Intel PowerStat and Splunk Integration

Input and output integration overview

Monitor power statistics on Intel-based platforms and is compatible with Linux-based operating systems. It helps in understanding and managing power efficiency and CPU performance.

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

Intel PowerStat

The Intel PowerStat plugin is designed to monitor power statistics specifically on Intel-based platforms running a Linux operating system. It offers visibility into critical metrics such as CPU temperature, utilization, and power consumption, making it essential for power saving initiatives and workload migration strategies. By leveraging telemetry frameworks, this plugin enables users to gain insights into platform-level metrics that help with monitoring and analytics systems in the context of Management and Orchestration (MANO). It facilitates the ability to make informed decisions and perform corrective actions based on the state of the platform, ultimately contributing to better system efficiency and reliability.

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

Intel PowerStat

[[inputs.intel_powerstat]]
  # package_metrics = ["current_power_consumption", "current_dram_power_consumption", "thermal_design_power"]
  # cpu_metrics = []
  # included_cpus = []
  # excluded_cpus = []
  # event_definitions = ""
  # msr_read_timeout = "0ms"

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

Intel PowerStat

1. Optimizing Data Center Energy Usage: Monitor power consumption metrics across all CPUs in a data center. By capturing real-time data, administrators can identify which servers consume the most power and implement shutdowns or load balancing strategies during low demand periods, effectively reducing operational costs.

2. Dynamic Workload Migration Based on Power Efficiency: Integrate this plugin with a cloud orchestration tool to enable dynamic migration of workloads based on power usage metrics. If a particular server is recorded as consuming excessive power without corresponding output, the orchestrator can seamlessly migrate workloads to more efficient nodes, ensuring optimal resource utilization and lower energy expenses.

3. Monitoring and Alerting Mechanism for Overheating CPUs: Implement an alerting system using the CPU temperature metrics captured by Intel PowerStat. Setting thresholds for temperature can alert system administrators when a CPU is prone to overheating, allowing proactive measures to be taken before hardware damage occurs, ultimately extending the life of the components.

4. Performance Benchmarking for CPU-intensive Applications: Use the metrics provided to benchmark the performance of CPU-intensive applications. By analyzing the cpu_frequency, cpu_temperature, and power metrics under load, developers can optimize application performance and make informed decisions regarding scaling and resource allocation.

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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