Mesos and Nebius Cloud Monitoring Integration

Input and output integration overview

This input plugin gathers metrics from Mesos.

This plugin allows users to effortlessly send aggregated metrics to Nebius Cloud Monitoring, leveraging the cloud’s monitoring solutions.

Integration details

Mesos

The Mesos plugin for Telegraf is designed to collect and report metrics from Apache Mesos clusters, which is essential for monitoring and observability in container orchestration and resource management. Mesos, known for its scalability and ability to manage diverse workloads, generates various metrics about resource usage, tasks, frameworks, and overall system performance. By utilizing this plugin, users can track the health and efficiency of their Mesos clusters, gather insights into resource distribution, and ensure that applications receive the necessary resources in a timely manner. The configuration allows users to specify the relevant Mesos master’s details, along with the desired metric groups to collect, making it adaptable to different deployments and monitoring needs. Overall, this plugin integrates seamlessly within the Telegraf collection pipeline, supporting detailed observability for cloud-native environments.

Nebius Cloud Monitoring

The Nebius Cloud Monitoring plugin serves as an intermediary to send custom metrics to the Nebius Cloud Monitoring service. It is designed specifically to facilitate the monitoring of applications and services running within the Nebius ecosystem. This plugin is especially useful for users of the Nebius Cloud Platform who need to leverage cloud-based monitoring capabilities without significant configuration overhead. The plugin’s integration relies on Google Cloud metadata, allowing it to automatically fetch the necessary authentication credentials from the Compute instance it operates within. Key technical considerations include the management of reserved labels to ensure metrics are recorded correctly without conflicts.

Configuration

Mesos

[[inputs.mesos]]
  ## Timeout, in ms.
  timeout = 100

  ## A list of Mesos masters.
  masters = ["http://localhost:5050"]

  ## Master metrics groups to be collected, by default, all enabled.
  master_collections = [
    "resources",
    "master",
    "system",
    "agents",
    "frameworks",
    "framework_offers",
    "tasks",
    "messages",
    "evqueue",
    "registrar",
    "allocator",
  ]

  ## A list of Mesos slaves, default is []
  # slaves = []

  ## Slave metrics groups to be collected, by default, all enabled.
  # slave_collections = [
  #   "resources",
  #   "agent",
  #   "system",
  #   "executors",
  #   "tasks",
  #   "messages",
  # ]

  ## Optional TLS Config
  # tls_ca = "/etc/telegraf/ca.pem"
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

Nebius Cloud Monitoring

[[outputs.nebius_cloud_monitoring]]
  ## Timeout for HTTP writes.
  # timeout = "20s"

  ## Nebius.Cloud monitoring API endpoint. Normally should not be changed
  # endpoint = "https://monitoring.api.il.nebius.cloud/monitoring/v2/data/write"

Input and output integration examples

Mesos

1. Resource Utilization Monitoring: Use the Mesos plugin to continually monitor CPU, memory, and disk usage across your Mesos cluster. For a rapidly scaling application, tracking these metrics helps ensure that resources are dynamically allocated according to workloads, preventing bottlenecks and optimizing performance.

2. Framework Performance Analysis: Integrate this plugin to measure the performance of different frameworks running on Mesos. By comparing active frameworks and their task success rates, you can identify which frameworks provide the best resource efficiency or may require optimization.

3. Alerts for System Health: Set up alerts based on metrics collected by the Mesos plugin to notify engineering teams when resource utilization exceeds key thresholds or when specific tasks fail. This allows for proactive intervention and maintenance before critical failures occur.

4. Capacity Planning: Utilize gathered metrics to analyze historical resource usage patterns to assist in capacity planning. By understanding peak loads and resource utilization trends, teams can make informed decisions on scaling infrastructure and deploying additional resources as needed.

Nebius Cloud Monitoring

1. Dynamic Application Monitoring: Integrate this plugin with your application to continuously send metrics related to resource usage, such as CPU and memory utilization, to Nebius Cloud Monitoring. By doing so, you can gain insights into the performance of your application, allowing for adjustments in real-time based on the metrics received.

2. Incident Response Automation: Use the Nebius Cloud Monitoring plugin to automatically send alerts and metrics when certain thresholds are reached. For instance, if a particular service’s uptime drops below a certain percentage, the plugin can be configured to report this directly to the monitoring service, enabling quicker incident response and resolution.

3. Comparative Service Analysis: Set up the plugin to send metrics from multiple cloud instances running different versions of the same application to Nebius Cloud Monitoring. This approach allows for a comparative analysis of resource usage and performance, helping teams determine which version performs best under similar workloads.

4. Aggregated Metrics Dashboard: Use this plugin to create a centralized dashboard displaying metrics from various services across your cloud instances. By aggregating different application metrics into one interface, stakeholders can assess the overall health and performance of their cloud environment easily.

Feedback

Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.