Lastly, we will use the terms “virtual machine”, “instance”, and “host” interchangeably. It’s worth mentioning that Google also has a concept of tags, which are used to apply network and firewall settings.
Google refers to this metadata as labels, whereas on some other platforms (including Datadog) the same metadata is known as tags. GCE metrics can generally be broken down into the following three categories:Ī note about terminology: In the metric breakdowns below, we’ll include the relevant metadata that you can use to filter and aggregate your metrics. Other metrics, like memory utilization, are not available at all without using a third-party tool, and some of the standard metrics have nuances and quirks specific to the GCE platform. Most standard system-level metrics, like CPU utilization and network throughput, are available for Google Compute Engine. Key GCE metricsīecause GCE provides the underlying infrastructure to host applications and services, the majority of available metrics are related to low-level resources. GCE powers a large number of high-profile businesses including Philips, Evernote, and HTC. It can be compared to services like Amazon’s Elastic Compute Cloud (EC2), or Azure Virtual Machines. The fully managed service enables users around the world to spin up virtual machines on demand. Google Compute Engine (GCE) is an infrastructure-as-a-service platform that is a core part of the Google Cloud Platform. This article describes in detail the resource and performance metrics that can be obtained from GCE. Part 2 covers the nuts and bolts of collecting GCE metrics, and part 3 describes how you can get started collecting metrics from GCE with Datadog.
This post is part 1 in a 3-part series about monitoring Google Compute Engine (GCE).