Wireguard and Google Cloud Monitoring Integration

Input and output integration overview

This plugin collects and reports statistics from the local Wireguard server, providing insights into its interfaces and peers.

The Stackdriver plugin allows users to send metrics directly to a specified project in Google Cloud Monitoring, facilitating robust monitoring capabilities across their cloud resources.

Integration details

Wireguard

The Wireguard plugin collects statistics on the local Wireguard server using the wgctrl library. It reports gauge metrics for Wireguard interface device(s) and its peers. This enables monitoring of various parameters related to Wireguard functionality, enhancing an administrator’s capability to assess the performance and status of their Wireguard setup. The metrics collected can lead to proactive management of the network interfaces, aiding in detecting and resolving issues before they impact service availability.

Google Cloud Monitoring

This plugin writes metrics to a project in Google Cloud Monitoring, which used to be known as Stackdriver. Authentication is a prerequisite and can be achieved via service accounts or user credentials. The plugin is designed to group metrics by a namespace variable and metric key, facilitating organized data management. However, users are encouraged to use the official naming format for enhanced query efficiency. The plugin supports additional configurations for managing metric representation and allows tags to be treated as resource labels. Notably, it imposes certain restrictions on the data it can accept, such as not allowing string values or points that are out of chronological order.

Configuration

Wireguard

[[inputs.wireguard]]
  ## Optional list of Wireguard device/interface names to query.
  ## If omitted, all Wireguard interfaces are queried.
  # devices = ["wg0"]

Google Cloud Monitoring

[[outputs.stackdriver]]
  ## GCP Project
  project = "project-id"

  ## Quota Project
  ## Specifies the Google Cloud project that should be billed for metric ingestion.
  ## If omitted, the quota is charged to the service account’s default project.
  ## This is useful when sending metrics to multiple projects using a single service account.
  ## The caller must have the `serviceusage.services.use` permission on the specified project.
  # quota_project = ""

  ## The namespace for the metric descriptor
  ## This is optional and users are encouraged to set the namespace as a
  ## resource label instead. If omitted it is not included in the metric name.
  namespace = "telegraf"

  ## Metric Type Prefix
  ## The DNS name used with the metric type as a prefix.
  # metric_type_prefix = "custom.googleapis.com"

  ## Metric Name Format
  ## Specifies the layout of the metric name, choose from:
  ##  * path: 'metric_type_prefix_namespace_name_key'
  ##  * official: 'metric_type_prefix/namespace_name_key/kind'
  # metric_name_format = "path"

  ## Metric Data Type
  ## By default, telegraf will use whatever type the metric comes in as.
  ## However, for some use cases, forcing int64, may be preferred for values:
  ##   * source: use whatever was passed in
  ##   * double: preferred datatype to allow queries by PromQL.
  # metric_data_type = "source"

  ## Tags as resource labels
  ## Tags defined in this option, when they exist, are added as a resource
  ## label and not included as a metric label. The values from tags override
  ## the values defined under the resource_labels config options.
  # tags_as_resource_label = []

  ## Custom resource type
  # resource_type = "generic_node"

  ## Override metric type by metric name
  ## Metric names matching the values here, globbing supported, will have the
  ## metric type set to the corresponding type.
  # metric_counter = []
  # metric_gauge = []
  # metric_histogram = []

  ## NOTE: Due to the way TOML is parsed, tables must be at the END of the
  ## plugin definition, otherwise additional config options are read as part of
  ## the table

  ## Additional resource labels
  # [outputs.stackdriver.resource_labels]
  #   node_id = "$HOSTNAME"
  #   namespace = "myapp"
  #   location = "eu-north0"

Input and output integration examples

Wireguard

1. Network Performance Monitoring: Monitor the performance metrics of your Wireguard interfaces, allowing you to track bandwidth usage and identify potential bottlenecks in real-time. By integrating these statistics into your existing monitoring system, network administrators can gain insights into the efficiency of their VPN configuration and make data-driven adjustments.

2. Peer Health Checks: Implement health checks for Wireguard peers by monitoring the last handshake time and traffic metrics. If a peer shows a significant drop in RX/TX bytes or hasn’t completed a handshake in a timely manner, alerts can be triggered to address potential connectivity issues proactively.

3. Dynamic Resource Allocation: Use the metrics collected by the Wireguard plugin to dynamically allocate or adjust network resources based on current bandwidth usage and peer activity. For instance, when a peer is heavily utilized, administrators can respond by allocating additional resources or adjusting configurations to optimize performance accordingly.

4. Historical Data Analysis: Aggregate data over time to analyze historical trends in Wireguard device performance. By storing these metrics in a time-series database, teams can visualize long-term trends, assess the impact of configuration changes, and drive strategic decisions regarding network management.

Google Cloud Monitoring

1. Multi-Project Metric Aggregation: Use this plugin to send aggregated metrics from various applications across different projects into a single Google Cloud Monitoring project. This use case helps centralize metrics for teams managing multiple applications, providing a unified view for performance monitoring and enhancing decision-making. By configuring different quota projects for billing, organizations can ensure proper cost management while benefiting from a consolidated monitoring strategy.

2. Anomaly Detection Setup: Integrate the plugin with a machine learning-based analytics tool that identifies anomalies in the collected metrics. Using the historical data provided by the plugin, the tool can learn normal baseline behavior and promptly alert the operations team when unusual patterns arise, enabling proactive troubleshooting and minimizing service disruptions.

3. Dynamic Resource Labeling: Implement dynamic tagging by utilizing the tags_as_resource_label option to adaptively attach resource labels based on runtime conditions. This setup allows metrics to provide context-sensitive information, such as varying environmental parameters or operational states, enhancing the granularity of monitoring and reporting without changing the fundamental metric structure.

4. Custom Metric Visualization Dashboards: Leverage the data collected by the Google Cloud Monitoring output plugin to feed a custom metrics visualization dashboard using a third-party framework. By visualizing metrics in real-time, teams can achieve better situational awareness, notably by correlating different metrics, improving operational decision-making, and streamlining performance management workflows.

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