AWS Right-sizing Recommendation Test
The Rightsizing Recommendations feature in Cost Explorer helps administrators identify cost-saving opportunities by downsizing or terminating instances in Amazon Elastic Compute Cloud (Amazon EC2). These recommendations help cloud administrators understand how to control their cloud spend.
If cloud administrators are notified as soon as a recommendation is generated, and are allowed to instantly view and analyze the recommendations so generated, then they would be able to initiate control actions promptly, and prevent cloud costs from sky-rocketing. This is exactly what the AWS RIght-sizing Recommendation test does!
This test reports the total count of recommendations generated for each Amazon instance that is monitored. Administrators can effortlessly configure the eG Enterprise system to send out an alert every time a new recommendation is generated. This way, administrators can have this test poke them frequently to check out the new recommendations. The test also reveals the percentage of savings that the recommendations, if implemented, may result in. If the test forecasts significant savings, then administrators are incentivized further to take a look at the recommendations and implement them. The eG monitoring console also allows administrators on-demand access to the detailed diagnostics of the test, where they can closely study each recommendation and figure out what action is to be taken for saving costs.
Target of the test: Amazon Cloud
Agent deploying the test: A remote agent
Output of the test: One set of results for each instancename:instanceID available for the configured AWS user account
Parameter | Description |
---|---|
Test Period |
How often should the test be executed. |
Host |
The host for which the test is to be configured. |
Access Type |
eG Enterprise monitors the AWS cloud using AWS API. By default, the eG agent accesses the AWS API using a valid AWS account ID, which is assigned a special role that is specifically created for monitoring purposes. Accordingly, the Access Type parameter is set to Role by default. Furthermore, to enable the eG agent to use this default access approach, you will have to configure the eG tests with a valid AWS Account ID to Monitor and the special AWS Role Name you created for monitoring purposes. Some AWS cloud environments however, may not support the role-based approach. Instead, they may allow cloud API requests only if such requests are signed by a valid Access Key and Secret Key. When monitoring such a cloud environment therefore, you should change the Access Type to Secret. Then, you should configure the eG tests with a valid AWS Access Key and AWS Secret Key. Note that the Secret option may not be ideal when monitoring high-security cloud environments. This is because, such environments may issue a security mandate, which would require administrators to change the Access Key and Secret Key, often. Because of the dynamicity of the key-based approach, Amazon recommends the Role-based approach for accessing the AWS API. |
AWS Account ID to Monitor |
This parameter appears only when the Access Type parameter is set to Role. Specify the AWS Account ID that the eG agent should use for connecting and making requests to the AWS API. To determine your AWS Account ID, follow the steps below:
|
AWS Role Name |
This parameter appears when the Access Type parameter is set to Role. Specify the name of the role that you have specifically created on the AWS cloud for monitoring purposes. The eG agent uses this role and the configured Account ID to connect to the AWS Cloud and pull the required metrics. To know how to create such a role, refer to Creating a New Role. |
AWS Access Key, AWS Secret Key, Confirm AWS Access Key, Confirm AWS Secret Key |
These parameters appear only when the Access Type parameter is set to Secret.To monitor an Amazon cloud instance using the Secret approach, the eG agent has to be configured with the access key and secret key of a user with a valid AWS account. For this purpose, we recommend that you create a special user on the AWS cloud, obtain the access and secret keys of this user, and configure this test with these keys. The procedure for this has been detailed in the Obtaining an Access key and Secret key topic. Make sure you reconfirm the access and secret keys you provide here by retyping it in the corresponding Confirm text boxes. |
Proxy Host and Proxy Port |
In some environments, all communication with the AWS cloud and its regions could be routed through a proxy server. In such environments, you should make sure that the eG agent connects to the cloud via the proxy server and collects metrics. To enable metrics collection via a proxy, specify the IP address of the proxy server and the port at which the server listens against the Proxy Host and Proxy Port parameters. By default, these parameters are set to none , indicating that the eG agent is not configured to communicate via a proxy, by default. |
Proxy User Name, Proxy Password, and Confirm Password |
If the proxy server requires authentication, then, specify a valid proxy user name and password in the proxy user name and proxy password parameters, respectively. Then, confirm the password by retyping it in the CONFIRM PASSWORD text box. By default, these parameters are set to none, indicating that the proxy sever does not require authentication by default. |
Proxy Domain and Proxy Workstation |
If a Windows NTLM proxy is to be configured for use, then additionally, you will have to configure the Windows domain name and the Windows workstation name required for the same against the proxy domain and proxy workstation parameters. If the environment does not support a Windows NTLM proxy, set these parameters to none. |
Exclude Region |
Here, you can provide a comma-separated list of region names or patterns of region names that you do not want to monitor. For instance, to exclude regions with names that contain 'east' and 'west' from |
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:
|
Measurement |
Description |
Measurement Unit |
Interpretation |
---|---|---|---|
Total recommendations |
Indicates the total number of rightsizing recommendations that are available based on the resources and usage. |
Number |
Recommendations use a machine learning engine to identify the optimal Amazon EC2 instance types for a particular workload. Instance types include those that are a part of AWS Auto Scaling groups. The recommendations engine analyzes the configuration and resource usage of a workload to identify dozens of defining characteristics. For example, it can determine whether a workload is CPU-intensive or whether it exhibits a daily pattern. The recommendations engine analyzes these characteristics and identifies the hardware resources that the workload requires. Finally, the engine concludes how the workload would perform on various Amazon EC2 instances to make recommendations for the optimal AWS compute resources that the specific workload would consume. You can use the detailed diagnosis of this measure to view, analyze, and take action on the recommendations. |
Estimated total monthly savings amount |
Indicates the sum of the projected monthly savings associated with each of the recommendations provided. |
USD |
The instance running in the last 14 days to is examined to identify whether it was partially or fully covered by an RI or Savings Plans, or running On-Demand. Another factor is whether the RI is size-flexible. The cost to run the instance is calculated based on the On-Demand hours and the rate of the instance type. For each recommendation, the cost to operate a new instance is calculated. It is assumed that a size-flexible RI covers the new instance in the same way as the previous instance if the new instance is within the same instance family. Estimated savings are calculated based on the number of On-Demand running hours and the difference in On-Demand rates. If the RI is not size-flexible, or if the new instance is in a different instance family, the estimated savings calculation is based on whether the new instance had been running during the last 14 days as On-Demand. |
Savings percentage |
Indicates the percentage of available savings relative to the direct instance costs (On-Demand) associated with the instances in the recommendation list. |
Percent |
Ideally, the value of this measure should be high. |