Model-Driven Telemetry. Shelly Cadora Principal Engineer, Technical Marketing
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1 Model-Driven Telemetry Shelly Cadora Principal Engineer, Technical Marketing
2 We Need More Data Use Cases Network Health Troubleshooting / Remediation SLAs, Performance Tuning Capacity Planning Product Management Trends Centralized / Software-defined Speed Scale Capabilities 2
3 Network data is bottlenecked Where Data Is Created Incomplete Scale Issues Where Data Is Useful SNMP SNMP Server sensing & measurement Non real time Syslog Syslog Collector CLI Scripts storage & analysis Unstructured Subject to Change Strong burden on back-end Must normalize different encodings, transports, data models, timestamps 3
4 The New Paradigm Free the Data Where Data Is Created Where Data Is Useful T sensing & measurement T Real time As Much Data As Fast As Useful As Easy As Possible Storage & analysis T 4
5 Three Enablers for Telemetry Push Not Pull Analytics-Ready Data Data-Model Driven 5
6 Ultra-high level picture: Config Instruction on: What data to collect With what cadence And send to where Collector 6
7 Sample CLI (XR 6.1.1) telemetry model-driven destination-group DGroup1 address family ipv port 2104 encoding self-describing-gpb protocol tcp! sensor-group SGroup1 sensor-path Cisco-IOS-XR-infra-statsdoper:infra-statistics/interfaces/interface/ latest/generic-counters! subscription Sub1 sensor-group-id SGroup1 sample-interval destination-id DGroup1 Where to Send and How What Data to Collect When 7
8 Config Model (XR 6.1.1) What Data to Collect When Where to Send and How What Data to Collect Where to Send and How When 8
![Use YANG Models in Sensor-Path module: Cisco-IOS-XR-infra-statsd-oper +--ro infra-statistics +--ro interfaces +--ro interface* [interface-name] +--ro latest +--ro generic-counters +--ro](/docs-images/81/84246032/images/9-0.jpg "packets-received +--ro bytes-received +--ro packets-sent +--ro bytes-sent +--ro multicast-packets-received +--ro broadcast-packets-received +--ro multicast-packets-sent etc How to Get Supported")
9 Use YANG Models in Sensor-Path module: Cisco-IOS-XR-infra-statsd-oper +--ro infra-statistics +--ro interfaces +--ro interface* [interface-name] +--ro latest +--ro generic-counters +--ro packets-received +--ro bytes-received +--ro packets-sent +--ro bytes-sent +--ro multicast-packets-received +--ro broadcast-packets-received +--ro multicast-packets-sent etc How to Get Supported Models tree/master/vendor/cisco/xr NETCONF <capabilities> Some Commonly Used Models 9
10 Ultra-high level picture: Transport Instruction on: What data to collect With what cadence And send to where Table 3 Table 2 Table 1 Collector 10
11 Three Transport Options Dial-Out Dial-In SYN SYN-ACK ACK Collector SYN SYN-ACK ACK Collector Data Data TCP & grpc grpc only 11
12 grpc: Like REST But Different Runs over HTTP/2 Optimize for page load time Server push, header compression, multiplexing, TLS RFC 7540 (May 2015) Preserves most HTTP1.1 syntax Defines Services ( RPCs ) Encodes Using Google Protocol Buffers ( GPB or protobufs ) Services and Messages Auto-generate code in many languages 12
13 Ultra-high level picture: Encoded Data Instruction on: What data to collect With what cadence And send to where I am the interface counters table Interface ifinerrors ifouterrors ifhcoutoctets HundredGigabitEthernet 0/1/0/ Bundle-Ether Table 3 Table 2 Table 1 Receiving unit 13
14 Basic Concept: Encoding DATA Encode Decode DATA Common Text- Based Encodings JSON XML Encoding (or serialization ) translates data (objects, state) into a format that can be transmitted across the network. When the receiver decodes ( deserializes ) the data, it has an semantically identical copy of the original data. 14
15 GPB Encoding Google Protocol Buffers (GPB) Design Goals Non-Goals Call them protobufs for short Simplicity Performance Forward/Backward Compatibility Human-Readable Self-Describing Text-based Protocol buffers are Google's language-neutral, platformneutral, extensible mechanism for serializing structured data think XML, but smaller, faster, and simpler. 15
16 Telemetry Has Two GPB Encoding Options GPB compact 1: GigabitEthernet0/0/0/0 50: : : : : <snip> 2X faster Operationally more complex (but not relative to SNMP!) GPB self-describing {InterfaceName: GigabitEthernet0/0/0/0 GenericCounters { PacketsSent: BytesSent: PacketsReceived: BytesReceived: MulticastPacketsReceived: <snip> 3X larger Native models: still need heuristics for key names 16
17 Lesson Learned: It s Not Hard to Beat SNMP 10 second poll / push 3 pollers / telemetry receivers 30 minute measurement intervals Gig E Interfaces (Line Rate) SNMP: IF-MIB (query by row) 17
18 Use Case: Bundle Polarization Telemetry 10 Second Push SNMP 1 Minute Polling G0/0/0/0 G0/0/0/1 Is the bundle polarized? 18
19 Use Case: Bundle Polarization Telemetry 10 Second Push G0/0/0/0 G0/0/0/1 Is the bundle polarized? 19
20 Use Case: Packet Loss Interpolation Yours Yours No Drops Not Yours No Drops Drops Pkts Tx VPN, Internet, etc Pkts Rx SNMP polling (5 min) first order linear interpolation detects ~10% packet loss Telemetry (10-30 sec) Polynomial interpolation Smaller losses, faster 20
21 Different Customers, Different Models Prometheus Kibana Panda ElasticSearch Kafka BYO Black Box Output Codec Logstash ST Input Codec Proprietary or OSbased Custom Open Source, Customizable Commercial Stack 21
22 Telemetry is a Journey / > 10 Seconds Periodic RP level JSON, GPB TCP, UDP Native schemas File-based policy Open Source tooling MDT YANG-modeled data OpenConfig YANG Config/modelbased policy grpc Future Event-based Low-level data 22
23 Tutorials and Blogs (devnet landing page for telemetry) (NANOG 67: 10 Lessons from Telemetry) YANG (Cisco YANG models) (lots of sample code) 23
24 Telemetry Tools on Github: Demos and Lab (dcloud telemetry lab) (demo with signalfx) (demo on NCS 5508) 24
25 Demo 25
26
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