Podman Images Performance Test

A Podman Container Engine can have multiple images using which containers are created. In large environments where the requirements of users vary from minute to minute, it is important for an administrator to cater to their specific needs by providing the images of their choice so that the users can create the containers and load applications accordingly. Often administrators may be confused while identifying the needs of the users. An image that is needed today may not be needed tomorrow to create a container. In such cases, the images may be unutilized but still hold on valuable disk space. To optimize the disk space and eliminate unused images, administrators are required to analyze the performance of the images as well as identify the images that are most commonly used by the users. The Podman Images Performance test helps administrators in this regard!

This test monitors the images of the Podman Container Engine and reports the number of containers created using each image. By analyzing the number of running containers created from each image and the resource utilization of those containers, administrators can figure out the image that is widely used by the users to create the containers and analyze how well the resources are utilized by those containers.

Target of the test : A Podman Container Engine

Agent deploying the test : An internal agent

Outputs of the test : One set of results for each image available on the target Podman Container Engine that is being monitored.

Configurable parameters for the test
Parameter	Description
Test Period	How often should the test be executed.
Host	The IP address of the host for which this test is to be configured.
Port	The port number at which the specified HOST listens. The default is 2375.
Use SUDO	By default, this flag is set to No. This indicates that, by default, this test will report the metrics of this test by executing the crictl command. However, in some highly secure environments, the eG agent install user may not have the permissions to execute this command directly. In such cases, do the following: Edit the SUDOERS file on the target host and append an entry of the following format to it: <eG_agent_install_user> ALL=(ALL) NOPASSWD:<Command> For instance, if the eG agent install user is eguser, then the entries in the sudoers file should be: eguser ALL=(ALL) NOPASSWD: crictl Finally, save the file. Then, when configuring the test using the eG admin interface, set this parameter to Yes. This will enable the eG agent to execute the sudo crictl command and retrieve the relevant metrics for this test.
Podman User and Podman Password	Specify the credentials of the user who is authorized to access remote REST API and invoke its methods for metrics collection.
Webport	By default, the remote REST API is enabled on port 2375. This implies that by default, this test connects to port 2375 to access the remote REST API and make API calls for metrics collection. In some environments however, the remote REST API can be enabled on a different port. Make sure you configure this parameter with the exact port on which the remote REST API has been enabled.
Detailed Diagnosis	To make diagnosis more efficient and accurate, the eG Enterprise embeds an optional detailed diagnostic capability. With this capability, the eG agents can be configured to run detailed, more elaborate tests as and when specific problems are detected. To enable the detailed diagnosis capability of this test for a particular server, choose the On option. To disable the capability, click on the Off option. The option to selectively enable/disable the detailed diagnosis capability will be available only if the following conditions are fulfilled: The eG manager license should allow the detailed diagnosis capability Both the normal and abnormal frequencies configured for the detailed diagnosis measures should not be 0.

Measurements made by the test
Measurement	Description	Measurement Unit	Interpretation
Total containers	Indicates the total number of containers that were mapped to this image.	Number	The detailed diagnosis of this measure lists the name of the containers. Compare the value of this measure across the images to identify the image to which maximum number of containers are mapped.
Running containers	Indicates the number of containers that are created from this image and are currently in Running state.	Number	Ideally, the value of this measure should be high. A low value for this measure is a cause of concern. The detailed diagnosis of this measure lists the containers that are currently in Running state and the resource utilization of the containers.
CPU utilization of containers	Indicates the percentage of CPU utilized by the containers (created from this image) that are currently in Running state.	Percent	A sudden increase or decrease in the value of this measure could be a cause of concern.
Memory used by containers	Indicates the amount of memory utilized by the containers (created from this image) that are currently in Running state.	MB	A low value is desired for this measure. A high value of this measure indicates that the container is running out of space.
Memory utilization of containers	Indicates the percentage of memory utilized by the containers (created from this image) that are currently in Running state.	Percent	A value close to 100% indicates that the memory is running out of space.
Disk space used by image	Indicates the amount of disk space utilized by this image.	MB	Ideally, the value of this measure should be low. A significant increase in the value of this measure could be a cause of concern.