer: FTP & TFTP Terminal: Telnet & SSHv2 Wireless Specific Correlated Insights Total Insights: 100+ issues in DNA-C 1.2 5959

63. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public DNA-C Assurance Apple Insights Device Profile Client shares these details 1. iPhone 7, iPad Pro 2. iOS 11 Support per device- group Policies and Analytics 1 Wi-Fi Analytics Client shares these details 1. BSSID 2. RSSI 3. Channel # Insights into the clients view of the network 2 Assurance Client shares these details Error code for why did it previously disconnected Provide clarity into the reliability of connectivity 3 6363

65. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Wireless Sensors Proactively Assess Performance Test your network anywhere at any time R1 Dedicated Sensor AP1800 Flexible Radio Sensors act as clients Access point  On-Boarding Tests • 802.11 Association • 802.11 Authentication & Key Exchange • IP Addressing DHCP (IPv4)  Network tests • DNS (IPv4) • RADIUS (IPv4) • First Hop Router/Default gateway (IPv4) • Intranet Host • External Host (IPv4)  Application tests • Email: POP3, IMAP, Outlook Web Access (IPv4) • File Transfer: FTP (IPv4) • Web: HTTP & HTTPS (IPv4) Flexible Radio Assignment Algorithm intelligently identifies excessive radios and seamlessly converts those into Sensor mode without client impact 6565

67. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Run Network Tests Hover over a Test to See Results 6767

68. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Filtering the View Select Only the Tests That Matter to You Now 6868

69. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Sensor Test How-To 1a. Create a New Test, Choose Location and Run Interval 6969

70. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Sensor Test How-To 1b. Choose the SSIDs to test 7070

91. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Calculating Jitter and Loss for RTP Apps RTP Header Format https://tools.ietf.org/html/rfc3550#section-5.1 Gaps in subsequent RTP packet sequence numbers identifies lost packets Jitter is calculated by comparing the timestamps of RTP packets with subsequent sequence numbers 91

92. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Calculating Latency and Loss for TCP Apps Client Server X SYN SYN-ACK ACK 6 Request 1 ACK DATA 4 DATA 3 DATA 5 DATA 3 Request 1 (Cont) X DATA 4 DATA 1 Request 2 DATA 6 DATA 2 ACK 3 ACK SND CND Request Response Retransmission RT Response Time (RT) t(First response pkt) – t(Last request pkt) Network Delay (ND) ND = ( CND + SND ) /2 Application Delay (AD) AD = RT – SND ART SND = Server Network Delay CND = Client Network Delay Packet Loss Loss ≈ Retransmissions (95%+ accuracy) Application Response Time (ART) 92

93. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Application Integration Data Center Application Servers Remote User Medium Branch Small Branch Large Branch Campus 1.2.8 Release App Integration: Other Integration Opportunities: • S4B • WebEx • Spark • CUCM • MS O365 SaaS Apps EM DNAC (NCP+ NDP) 93

94. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Why Integrate DNAC with Collab Apps? • the network doesn’t see everything • network measurements are mid-stream to the flows • loss, latency and jitter may all be induced downstream from where network-measurements are made App performance measurements made here Loss, latency and/or jitter induced here is not measured/reported

95. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Why Integrate DNAC with Cisco Collab Apps? • the network can measure QoS, but not QoE • Video codecs can react to network congestion by lowering frame-rates and/or resolution levels • Packet flows may look perfect from the network QoS perspective, even during periods when the user- experience may be significantly degraded QoS QoE

98. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Client 360—Application Experience (1.2.8 S4B) 98 S4B MOS Scores

102. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Intelligent Capture: Real-time Client and App Insights to enhance DNA Wireless Assurance • Enhance Sticky client issue analysis with Real-time location • Identify coverage holes with pinpoint accuracy (<3 mtr. accuracy) • 24x7 monitoring of Wi- Fi and non-Wi-Fi interference using Intelligent Capture radio • 24x7 wIPS forensics to prevent over the air attacks using Intelligent Capture radio • Real-time client RF view • In-service packet captures using Intelligent Capture analytics • Monitor Client Onboarding real-time • Real-time App performance insights Client and App Real-time Forensics Hyperlocation – Client Pin-pointing Total Secure Coverage Monitoring • Onboarding Tests • Network Services Tests • App Connectivity Tests • App Experience Tests Active Sensor Testing 102

103. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 1 On-Demand RF Scanner Key Issues addressed Poor RF Zones RF design optimization Sticky client analysis with real-time location Key Industries Healthcare, Distribution, Logistics, Retail and Higher Ed. VoIP performance and audio issues Situation • A large logistics company uses Wi-Fi operated Auto-guided vehicles (AGV) in its logistics warehouses. Due to high ceilings and large moving metal structures, these warehouse have dynamic RF scenarios • Due to little human involvement sometimes these machines don’t take optimal paths due to temporal coverage hole Solution • IT staff uses the On-Demand RF scanner tool to validate AGV’s RF coverage during the live operation and detect coverage holes in a highly dynamic RF environment • This helps IT staff immensely in RF design, planning and optimization Live RF Network || Pause 11:50:32 Record Session Red Spot: Coverage Hole KPI List Graph* SNR RSSI MCS Throughput Packet retry Green Spot: Voice-quality Throughput Orange Spot: Browsing-quality Throughput Red Spot: Coverage Hole Troubleshooting Use Cases using Intelligent Capture

104. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 2 Automated Packet Captures Key Issues addressed On-boarding and roaming failures VOIP Performance issues Sticky client analysis with real-time location Key Industries All segments Situation • A Financial services giant decided to adopt to BYOD to encourage people to use its Virtual Desktop Infrastructure for a certain critical applications. In the first week, John discovered longer Onboarding time from mobile device compare to laptop and received complaints from several end-users Solution • IT staff turned on the Auto PCAP to capture onboarding and roaming failure anomalies for the BYOD device at a specific site. This allowed IT staff to capture de-authentication packets that are typically observed when the client has driver issues • IT staff also had access to detailed PCAP analysis that enabled them to identify the root cause behind onboarding and roaming failures Visual Packet Trace Analyzer (<5 sec) Troubleshooting Use Cases using Intelligent Capture Real-time Anomalies with Auto PCAPs (<5 sec)

105. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Real-time Client Location Tracking (<5 sec) 3 VIP Service Assurance Visual Packet Trace Analyzer (<5 sec) Key Issues addressed On-boarding and roaming failures w/ PCAP Remote Wireless troubleshooting through Full PCAP Sticky client analysis with real-time location Key Industries Healthcare, Distribution, Logistics, Retail, Higher Ed, MSPs Service Level Assurance for critical apps/users Situation • A large healthcare deployed 5000 vital sign monitor for every in-patient. These vital sign allow doctors to monitor critical parameters on a real-time basis • Wi-Fi connection of vital sign monitor starts to get disrupted on an intermitted basis during the middle of the day and if not addressed it can be life threateningSolution • IT staff uses Live Troubleshooting tool to perform detailed forensics on both the client state and the location • Using packet trace analyzer, the IT staff is able to visualize frequent roaming and re-authentication failures from the device along with lower RSSI than anticipated in particular location Troubleshooting Use Cases using Intelligent Capture

106. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Intelligent Capture—Wireless OnBoarding Analysis 106

108. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public Intelligent Capture—Application Analysis (cont) 108

110. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Kairos Cloud Cloud Based 1 click deployment ` Strong Anonymization All Data sent to the cloud is anonymize and fed in the ML algorithms to improve experience of other customers Support of Wireless/Wired/IoT Extensible to all networking gears Anonymous Anonymous Anonymous Anonymous On-going Cross Learning Building of behavioral models using anonymized data set covering a broad range of deployments models Customer B Customer A Customer D Customer C Cognitive & Predictive Analytics with Machine Learning Collect Network Data in the cloud, Pipeline of AI (Machine Learning) Algorithms to address uses cases Cognitive analytics & Predictive analytics sis, long term analysis models, optional close loop control (e.g. network to end device) DNA Analytics – Kairos Architecture

111. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Cisco PaaS NDP platformDNA Controller DNA Center Assurance UI Machine Learning Stack Graphical Models Deep Learning Time Series Models NLP/NLG Public Cloud Orchestrator Trained Models Multi-Customer Database Strong Anonymization Prediction Pipelines APIs Batch Pipelines Training Data Models ETL Pipelines Collectors Public Broker Feature Constructors DNA Analytics – Kairos Cloud Architecture Network ServicesDC WA N Office Site DH CP CM X Customer Network Network Control Points Metrics, Events, Config, Notifications Protocols & APIs (SNMP, JSON, NetFlow, pxGrid, CLI, ...) Data Collection from various sources (SNMP, JSON-based protocol, Logs, CLI, …) – Agnostic to data source & platform Anonymization of sensitive and/or personal data (if any) Batch pipelines that run at regular time intervals to continuously train models on multi-customer data.Extraction of data from various sources, Computation of use case specific variables and Conversion to a unique data model (KID format)

112. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Cisco PaaS NDP platformDNA Controller DNA Center Assurance UI Machine Learning Stack Graphical Models Deep Learning Time Series Models NLP/NLG Public Cloud Orchestrator Trained Models Multi-Customer Database Strong Anonymization Prediction Pipelines APIs Batch Pipelines Training Data Models ETL Pipelines Collectors Public Broker Feature Constructors DNA Analytics – Kairos Cloud Architecture Network ServicesDC WA N Office Site DH CP CM X Customer Network Network Control Points Metrics, Events, Config, Notifications Protocols & APIs (SNMP, JSON, NetFlow, pxGrid, CLI, ...) On-premise Kairos UI, fully integrated in DNA Center, and serving data computed in the cloud locally, de- anonymization. Prediction pipelines that apply ML models trained using multi- customer batch data on single-customer live data to produce use case predictions. On-premise Orchestrator responsible for authenticating and relaying control instructions from the cloud (e.g. Fusion)

113. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Cisco PaaS NDP platformDNA Controller DNA Center Assurance UI Machine Learning Stack Graphical Models Deep Learning Time Series Models NLP/NLG Public Cloud Orchestrator Trained Models Multi-Customer Database Strong Anonymization Prediction Pipelines APIs Batch Pipelines Training Data Models ETL Pipelines Collectors Public Broker Feature Constructors DNA Analytics – Kairos On-Prem Architecture Dynamic Parameter Tuning (DPT) Network ServicesDC WA N Office Site DH CP CM X Customer Network Network Control Points Metrics, Events, Config, Notifications Protocols & APIs (SNMP, JSON, NetFlow, pxGrid, CLI, ...) • AD 100% on premise but sophisticatedmodels computed in the cloud and pushed on-premise • No data sent to the cloud

114. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public#CLUS Why do we need Machine Learning? • Anomaly detection • Dynamic network performanceat different times and on different network conditions • Different expected performanceon different SSIDs and/or locations for the same customer • Different expected performancefor different customers • Static thresholds (even if configurable) would likely raise many false positives or miss relevant events • Root cause analysis • Automatic selection of relevant KPIs explaining an issue • Cross-correlation across multipledevices • Long-term trending • Automatically identifying trends and behavior changes on network entities/locations 114

115. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Radio Throughput – Bad RF conditions Category Real-time Anomaly Detection Context University Findings Throughput drops when interference increases as well as 100% of clients have low RSSI and SNR Root Cause Coverage issue. Actions Review the RF design to provide better coverage in this área.

116. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Onboarding Time rate due to DHCP Category Real-time Anomaly Detection Context University / local user SSID (Open) Findings Onboarding time spiking around 7 AM, in relation with some slow DHCP time. Note: Issue raised on onboarding time taking around 3 seconds (because the model predicted less than 2 seconds to be the normal/expected onboarding time). Root Cause The WLC serves dorms areas and the peak is happening when people wake up in the morning (higher peak load), but then both the client count and issue ends as soon as people move out of the dorms. Actions Verification of DHCP performance under peak times.

117. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Onboarding failure – AAA backend maintenance Category Real-time Anomaly Detection Context University / eduroam Findings Spike of AAA Auth failures and increased AAA time. Root Cause The customer is confident that this is due to an eduroam night maintenace window. Actions None, as this issue was expected

118. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential Smart Dashboards – Channel change count Category Smart Dashboards Context University Findings Two 5 GHz radios that usually had less than 20 channel changes per week, suddenly have >50 Actions The following week those APs went back to their usual behavior.

120. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential DNA Assurance—Summary • Most networking departments today are bogged down by operations spending the majority of their time manually configuring and troubleshooting their networks • Enabling an intent-based closed-loop architecture, including automation and analytics, significantly frees up IT time and resources to drive innovation • DNA Assurance provides actionable 150+ insights for: • Clients • Network Devices, and • Applications • Machine Learning provides even deeper insights

121. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public DNAC 1.2 Platform: Scale and Hardware specification • Centralized deployment, cloud tethered • 1 RU Small form factor • 2 x 10Gbps Data links • Built in Network Telemetry collection(FNF, SNMP, Syslog) • Built in Contextual connectors (ISE/PxGrid, IPAM, Location) • HA (3 Node, Automation), RBAC, Backup/Restore, Scheduler, APIs • 64-bit x86 Processors • Solid State Disks in RAID10 • Hardware MRAID Controller • Dual PSU Scale:Single Node 5,000  4K APs + 1K Network Devices 25,000  Clients/Hosts 121

122. © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Confidential © 2018 Cisco and/or its affiliates. All rights reserved. Cisco Public CAT2K / CAT3K / CAT4K Switches CAT9K / CAT6K / N7K Switches ASR / ISR / CSRv Routers CA T2 K R ecommended OS M inimum OS C2960-L IOS 15.2(2)E7 IOS 15.2(1)E1 C2960-P IOS 15.2(2)E7 IOS 15.2(1)E1 C2960-C IOS 15.2(2)E8 IOS 15.2(1)E1 C2960-CPD IOS 15.2(2)E8 IOS 15.2(1)E1 C2960-X Stack IOS 15.2(2)E6 IOS ≥ 12.1 C2960-XR IOS 15.2(2)E6 IOS ≥ 12.1 C2960-XR Stack IOS 15.2(2)E6 IOS ≥ 12.1 C2960-CX IOS 15.2(4)E3 IOS ≥ 12.1 CA T3 K R ecommended OS M inimum OS C3560-CX IOS 15.2(6)E All Versions C3650 (Copper) IOS-XE 16.6.1 All Versions C3650-Stack IOS-XE 16.6.1 All Versions C3850(Copper/Fiber) IOS-XE 16.6.1 All Versions C3850-Stack (Copper/Fiber) IOS-XE 16.6.1 All Versions CA T4 K R ecommended OS M inimum OS C4500-X IOS-XE 3.10E All Versions C4500-E (SUP 7E|7LE|8LE) IOS-XE 3.10E All Versions C4507R+E (SUP 7E|7LE|8LE) IOS-XE 3.10E All Versions C4503E (Sup 8E|9E) IOS-XE 3.10E All Versions C4506E (Sup 8E|9E) IOS-XE 3.10E All Versions C4507R+E (Sup 8E|9E) IOS-XE 3.10E All Versions C4510R+E (Sup 8E|9E) IOS-XE 3.10E All Versions C AT9K Re commended OS M i nimumOS C9300 IOS-XE 16.6.2 IOS-XE 16.6.1 C9300 Stack IOS-XE 16.6.2 IOS-XE 16.6.1 C9400-LC-48UX IOS-XE 16.6.2 IOS-XE 16.6.1 C9400-LC-24XS IOS-XE 16.6.2 IOS-XE 16.6.1 C9400 (Sup1XL) IOS-XE 16.6.2 IOS-XE 16.6.1 C9400 (Sup1E) IOS-XE 16.6.2 IOS-XE 16.6.1 C9500 IOS-XE 16.6.2 IOS-XE 16.6.1 C9500 Stack IOS-XE 16.6.2 IOS-XE 16.6.1 IS R 4K R ecommended OS M inimum OS ISR 4431 WIP WIP ISR 4221 WIP WIP ISR 4351 WIP WIP ISR 4451-X WIP WIP CA T6 K R ecommended OS M inimum OS C6503E (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2 C6504E (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2 C6506E (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2 C6509E (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2 C6513E (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2 C6807-XL (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2 C6840-X (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2 C6880-X (Sup 2T|6T) IOS 15.5.1 SY ≥ 12.2 A S R 1K R ecommended OS M inimum OS ASR 1001-X WIP ≥ 15.2(2)S, ≥ 15.3(1)S1 ASR 1002-X WIP ≥ 15.2(2)S, ≥ 15.3(1)S1 ASR 1006-X WIP ≥ 15.2(2)S, ≥ 15.3(1)S1 ASR 1009-X (RP2|RP3) WIP WIP ASR 1001-HX WIP WIP ASR 1002-HX WIP WIP V ir tual Router R ecommended OS M inimum OS CSRv (Virtual) WIP WIP IS R 1K ( Selected PIDs Only ) R ecommend ed O S M inimum O S C1112-8P + (LTEEA) IOS-XE 16.7.1 IOS-XE 16.6.1 C1113-8P + (M,LTE*,WE,WA,WZ,MWE) IOS-XE 16.7.1 IOS-XE 16.6.1 C1114-8P + (LTEEA) IOS-XE 16.7.1 IOS-XE 16.6.1 C1115-8P + (PM, LTEEA,PMLTEEA) IOS-XE 16.7.1 IOS-XE 16.6.1 Wireless Controllers / APs DNAC 1.2 Supported Network Platforms 122

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