Automation Suite
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Automation Suite Installation Guide
Last updated Mar 25, 2024

Evaluating Your Storage Needs

An Automation Suite cluster uses the data disks attached to its server nodes as storage resources available to all the products enabled on your cluster. Each product uses these resources differently.

To understand your storage needs and plan for them accordingly, refer to the following terminology and guidelines.

Terminology

  • Server node disk size – The size of all individual disks attached to each server node.

    • All servers must have the same number of disks attached.
    • Disks on each server may have different sizes as long as the sum of all the disk sizes is identical on all servers.
  • Total cluster disk size – Server node disk size multiplied by the number of server nodes.
  • Application available storage – The amount of storage available for applications to consume.

    • Application available storage is lower than the total cluster disk size due to the way fault resiliency and high availability are implemented in the Automation Suite cluster.

The following table describes the multi-node HA-ready hardware requirements for the Basic and Complete profiles in the context of the previously introduced terms.

Preset hardware configuration

Number of server nodes

Server node disk size

Total cluster disk size

Application available storage (online)

Application available storage (offline)

3

512 GiB

1.5 TiB

41 GiB

37 GiB

3

2 TiB

6 TiB

291 GiB

286 GiB

Important:

To leverage the 291 GiB available storage, you must resize the PVC value to 291 GiB instead of the preconfigured 100 GiB value. Otherwise, your applications will not be able to take advantage of more than 100 GiB.

For instructions, see Resizing PVC.

Estimating the storage used by your applications

As you enable and use products on the cluster, they consume some storage from the application available storage. Products usually have a small enablement footprint as well as some usage-dependent footprint that varies depending on the use case, scale of use, and project. The storage consumption is evenly distributed across all the storage resources (data disks), and you can monitor the levels of storage utilization using the Automation Suite monitoring stack.

How to Monitor Available Storage

The Automation Suite cluster uses an internal Kubernetes concept called Persistent Volumes as an internal abstraction that represent disks across all the nodes on the cluster.

To avoid instabilities, it is recommended to set up monitoring and alerts to constantly check if the free space on the Persistent Volumes drops below the application available storage value. For more details, see Monitoring Persistent Volumes.

If an alert triggers, you can mitigate it by increasing the storage capacity of your cluster as described in the following section.

How to Increase Storage Capacity

If your evaluated needs do not meet the recommended hardware requirements, you can add more storage capacity using either one or both of the following methods:

  1. Add more server nodes with disks. For instructions, see Adding a new node to the cluster.
  2. Important: For each 60 GiB of product-specific storage needed, your Automation Suite cluster will need an additional 1 TiB of storage added to the total storage available on your cluster, distributed equally across your server nodes.

How to Calculate Your Usage Needs

You can estimate your storage consumption using the product-specific metric in the following tables. These tables describe how much content you can place on your cluster out of the box. For reference, they include the storage footprint of a typical usage scenario of each product.

Basic product selection

Product

Storage-driving metric

Storage per metric

Typical use case

Orchestrator

  • Size of the automation packages for deployed automations
  • Size of the storage buckets of deployed automation
  • MiB per package
  • MiB per bucket

Typically, a package is 5 MiB, and buckets, if any, are less than 1 MiB. A mature enterprise has 5 GiB of packages and 6 GiB of buckets deployed.

Action Center

  • Number of documents stored by customer in document tasks
  • Number of tasks created
  • GiB per document in document tasks
  • Number of tasks

Typically, a document takes 0.15 MiB, and the forms to fill take an additional 0.15 KiB. In a mature enterprise this can roll up to 4GiB in total.

Test Manager

  • Number of attachments and screenshots stored by users
  • MiB of attachments and screenshots

Typically, all files and attachments add up to approximately 5 GiB.

Insights

  • Enablement footprint and the number of dashboards published
  • GiB per dashboard

2 GiB are required for enablement, with the storage footprint growing with the number. A well-established enterprise-scale deployment requires another few GiB for all the dashboards.

Automation Hub

N/A

N/A

2 GiB fixed footprint

Automation Ops

N/A

N/A

No storage footprint

Complete product selection

Product

Storage-driving metric

Storage per metric

Typical use case

Apps

  • Number of apps deployed and enablement footprint
  • Number of apps, size of apps, size of database supporting apps

Typically, the database takes approximately 5 GiB, and a typical complex app consumes approximately 15 MiB.

AI Center

  • Number of uploaded ML packages
  • Number of datasets for analysis
  • Number of published pipelines
  • GiB per package
  • GiB per dataset
  • Number of pipelines

A typical and established installation will consume 8 GiB for 5 packages and an additional 1GiB for the datasets.

A pipeline may consume an additional 50 GiB, but only when actively running.

Document Understanding

  • Size of ML model
  • Size of OCR model
  • Number of stored documents
  • GiB per ML model
  • GiB per OCR model
  • Number of documents stored

In a mature deployment, 12GiB will go to ML model, 17GiB to the OCR, and 50GiB to all documents stored.

Task Mining

  • Hours of user activity analyzed to suggest automation patterns
  • GiB per hour

Typically, about 200GiB of activity log data should be analyzed to suggest meaningful automations. Highly repetitive tasks however, may require much less data.

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