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Cloud Automation Applies to RPA

  • Last Updated : 16 Aug, 2021

Cloud computing is the process of running workloads in the clouds – IT environments that abstract, aggregate, and share scalable resources across the network. Neither cloud computing nor clouds are technologies in and of themselves. And this application of automation has not bypassed RPA.

Many companies see and understand the need to implement robotic process automation (RPA). And this is logical since it will allow them to increase the efficiency of their activities, regardless of their global nature, the number of employees, or other determining factors. And if there are fewer questions regarding the development of a process based on RPA technology because often development environments for such processes have visual programming tools, which helps to get used to new technology, then when implementing industrial projects of the corresponding technology, the question arises, how will it be managed? Are there any standardized approaches for implementing an RPA project and a whole pool of organizational issues that are no less important than writing the project code?

In this article, we will give an example of a common platform for automating business decisions – UiPath, the cloud solution of which, Orchestrator, is considered one of the best, and let’s see what offers for cloud automation and RPA process management it has.

Key Features of Orchestrator for Cloud Automation for RPA

  1. Deployment – Ensures that package versions are delivered to designated robots for execution.
  2. Configuration – Maintains and enforces the configuration of robot environments and processes.
  3. Queues – Provides automatic load balancing among robots.
  4. Monitoring – monitors general data about the work of the robot and allows you to evaluate the productivity of the running processes.
  5. Logging – Stores and indexes logs in SQL and Elasticsearch database.

Let’s go through each of them in more detail:

Deployment

The system accepts user-generated processes in the format of assembled NuGet packages, and in turn, the distribution system allocates a designated resource for executing the code from the package (allocates a machine). This happens by binding each package (process) under the execution environment, therefore, due to the specifics of executing the RPA process code, under a specific machine or server.

Configuration

As you know, the specifics of the RPA processes work imply the availability of desktop access for the virtual machine or server on which the automation is planned to run. This means that the authorization data for each machine, as well as its unified identifier, must be kept in memory and this helps to fulfill the part of the Orchestration system, which is just engaged in allocating a special machine key for each individual machine.

Queues

The records of transactions processed by RPA processes are kept in the data structure – “Queues”, which allows, using the FIFO method, taking into account the priority of tasks, to execute queue transactions. The availability of the transaction prioritization functionality is very important when using the process at industrial facilities because this does not allow the process to be more flexible and meet the current needs of users.

Monitoring

One of the main functions of this system is the monitoring module, which allows you to track the productivity of running processes, errors received by the process, and tracklog files. This functionality helps to establish a user experience when working with the system and brings the client and the target process closer together by demonstrating the processing of each transaction separately, as the target specialist would do.

Logging

Orchestrator offers both Cloud and on-premise solutions, i.e. computing power can be applied both locally and from UiPath servers. Elasticsearch is used to index and account for log items and internal storage, which is based on SQL when installed locally.

RPA analytics

Let’s assume that you have implemented RPA. Excellent. But the question arises, how to understand what will be beneficial, how to understand how successful it is, whether the benefits have increased over time or not? RPA analytics will help answer these questions.

RPA process analytics are critical to technology integration as they provide detailed and predictive information about the profitability of ongoing automated processes.

The key quality of the analytical capabilities of RPA is the ability to independently customize the way to determine success and results, the choice of key indicators, customization for the goals and objectives of the company, and the form in which these results are presented is also your choice.

Orchestrator Insights is used to implement these functions.

Insights is an Orchestrator-integrated platform designed to analyze and present data in a custom format. The productivity of using robots licenses, collecting information about the bottlenecks of the automated process – all this is present in this cloud system.

CI / CD applicable for RPA

RPA is increasingly being used to automate processes while saving time and money. However, in terms of simplifying the deployment process and delivering the written process, the Orchestrator system cannot provide a suitable solution. As a result, numerous RPA development companies use common techniques to automate the delivery process.

The CI / CD “conveyor” is an automation of the delivery and integration process of any software for a project. For it to work correctly, the pipeline must be developed before the coding process begins, allowing it to work when coding, testing, and directly injecting it.

What is CI / CD?

Continuous Integration (CI)

Continuous Integration is a software development practice used by developers to make it easier to develop and test code by automating related tasks. Using it in RPA, developers continuously integrate code changes into a central repository, and tests are run on a separate server.

Continuous delivery (CD)

Continuous delivery is a practice that allows easy packaging and continuous deployment of code. It allows you to customize and package software, such as building an RPA bot and deploy it continuously at a lower cost.

Benefits of CI / CD:

  • Faster delivery: shorter turnaround times, resulting in faster time to market
  • Support: Problem detection during the build phase of the bot is much faster, allowing for faster problem resolution and error-free bot deployment.
  • Improvement: End-user involvement in continuous development results in improved usability. New third-party feedback requirements may be met on a daily basis.
  • Updates: Users receive updates on time because rolling releases using CDs is easier and less time-consuming. Release cycles, or sprints, in short, are targeted and tested for bugs before moving on to the next sprint.
  • Monitoring: The progress of the development process can be communicated to the user, allowing real-time monitoring and elimination of pending feedback.
  • Releases: Software deployments are painless and low-risk because the code can be viewed and edited on demand.

Using CI / CD for RPA Process Development

A common CI / CD implementation system for RPA projects using Azure DevOps Pipelines, GIT is a repository (due to its greater acceptance in client security services) and Docker containerization.

For an example of the flow of a standard CI / CD implementation for RPA projects, the following sequence can be given:

  1. The code written in UiPath Studio is committed to the Git repository. UiPath development studio – allows you to natively customize the integration of the commit process into the studio interface, as you could see in the solution from Microsoft – Visual Studio.
  2. A push commit triggers the pipeline in the Azure system, which in turn initiates the CI process.
  3. In a Docker container, the process is launched on a dedicated server for testing the process. Using the built-in code testing capabilities in UiPath, we have the ability to compose unit tests for each part of the process and make an assessment of the correctness of the process to execute them.
  4. Writing the logic of interaction with Pipelines – this container, upon successful completion of the tests, can be delivered to the production server, and merge with the local storage will be performed to update the latest version of the code.

We will talk about the details of the implementation of such a method in the following articles.

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