Intel PowerStat and Prometheus 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.

The Prometheus Output Plugin enables Telegraf to expose metrics at an HTTP endpoint for scraping by a Prometheus server. This integration allows users to collect and aggregate metrics from various sources in a format that Prometheus can process efficiently.

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.

Prometheus

This plugin for facilitates the integration with Prometheus, a well-known open-source monitoring and alerting toolkit designed for reliability and efficiency in large-scale environments. By working as a Prometheus client, it allows users to expose a defined set of metrics via an HTTP server that Prometheus can scrape at specified intervals. This plugin plays a crucial role in monitoring diverse systems by allowing them to publish performance metrics in a standardized format, enabling extensive visibility into system health and behavior. Key features include support for configuring various endpoints, enabling TLS for secure communication, and options for HTTP basic authentication. The plugin also integrates seamlessly with global Telegraf configuration settings, supporting extensive customization to fit specific monitoring needs. This promotes interoperability in environments where different systems must communicate performance data effectively. Leveraging Prometheus’s metric format, it allows for flexible metric management through advanced configurations such as metric expiration and collectors control, offering a sophisticated solution for monitoring and alerting workflows.

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"

Prometheus

[[outputs.prometheus_client]]
  ## Address to listen on.
  ##   ex:
  ##     listen = ":9273"
  ##     listen = "vsock://:9273"
  listen = ":9273"

  ## Maximum duration before timing out read of the request
  # read_timeout = "10s"
  ## Maximum duration before timing out write of the response
  # write_timeout = "10s"

  ## Metric version controls the mapping from Prometheus metrics into Telegraf metrics.
  ## See "Metric Format Configuration" in plugins/inputs/prometheus/README.md for details.
  ## Valid options: 1, 2
  # metric_version = 1

  ## Use HTTP Basic Authentication.
  # basic_username = "Foo"
  # basic_password = "Bar"

  ## If set, the IP Ranges which are allowed to access metrics.
  ##   ex: ip_range = ["192.168.0.0/24", "192.168.1.0/30"]
  # ip_range = []

  ## Path to publish the metrics on.
  # path = "/metrics"

  ## Expiration interval for each metric. 0 == no expiration
  # expiration_interval = "60s"

  ## Collectors to enable, valid entries are "gocollector" and "process".
  ## If unset, both are enabled.
  # collectors_exclude = ["gocollector", "process"]

  ## Send string metrics as Prometheus labels.
  ## Unless set to false all string metrics will be sent as labels.
  # string_as_label = true

  ## If set, enable TLS with the given certificate.
  # tls_cert = "/etc/ssl/telegraf.crt"
  # tls_key = "/etc/ssl/telegraf.key"

  ## Set one or more allowed client CA certificate file names to
  ## enable mutually authenticated TLS connections
  # tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]

  ## Export metric collection time.
  # export_timestamp = false

  ## Specify the metric type explicitly.
  ## This overrides the metric-type of the Telegraf metric. Globbing is allowed.
  # [outputs.prometheus_client.metric_types]
  #   counter = []
  #   gauge = []

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.

Prometheus

1. Monitoring Multi-cloud Deployments: Utilize the Prometheus plugin to collect metrics from applications running across multiple cloud providers. This scenario allows teams to centralize monitoring through a single Prometheus instance that scrapes metrics from different environments, providing a unified view of performance metrics across hybrid infrastructures. It streamlines reporting and alerting, enhancing operational efficiency without needing complex integrations.

2. Enhancing Microservices Visibility: Implement the plugin to expose metrics from various microservices within a Kubernetes cluster. Using Prometheus, teams can visualize service metrics in real time, identify bottlenecks, and maintain system health checks. This setup supports adaptive scaling and resource utilization optimization based on insights generated from the collected metrics. It enhances the ability to troubleshoot service interactions, significantly improving the resilience of the microservice architecture.

3. Real-time Anomaly Detection in E-commerce: By leveraging this plugin alongside Prometheus, an e-commerce platform can monitor key performance indicators such as response times and error rates. Integrating anomaly detection algorithms with scraped metrics allows the identification of unexpected patterns indicating potential issues, such as sudden traffic spikes or backend service failure. This proactive monitoring empowers business continuity and operational efficiency, minimizing potential downtimes while ensuring service reliability.

4. Performance Metrics Reporting for APIs: Utilize the Prometheus Output Plugin to gather and report API performance metrics, which can then be visualized in Grafana dashboards. This use case enables detailed analysis of API response times, throughput, and error rates, promoting continuous improvement of API services. By closely monitoring these metrics, teams can quickly react to degradation, ensuring optimal API performance and maintaining a high level of service availability.

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