Cisco Model-Driven Telemetry and OSI PI 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 setup converts Telegraf into a lightweight PI Web API publisher, letting you push any Telegraf metric into the OSI PI System with a simple HTTP POST.

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.

OSI PI

OSI PI is an data management and analytics platform used in energy, manufacturing, and critical infrastructure. The PI Web API is its REST interface, exposing endpoints such as /piwebapi/streams/{WebId}/value that accept JSON payloads containing a Timestamp and Value. By pairing Telegraf’s flexible HTTP output with this endpoint, any metric Telegraf collects—SNMP counters, Modbus readings, Kubernetes stats—can be written directly into PI without installing proprietary interfaces. The configuration above authenticates with Basic or Kerberos, serializes each batch to JSON, and renders a minimal body template that aligns with PI Web API’s single-value write contract. Because Telegraf already supports batching, TLS, proxies, and custom headers, this approach scales from edge gateways to cloud VMs, allowing organizations to back-fill historical data, stream live telemetry, or mirror non-PI sources (e.g., Prometheus) into the PI data archive. It also sidesteps older SDK dependencies and enables hybrid architectures where PI remains on-prem while Telegraf agents run in containers or IIoT devices.

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"

OSI PI

[[outputs.http]]
  ## PI Web API endpoint for writing a single value to a PI Point by Web ID
  url = "https://${PI_HOST}/piwebapi/streams/${WEB_ID}/value"

  ## Use POST for each batch
  method       = "POST"
  content_type = "application/json"

  ## Basic-auth header (base64-encoded "DOMAIN\\user:password")
  headers = { Authorization = "Basic ${BASIC_AUTH}" }

  ## Serialize Telegraf metrics as JSON
  data_format           = "json"
  json_timestamp_units  = "1ms"

  ## Render the JSON body that PI Web API expects
  body_template = """
  {{ range .Metrics -}}
  { "Timestamp": "{{ .timestamp | formatDate \"2006-01-02T15:04:05Z07:00\" }}", "Value": {{ index .fields 0 }} }
  {{ end -}}
  """

  ## Tune networking / batching if needed
  # timeout     = "10s"
  # batch_size  = 1

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.

OSI PI

1. Remote Pump Stations Telemetry Bridge: Install Telegraf on edge gateways at oil-field pump stations, gather flow-meter and vibration readings over Modbus, and POST them to the PI Web API. Operations teams view real-time data in PI Vision without deploying heavyweight PI interfaces, while bandwidth-friendly batching keeps satellite links economical.

2. Green-Energy Micro-Grid Dashboard: Export inverter, battery, and weather metrics from MQTT into Telegraf, which relays them to PI. PI AF analytics can calculate real-time power balance and feed a campus dashboard; historical deltas inform sustainability reports.

3. Brownfield SCADA Modernization: Legacy PLCs logged to CSV are ingested by Telegraf’s tail input; each row is parsed and immediately sent to PI via HTTP, creating a live data stream that co-exists with archival files while the SCADA upgrade proceeds incrementally.

4. Synthetic Data Generator for Training: Telegraf’s exec input can run a script that emits simulated sensor patterns. Posting those metrics to a non-production PI server through the Web API supplies realistic datasets for PI Vision training sessions without risking production tags.

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