AWS Resource Usage Test

Each AWS account has default quotas, formerly referred to as limit. Quotas are the maximum values for the resources, actions, and items in your AWS account. In the context of quotas, a Resource refers to an API operation that is called in an AWS account. Unless otherwise noted, each quota is Region-specific. You can request increases for some quotas, but not all quotas can be increased.

The need to increase a quota arises only if the corresponding resource is consistently over-used - i.e., the corresponding API operation is frequently called - thereby rendering its current quota setting insufficient. A resource can no longer to used/accessed once the limit defined by its quota is reached. To ensure that critical API operations are performed without any interruptions, it is important to track the usage of resource quotas over time, check if business needs are met by the quota settings, and if not, request for an increase in the quota. This is where the AWS Resource Usage test helps!

This test reports the number of resources used - i.e., the number of times API operations were called - by each AWS region. Detailed diagnostics, if enabled, reveal the number of times each resource / API operation was called. By comparing these insights with the quota settings per resource, you can quickly identify resources for which the quota setting may soon have to be increased to ensure their continued usage.

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

Configurable parameters for the test
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: Login to the AWS management console. with your credentials. Click on your IAM user/role on the top right corner of the AWS Console. You will see a drop-down menu containing the Account ID (see Figure 1). Figure 1 : Identifying the AWS Account ID
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 instance, 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 monitoring, your specification should be: east,west
DD For Call Count	As part of detailed diagnostics, the test reports the number of times each resource/API operation is called in a specific AWS region. Typically, in an AWS cloud environment, numerous API operations will be performed in a given time window. As a result, whenever detailed metrics are collected, thousands of resource records will be pulled and stored in the eG backend. Where the eG database is not well-sized or tuned, these records may choke the database and cause it to slow down. To avoid this, the test does not collect detailed metrics by default. Accordingly, the DD For Call Count flag is set to No by default. Set this flag to Yes only if your eG database is adequately sized.
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.

Measures reported by the test:
Measurement	Description	Measurement Unit	Interpretation
Resource call count	Indicates the number of resources that were used - i.e., the number of times API operations were called - in this AWS region.	Number	If the DD For Call Count flag is set to Yes, then detailed diagnostics will be reported for this measure. In such a case, you can use the detailed metrics to identify the API operation that is called frequently. You can compare the call count of each resource in the detailed diagnosis with the quota setting of that resource in AWS to proactively detect any potential inadequacies.