Nvidia SMI and ServiceNow Integration

Input and output integration overview

The Nvidia SMI Plugin enables the retrieval of detailed statistics about NVIDIA GPUs attached to the host system, providing essential insights for performance monitoring.

This output plugin streams metrics from Telegraf directly to a ServiceNow MID Server via HTTP, leveraging the nowmetric serializer for efficient integration with ServiceNow’s Operational Intelligence and Event Management.

Integration details

Nvidia SMI

The Nvidia SMI Plugin is designed to gather metrics regarding the performance and status of NVIDIA GPUs on the host machine. By leveraging the capabilities of the nvidia-smi command-line tool, this plugin pulls crucial information such as GPU memory utilization, temperature, fan speed, and various performance metrics. This data is essential for monitoring GPU health and performance in real-time, particularly in environments where GPU performance directly impacts computing tasks, such as machine learning, 3D rendering, and high-performance computing. The plugin provides flexibility by allowing users to specify the path to the nvidia-smi binary and configure polling timeouts, accommodating both Linux and Windows systems where the nvidia-smi tool is commonly located. With its ability to collect detailed statistics on each GPU, this plugin becomes a vital resource for any infrastructure relying on NVIDIA hardware, facilitating proactive management and performance tuning.

ServiceNow

Telegraf can be used to send metric data directly to a ServiceNow MID Server REST endpoint. Metrics are formatted either using ServiceNow’s Operational Intelligence (OI) format or JSONv2 format, enabling seamless integration with ServiceNow’s Event Management and Operational Intelligence platforms. The serializer batches metrics efficiently, reducing network overhead by minimizing the number of HTTP POST requests. This integration allows users to quickly leverage metrics in ServiceNow for enhanced observability, proactive incident management, and performance monitoring, with ServiceNow’s operational intelligence capabilities.

Configuration

Nvidia SMI

[[inputs.nvidia_smi]]
  ## Optional: path to nvidia-smi binary, defaults "/usr/bin/nvidia-smi"
  ## We will first try to locate the nvidia-smi binary with the explicitly specified value (or default value),
  ## if it is not found, we will try to locate it on PATH(exec.LookPath), if it is still not found, an error will be returned
  # bin_path = "/usr/bin/nvidia-smi"

  ## Optional: timeout for GPU polling
  # timeout = "5s"

ServiceNow

[[outputs.http]]
  ## ServiceNow MID Server metrics endpoint
  url = "http://mid-server.example.com:9082/api/mid/sa/metrics"

  ## HTTP request method
  method = "POST"

  ## Basic Authentication credentials
  username = "evt.integration"
  password = "P@$$w0rd!"

  ## Data serialization format for ServiceNow
  data_format = "nowmetric"

  ## Metric format type: "oi" (default) or "jsonv2"
  nowmetric_format = "oi"

  ## HTTP Headers
  [outputs.http.headers]
    Content-Type = "application/json"
    Accept = "application/json"

  ## Optional timeout
  # timeout = "5s"

  ## TLS configuration options
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  # insecure_skip_verify = false

Input and output integration examples

Nvidia SMI

1. Real-Time GPU Monitoring for ML Training: Continuously monitor the GPU utilization and memory usage during machine learning model training. This enables data scientists to ensure that their GPUs are not being overutilized or underutilized, optimizing resource allocation and reviewing performance bottlenecks in real-time.

2. Automated Alerts for Overheating GPUs: Implement a system using the Nvidia SMI plugin to track GPU temperatures and set alerts for instances where temperatures exceed safe thresholds. This proactive monitoring can prevent hardware damage and improve system reliability by alerting administrators to potential cooling issues before they result in failure.

3. Performance Baselines for GPU Resources: Establish baseline performance metrics for your GPU resources. By regularly collecting data and analyzing trends in GPU usage, organizations can identify anomalies and optimize their workloads accordingly, leading to enhanced operational efficiency.

4. Dockerized GPU Usage Insights: In a containerized environment, use the plugin to monitor GPU performance from within a Docker container. This allows developers to track GPU performance of their applications in production, facilitating troubleshooting and performance optimization within isolated environments.

ServiceNow

1. Proactive Incident Management: Utilize the Telegraf and ServiceNow integration to stream infrastructure and application metrics in real-time to ServiceNow Event Management. Automatically trigger incidents or remediation workflows based on thresholds, significantly reducing incident detection and response times.

2. End-to-End Application Monitoring: Deploy Telegraf agents across multiple layers of an application stack, sending performance metrics directly into ServiceNow. Leveraging ServiceNow’s Operational Intelligence, teams can correlate metrics across components, quickly identifying performance bottlenecks.

3. Dynamic CI Performance Tracking: Integrate Telegraf metrics with ServiceNow’s CMDB by using this plugin to push performance data, allowing automatic updates of Configuration Item (CI) health states based on live metrics. This ensures an accurate and current state of infrastructure health in ServiceNow.

4. Cloud Resource Optimization: Collect metrics from hybrid and multi-cloud infrastructures using Telegraf, streaming directly to ServiceNow. Leverage these metrics for real-time analytics, predictive capacity planning, and resource optimization, enabling proactive management and reduced operational costs.

Feedback

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