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To install htop do the following on Ubuntu:
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$ sudo apt-get install htop |
And the following on CentOS:
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$ sudo yum install htop |
Monitoring Memory Usage
Memory usage is a little more complicated than CPU usage. This is because memory usage can come in many different forms: free, cached, buffered, and in-use.
This article describes a few different tools you can use to see details about your instance's memory usage. In addition, we also recommend the site Linux Ate My RAM! as it explains in detail why all of your memory is not in use even though some tools might report it as such.
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Depending on the application you're using, you might be able to tune it so it consumes a smaller amount of resources. For example, Java-applications are notorious for consuming large amounts of memory, however, this is only if the Java-based application has not been configured with a memory limit.
This article has details on how you can configure your Java application to use a specified amount of memory.
If you're using a web-based application which is based on Apache, you can also configure it to use a lower amount of memory. See this article for more details.
Use Smaller Flavors
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If your use-cases requires multiple instances, you should try using the smallest possible flavor before moving up to a larger flavor. For example, given a scenario where you wanted to develop a web application, you could first try installing everything on a single m1.small instance. If you find your application is unable to effectively run in this flavor, you can do one of two things:
- Create a larger instance.
- Create another m1.small flavor instance and move a portion of the web application to that new instance. For example, move the database
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- service to a dedicated instance.
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We highly recommend using a DevOps methodology when using the Rapid Access Cloud. This includes using tools such as Terraform and Ansible. These tools will allow you to codify your configurations and provide the ability to automatically rebuild your deployments. |
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It's possible that the application reading your dataset can efficiently read it without exhausting all available memory. For example, if your dataset is 32 gigabytes in size but your instance only has 16 8 gigabytes of memory, the application might recognize this and automatically read only a small portion of data at a time.
However, if your application doesn't do this, we recommend breaking your dataset up into smaller pieces, sized appropriately for the amount of memory in available to your instance.
If your dataset cannot be broken up into smaller pieces, a final one other option is to add more swap space to your instance. Instructions on how to do this can be found here.
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Containers can be thought of as a form of virtualization. You can install a container management service on your instance which will allow you to run "contained" applications or even full Linux distributions within it. Containers can be very resource - efficient since all containers share the same underlying kernel (as opposed to instances which are discrete, isolated virtual machines all having a different operating system kernel).
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If you've read everything above and still find that your use-case requires access to more resources, please contact us. We are more than happy to provide users with a temporary quota increase to help resolve any difficulties.
We do request you let us know how much of each resource you require and for how long you wish to have the additional resources for.