# Compute Optimizer Dashboard
Compute Optimizer Dashboard

## Introduction


AWS Compute Optimizer recommends optimal AWS resources for your workloads to reduce costs and improve performance by using machine learning to analyze historical utilization metrics. Compute Optimizer Dashboard lets you view cost optimization and risk reduction opportunities for all accounts in your AWS Organizations across all AWS Regions. Out-of-the-box benefits of the COD include (but are not limited to):
+ Find over and underutilized resources (EC2, AutoScaling Groups, EBS, Lambda).
+ Get right-sizing recommendations.
+ Identify potential savings across all payer accounts and regions.
+ Track optimization progress over time by AWS Account team or business unit.

## Architecture


![\[architecture\]](http://docs.aws.amazon.com/guidance/latest/cloud-intelligence-dashboards/images/images/architecture/compute-optimizer-architecture.png)


1. AWS Compute Optimizer Collects the data about running instances and uses Machine Learning to generate recommendations

1. CID Data Collection has a Lambda functions that on schedule assume a role in Management account(s) to trigger an export of AWS Compute Optimizer in each region configured in data collection stack. By default it i scheduled for every 14 days, but it can be changed in the parameters of the Data Collection Stack.

1. The AWS Compute Optimizer exports data to regional buckets.

1. The replication mechanisms consolidates all data from regional buckets to one data bucket. All exports on regional bucket will be deleted after 1 day as per lifecycle policy.

1. Quick Sight Dataset refreshes daily to show the latest state on the dashboard.

## Learn more

+  [3 New Features on the Compute Optimizer Dashboard](https://www.youtube.com/watch?v=IP_k2OXHPoo) 

See also:
+ A basics of Compute Optimizer Right Sizing in a [lab 200 Rightsizing with Compute Optimizer](https://wellarchitectedlabs.com/cost/200_labs/200_aws_resource_optimization/) 
+  [AWS Compute Optimizer FAQ](https://aws.amazon.com/compute-optimizer/faqs/) 

## Demo Dashboard


Get more familiar with Dashboard using the live, interactive demo dashboard following this [link](https://cid.workshops.aws.dev/demo?dashboard=compute-optimizer-dashboard) 

![\[Image of a compute optimizer dashboard in Quick Sight\]](http://docs.aws.amazon.com/guidance/latest/cloud-intelligence-dashboards/images/co_demo.png)


## Prerequisites


1. To get right sizing recommendations you need to [Enroll all accounts to Compute Optimizer](https://docs.aws.amazon.com/compute-optimizer/latest/ug/getting-started.html#account-opt-in). You can use free version that provides recommendations based on 14 days of look-back period.

1. Deploy or update [Data Collection Lab](data-collection.md) and make sure AWS Compute Optimizer Data Collection Module is enabled.

1. Ensure you have [Compute Optimizer enabled at Organization level](https://docs.aws.amazon.com/organizations/latest/userguide/services-that-can-integrate-compute-optimizer.html).

## Deployment


**Example**  
If you already have CUDOS, Cost Intelligence Dashboard or KPI Dashboard installed via CloudFormation as described [here](deployment-in-global-regions.md), you can update the Stack by setting DeployComputeOptimizerDashboard to "yes" and updating the path of Data Collection S3 bucket (if different from default).  
If you do not have the stack installed, you can install using the instructions [here](deployment-in-global-regions.md) (you can ignore the Cost and Usage report part as it is not required for this dashboard).
Alternative method to install dashboards is the [cid-cmd](https://github.com/aws-solutions-library-samples/cloud-intelligence-dashboards-framework/blob/main/CID-CMD.md#command-line-tool-cid-cmd) tool.  

1. Log in to to your **Data Collection** Account.

1. Open up a command-line interface with permissions to run API requests in your AWS account. We recommend to use [CloudShell](https://console.aws.amazon.com/cloudshell).

1. In your command-line interface run the following command to download and install the CID CLI tool:

   ```
   pip3 install --upgrade cid-cmd
   ```

   If using [CloudShell](https://console.aws.amazon.com/cloudshell), use the following instead:

   ```
   sudo yum install python3.11-pip -y
   python3.11 -m pip install -U cid-cmd
   ```

1. In your command-line interface run the following command to deploy the dashboard:

   ```
   cid-cmd deploy --dashboard-id compute-optimizer-dashboard
   ```

   Please follow the instructions from the deployment wizard. More info about command line options are in the [Readme](https://github.com/aws-solutions-library-samples/cloud-intelligence-dashboards-framework/blob/main/CID-CMD.md#command-line-tool-cid-cmd) or `cid-cmd --help`.

1. You can provide also additional tag names. This dashboard supports 2 tags: Primary and Secondary. Tags are the "key" part of the Resource Tag. Please note that Tags here are case sensitive and they not AWS Cost Allocation Tags.

   Recommendation: You can use one Tag to define ownership of resource and another tag to define if this particular resource is eligible for RightSizing (default) or must be excluded for valid reason (DRP, Vendor Compliance, Test, or any other reason for resource to be over-provisioned).If your are not using tags, leave defaults.

   Tags that are using a dash as separator (`-`) are not supported by default. If you have this kind of tags you might need additional customization of a dataset compute\$1optimizer\$1all\$1options. For example in `primary_tag` field you can use `parseJson(replace(tags, '-', '_'),'$.My_Tag')` 

   You can also define these tags later and apply to dashboard using `cid-cmd update --force --recursive` 

## Update


Please note that dashboards are not updated with update of CloudFormation Stack. When new version of the dashboard template is released, you can update your dashboard by running the following command in your command-line interface:

```
cid-cmd update --dashboard-id compute-optimizer-dashboard
```

## OPTIONAL STEPS


 **Manage Business Units Map** 

For managing Business Units please modify business\$1units\$1map view. You can update view definition providing your values, or you can create an csv file upload to s3, create a table and set business\$1units\$1map view to select from this table.

```
CREATE OR REPLACE VIEW business_units_map AS
SELECT *
FROM
    (
    VALUES
        ROW ('`111111111`', '`account1`', '`Business Unit 1`')
        , ROW ('`222222222`','`account2`', '`Business Unit 2`')
    ) ignored_table_name (account_id, account_name, bu)
```

Also you can use business\$1units\$1map view as a proxy to other data sources.

In case if you do not need Business Units functionality and you have CUDOS dashboard installed with account\$1map, you can use this view to SELECT from account\$1map.

```
CREATE OR REPLACE VIEW business_units_map AS
SELECT
    account_id as account_id,
    account_name as account_name,
    '`Undefined`' as bu
FROM account_map
```

## Authors

+ Iakov Gan, Ex-Amazonian
+ Yuriy Prykhodko, Principal Technical Account Manager
+ Voicu Chirtes, Senior Technical Account Manager
+ Timur Tulyaganov, Ex-Amazonian

## Feedback & Support


Follow [Feedback & Support](feedback-support.md) guide

**Note**  
These dashboards and their content: (a) are for informational purposes only, (b) represent current AWS product offerings and practices, which are subject to change without notice, and (c) does not create any commitments or assurances from AWS and its affiliates, suppliers or licensors. AWS content, products or services are provided "as is" without warranties, representations, or conditions of any kind, whether express or implied. The responsibilities and liabilities of AWS to its customers are controlled by AWS agreements, and this document is not part of, nor does it modify, any agreement between AWS and its customers.