Cisco Model-Driven Telemetry and Thanos Integration

Input and output integration overview

The Cisco Model-Driven Telemetry (MDT) plugin facilitates the collection of telemetry data from Cisco networking platforms, utilizing gRPC and TCP transport mechanisms. This plugin is essential for users looking to implement advanced telemetry solutions for better insights and operational efficiency.

This plugin sends metrics from Telegraf to Thanos using the Prometheus remote write protocol over HTTP, allowing efficient and scalable ingestion into Thanos Receive components.

Integration details

Cisco Model-Driven Telemetry

Cisco model-driven telemetry (MDT) is designed to provide a robust means of consuming telemetry data from various Cisco platforms, including IOS XR, IOS XE, and NX-OS. This plugin focuses on the efficient transport of telemetry data using either TCP or gRPC protocols, offering flexibility based on the network environment and requirements. The gRPC transport is particularly advantageous as it supports TLS for enhanced security through encryption and authentication. The plugin is compatible with a range of software versions on Cisco devices, enabling organizations to leverage telemetry capabilities across their network operations. It is especially useful for network monitoring and analytics, as it enables real-time data collection directly from Cisco devices, enhancing visibility into network performance, resource utilization, and operational metrics.

Thanos

Telegraf’s HTTP plugin can send metrics directly to Thanos via its Remote Write-compatible Receive component. By setting the data format to prometheusremotewrite, Telegraf can serialize metrics into the same protobuf-based format used by native Prometheus clients. This setup enables high-throughput, low-latency metric ingestion into Thanos, facilitating centralized observability at scale. It is particularly useful in hybrid environments where Telegraf is collecting metrics from systems outside Prometheus’ native reach, such as SNMP devices, Windows hosts, or custom apps, and streams them directly to Thanos for long-term storage and global querying.

Configuration

Cisco Model-Driven Telemetry

[[inputs.cisco_telemetry_mdt]]
 ## Telemetry transport can be "tcp" or "grpc".  TLS is only supported when
 ## using the grpc transport.
 transport = "grpc"

 ## Address and port to host telemetry listener
 service_address = ":57000"

 ## Grpc Maximum Message Size, default is 4MB, increase the size. This is
 ## stored as a uint32, and limited to 4294967295.
 max_msg_size = 4000000

 ## Enable TLS; grpc transport only.
 # tls_cert = "/etc/telegraf/cert.pem"
 # tls_key = "/etc/telegraf/key.pem"

 ## Enable TLS client authentication and define allowed CA certificates; grpc
 ##  transport only.
 # tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]

 ## Define (for certain nested telemetry measurements with embedded tags) which fields are tags
 # embedded_tags = ["Cisco-IOS-XR-qos-ma-oper:qos/interface-table/interface/input/service-policy-names/service-policy-instance/statistics/class-stats/class-name"]

 ## Include the delete field in every telemetry message.
 # include_delete_field = false

 ## Specify custom name for incoming MDT source field.
 # source_field_name = "mdt_source"

 ## Define aliases to map telemetry encoding paths to simple measurement names
 [inputs.cisco_telemetry_mdt.aliases]
   ifstats = "ietf-interfaces:interfaces-state/interface/statistics"
 ## Define Property Xformation, please refer README and https://pubhub.devnetcloud.com/media/dme-docs-9-3-3/docs/appendix/ for Model details.
 [inputs.cisco_telemetry_mdt.dmes]
#    Global Property Xformation.
#    prop1 = "uint64 to int"
#    prop2 = "uint64 to string"
#    prop3 = "string to uint64"
#    prop4 = "string to int64"
#    prop5 = "string to float64"
#    auto-prop-xfrom = "auto-float-xfrom" #Xform any property which is string, and has float number to type float64
#    Per Path property xformation, Name is telemetry configuration under sensor-group, path configuration "WORD         Distinguished Name"
#    Per Path configuration is better as it avoid property collision issue of types.
#    dnpath = '{"Name": "show ip route summary","prop": [{"Key": "routes","Value": "string"}, {"Key": "best-paths","Value": "string"}]}'
#    dnpath2 = '{"Name": "show processes cpu","prop": [{"Key": "kernel_percent","Value": "float"}, {"Key": "idle_percent","Value": "float"}, {"Key": "process","Value": "string"}, {"Key": "user_percent","Value": "float"}, {"Key": "onesec","Value": "float"}]}'
#    dnpath3 = '{"Name": "show processes memory physical","prop": [{"Key": "processname","Value": "string"}]}'

 ## Additional GRPC connection settings.
 [inputs.cisco_telemetry_mdt.grpc_enforcement_policy]
  ## GRPC permit keepalives without calls, set to true if your clients are
  ## sending pings without calls in-flight. This can sometimes happen on IOS-XE
  ## devices where the GRPC connection is left open but subscriptions have been
  ## removed, and adding subsequent subscriptions does not keep a stable session.
  # permit_keepalive_without_calls = false

  ## GRPC minimum timeout between successive pings, decreasing this value may
  ## help if this plugin is closing connections with ENHANCE_YOUR_CALM (too_many_pings).
  # keepalive_minimum_time = "5m"

Thanos

[[outputs.http]]
  ## Thanos Receive endpoint for remote write
  url = "http://thanos-receive.example.com/api/v1/receive"

  ## HTTP method
  method = "POST"

  ## Data format set to Prometheus remote write
  data_format = "prometheusremotewrite"

  ## Optional headers (authorization, etc.)
  # [outputs.http.headers]
  #   Authorization = "Bearer YOUR_TOKEN"

  ## Optional TLS configuration
  # tls_ca = "/path/to/ca.pem"
  # tls_cert = "/path/to/cert.pem"
  # tls_key = "/path/to/key.pem"
  # insecure_skip_verify = false

  ## Request timeout
  timeout = "10s"

Input and output integration examples

Cisco Model-Driven Telemetry

1. Real-Time Network Monitoring: Utilize the Cisco MDT plugin to collect network performance metrics from Cisco routers and switches. By feeding telemetry data into a visualization tool, network operators can observe traffic trends, bandwidth usage, and error rates in real-time. This proactive monitoring allows teams to swiftly address issues before they affect network performance, resulting in a more reliable service.

2. Automated Anomaly Detection: Integrate Cisco MDT with machine learning algorithms to create an automated anomaly detection system. By continuously analyzing telemetry data, the system can identify deviations from typical operational patterns, providing alerts for unusual conditions that may signify network problems or security threats, which can aid in maintaining operational integrity.

3. Dynamic Configuration Management: Leveraging the telemetry data collected from Cisco devices, organizations can implement dynamic configuration management solutions that automatically adjust network settings based on current performance indicators. For instance, if the telemetry indicates high utilization on certain links, the system could dynamically route traffic to underutilized paths, optimizing resource usage.

4. Enhanced Reporting and Analytics: Use the Cisco MDT plugin to feed detailed telemetry data into analytics platforms, enabling comprehensive reporting on network health and performance. Historical and real-time analysis can guide decision-making and strategic planning, helping organizations to allocate resources more effectively and understand their network’s operational landscape better.

Thanos

1. Agentless Cloud Monitoring: Deploy Telegraf agents across cloud VMs to collect system and application metrics, then stream them directly into Thanos using Remote Write. This provides centralized observability without requiring Prometheus nodes at each location.

2. Scalable Windows Host Monitoring: Use Telegraf on Windows machines to collect OS-level metrics and send them via Remote Write to Thanos Receive. This enables observability across heterogeneous environments with native Prometheus support only on Linux.

3. Cross-Region Metrics Federation: Telegraf agents in multiple geographic regions can push data to region-local Thanos Receivers using this plugin. From there, Thanos can deduplicate and query metrics globally, reducing latency and network egress costs.

4. Integrating Third-Party Data into Thanos: Collect metrics from custom telemetry sources such as REST APIs or proprietary logs using Telegraf inputs and forward them to Thanos via Remote Write. This brings non-native data into a Prometheus-compatible, long-term analytics pipeline.

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