Cisco Model-Driven Telemetry and Splunk 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 output plugin facilitates direct streaming of Telegraf collected metrics into Splunk via the HTTP Event Collector, enabling easy integration with Splunk’s powerful analytics platform.

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.

Splunk

Use Telegraf to easily collect and aggregate metrics from many different sources and send them to Splunk. Utilizing the HTTP output plugin combined with the specialized Splunk metrics serializer, this configuration ensures efficient data ingestion into Splunk’s metrics indexes. The HEC is an advanced mechanism provided by Splunk designed to reliably collect data at scale via HTTP or HTTPS, providing critical capabilities for security, monitoring, and analytics workloads. Telegraf’s integration with Splunk HEC streamlines operations by leveraging standard HTTP protocols, built-in authentication, and structured data serialization, optimizing metrics ingestion and enabling immediate actionable insights.

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"

Splunk

[[outputs.http]]
  ## Splunk HTTP Event Collector endpoint
  url = "https://splunk.example.com:8088/services/collector"

  ## HTTP method to use
  method = "POST"

  ## Splunk authentication token
  headers = {"Authorization" = "Splunk YOUR_SPLUNK_HEC_TOKEN"}

  ## Serializer for formatting metrics specifically for Splunk
  data_format = "splunkmetric"

  ## Optional parameters
  # timeout = "5s"
  # insecure_skip_verify = false
  # tls_ca = "/path/to/ca.pem"
  # tls_cert = "/path/to/cert.pem"
  # tls_key = "/path/to/key.pem"

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.

Splunk

1. Real-Time Security Analytics: Utilize this plugin to stream security-related metrics from various applications into Splunk in real-time. Organizations can detect threats instantly by correlating data streams across systems, significantly reducing detection and response times.

2. Multi-Cloud Infrastructure Monitoring: Integrate Telegraf to consolidate metrics from multi-cloud environments directly into Splunk, enabling comprehensive visibility and operational intelligence. This unified monitoring allows teams to detect performance issues quickly and streamline cloud resource management.

3. Dynamic Capacity Planning: Deploy the plugin to continuously push resource metrics from container orchestration platforms (like Kubernetes) into Splunk. Leveraging Splunk’s analytics capabilities, teams can automate predictive scaling and resource allocation, avoiding resource bottlenecks and minimizing costs.

4. Automated Incident Response Workflows: Combine this plugin with Splunk’s alerting system to create automated incident response workflows. Metrics collected by Telegraf trigger real-time alerts and automated remediation scripts, ensuring rapid resolution and maintaining high system availability.

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