VMware vSphere and Splunk Integration

Input and output integration overview

The VMware vSphere Telegraf plugin provides a means to collect metrics from VMware vCenter servers, allowing for comprehensive monitoring and management of virtual resources in a vSphere environment.

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

VMware vSphere

This plugin connects to VMware vSphere servers to gather a variety of metrics from virtual environments, enabling efficient monitoring and management of virtual resources. It interfaces with the vSphere API to collect statistics regarding clusters, hosts, resource pools, VMs, datastores, and vSAN entities, presenting them in a format suitable for analysis and visualization. The plugin is particularly valuable for administrators who manage VMware-based infrastructures, as it helps to track system performance, resource usage, and operational issues in real-time. By aggregating data from multiple sources, the plugin empowers users with insights that facilitate informed decision-making regarding resource allocation, troubleshooting, and ensuring optimal system performance. Additionally, the support for secret-store integration allows secure handling of sensitive credentials, promoting best practices in security and compliance assessments.

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

VMware vSphere

[[inputs.vsphere]]
  vcenters = [ "https://vcenter.local/sdk" ]
  username = "[email protected]"
  password = "secret"

  vm_metric_include = [
    "cpu.demand.average",
    "cpu.idle.summation",
    "cpu.latency.average",
    "cpu.readiness.average",
    "cpu.ready.summation",
    "cpu.run.summation",
    "cpu.usagemhz.average",
    "cpu.used.summation",
    "cpu.wait.summation",
    "mem.active.average",
    "mem.granted.average",
    "mem.latency.average",
    "mem.swapin.average",
    "mem.swapinRate.average",
    "mem.swapout.average",
    "mem.swapoutRate.average",
    "mem.usage.average",
    "mem.vmmemctl.average",
    "net.bytesRx.average",
    "net.bytesTx.average",
    "net.droppedRx.summation",
    "net.droppedTx.summation",
    "net.usage.average",
    "power.power.average",
    "virtualDisk.numberReadAveraged.average",
    "virtualDisk.numberWriteAveraged.average",
    "virtualDisk.read.average",
    "virtualDisk.readOIO.latest",
    "virtualDisk.throughput.usage.average",
    "virtualDisk.totalReadLatency.average",
    "virtualDisk.totalWriteLatency.average",
    "virtualDisk.write.average",
    "virtualDisk.writeOIO.latest",
    "sys.uptime.latest",
  ]

  host_metric_include = [
    "cpu.coreUtilization.average",
    "cpu.costop.summation",
    "cpu.demand.average",
    "cpu.idle.summation",
    "cpu.latency.average",
    "cpu.readiness.average",
    "cpu.ready.summation",
    "cpu.swapwait.summation",
    "cpu.usage.average",
    "cpu.usagemhz.average",
    "cpu.used.summation",
    "cpu.utilization.average",
    "cpu.wait.summation",
    "disk.deviceReadLatency.average",
    "disk.deviceWriteLatency.average",
    "disk.kernelReadLatency.average",
    "disk.kernelWriteLatency.average",
    "disk.numberReadAveraged.average",
    "disk.numberWriteAveraged.average",
    "disk.read.average",
    "disk.totalReadLatency.average",
    "disk.totalWriteLatency.average",
    "disk.write.average",
    "mem.active.average",
    "mem.latency.average",
    "mem.state.latest",
    "mem.swapin.average",
    "mem.swapinRate.average",
    "mem.swapout.average",
    "mem.swapoutRate.average",
    "mem.totalCapacity.average",
    "mem.usage.average",
    "mem.vmmemctl.average",
    "net.bytesRx.average",
    "net.bytesTx.average",
    "net.droppedRx.summation",
    "net.droppedTx.summation",
    "net.errorsRx.summation",
    "net.errorsTx.summation",
    "net.usage.average",
    "power.power.average",
    "storageAdapter.numberReadAveraged.average",
    "storageAdapter.numberWriteAveraged.average",
    "storageAdapter.read.average",
    "storageAdapter.write.average",
    "sys.uptime.latest",
  ]

  datacenter_metric_include = [] ## if omitted or empty, all metrics are collected
  datacenter_metric_exclude = [ "*" ] ## Datacenters are not collected by default.

  vsan_metric_include = [] ## if omitted or empty, all metrics are collected
  vsan_metric_exclude = [ "*" ] ## vSAN are not collected by default.

  separator = "_"
  max_query_objects = 256
  max_query_metrics = 256
  collect_concurrency = 1
  discover_concurrency = 1
  object_discovery_interval = "300s"
  timeout = "60s"
  use_int_samples = true
  custom_attribute_include = []
  custom_attribute_exclude = ["*"]
  metric_lookback = 3
  ssl_ca = "/path/to/cafile"
  ssl_cert = "/path/to/certfile"
  ssl_key = "/path/to/keyfile"
  insecure_skip_verify = false
  historical_interval = "5m"
  disconnected_servers_behavior = "error"
  use_system_proxy = true
  http_proxy_url = ""

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

VMware vSphere

1. Dynamic Resource Allocation: Utilize this plugin to monitor resource usage across a fleet of VMs and automatically adjust resource allocations based on performance metrics. This scenario could involve triggering scaling actions in real time based on CPU and memory usage metrics collected from the vSphere API, ensuring optimal performance and cost-efficiency.

2. Capacity Planning and Forecasting: Leverage the historical metrics gathered from vSphere to conduct capacity planning. Analyzing the trends of CPU, memory, and storage usage over time helps administrators anticipate when additional resources will be needed, avoiding outages and ensuring that the virtual infrastructure can handle growth.

3. Automated Alerting and Incident Response: Integrate this plugin with alerting tools to set up automated notifications based on the metrics gathered. For example, if the CPU usage on a host exceeds a specified threshold, it could trigger alerts and automatically initiate predefined remediation steps, such as migrating VMs to less utilized hosts.

4. Performance Benchmarking Across Clusters: Use the metrics collected to compare the performance of clusters in different vCenters. This benchmarking provides insights into which cluster configurations yield the best resource efficiency and can guide future infrastructure enhancements.

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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