An Amazon Machine Image (AMI) provides the information required to launch an instance, which is a virtual server in the cloud. You specify an AMI when you launch an instance, and you can launch as many instances from the AMI as you need. You can also launch instances from as many different AMIs as you need.
The sole purpose of this tutorial is to customize an existing AMI and to understand the procedure involved in it.
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AWS OpsWorks supports two ways to customize instances: custom Amazon Machine Images (AMIs) and Chef recipes. Both approaches give you control over which packages and package versions are installed, how they are configured, and so on. However, each approach has different advantages, so the best one depends on your requirements.
The following are the primary reasons to consider when using a custom AMI:
Now that we have customized our Amazon EC2 instance using the Amazon Machine Image (AMI), we can save and launch future environments with this configuration. AMI is specified while launching an instance and as many instances as you need can be launched.
To create an AMI from a running Amazon EC2 instance
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Amazon EC2 terminates the instance, takes images of any volumes that were attached, creates and registers the AMI, and then relaunches the instance.
It takes a few minutes for the whole process to finish and for AMI to be created. Once it is finished, it appears in AMIs view in AWS Explorer.
Eventually, you’ll probably want to have multiple Amazon EC2 instances running across multiple Availability Zones. If one Availability Zone becomes unavailable, the traffic will be rerouted to another Availability Zone. An Elastic Load Balancer will enhance the availability of your application, whether all of your instances are in the same Availability Zone or in multiple Availability Zones. To create an Elastic Load Balancer, move on to further post Create an Elastic Load Balancer.
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Prasanthi is an expert writer in MongoDB, and has written for various reputable online and print publications. At present, she is working for Mindmajix, and writes content not only on MongoDB, but also on Sharepoint, Uipath, and AWS.