EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login
Home Data Science Data Science Tutorials Machine Learning Tutorial Automation Anywhere Architecture
Secondary Sidebar
Machine Learning Tutorial
  • Classification
    • Kernel Methods in Machine Learning
    • Clustering in Machine Learning
    • Machine Learning Architecture
    • Automation Anywhere Architecture
    • Machine Learning C++ Library
    • Machine Learning Frameworks
    • Data Preprocessing in Machine Learning
    • Data Science Machine Learning
    • Classification of Neural Network
    • Neural Network Machine Learning
    • What is Convolutional Neural Network?
    • Single Layer Neural Network
    • Kernel Methods
    • Forward and Backward Chaining
    • Forward Chaining
    • Backward Chaining
  • Basic
    • Introduction To Machine Learning
    • What is Machine Learning?
    • Uses of Machine Learning
    • Applications of Machine Learning
    • Naive Bayes in Machine Learning
    • Dataset Labelling
    • DataSet Example
    • Deep Learning Techniques
    • Dataset ZFS
    • Careers in Machine Learning
    • What is Machine Cycle?
    • Machine Learning Feature
    • Machine Learning Programming Languages
    • What is Kernel in Machine Learning
    • Machine Learning Tools
    • Machine Learning Models
    • Machine Learning Platform
    • Machine Learning Libraries
    • Machine Learning Life Cycle
    • Machine Learning System
    • Machine Learning Datasets
    • Machine Learning Certifications
    • Machine Learning Python vs R
    • Optimization for Machine Learning
    • Types of Machine Learning
    • Machine Learning Methods
    • Machine Learning Software
    • Machine Learning Techniques
    • Machine Learning Feature Selection
    • Ensemble Methods in Machine Learning
    • Support Vector Machine in Machine Learning
    • Decision Making Techniques
    • Restricted Boltzmann Machine
    • Regularization Machine Learning
    • What is Regression?
    • What is Linear Regression?
    • Dataset for Linear Regression
    • Decision tree limitations
    • What is Decision Tree?
    • What is Random Forest
  • Algorithms
    • Machine Learning Algorithms
    • Apriori Algorithm in Machine Learning
    • Types of Machine Learning Algorithms
    • Bayes Theorem
    • AdaBoost Algorithm
    • Classification Algorithms
    • Clustering Algorithm
    • Gradient Boosting Algorithm
    • Mean Shift Algorithm
    • Hierarchical Clustering Algorithm
    • Hierarchical Clustering Agglomerative
    • What is a Greedy Algorithm?
    • What is Genetic Algorithm?
    • Random Forest Algorithm
    • Nearest Neighbors Algorithm
    • Weak Law of Large Numbers
    • Ray Tracing Algorithm
    • SVM Algorithm
    • Naive Bayes Algorithm
    • Neural Network Algorithms
    • Boosting Algorithm
    • XGBoost Algorithm
    • Pattern Searching
    • Loss Functions in Machine Learning
    • Decision Tree in Machine Learning
    • Hyperparameter Machine Learning
    • Unsupervised Machine Learning
    • K- Means Clustering Algorithm
    • KNN Algorithm
    • Monty Hall Problem
  • Supervised
    • What is Supervised Learning
    • Supervised Machine Learning
    • Supervised Machine Learning Algorithms
    • Perceptron Learning Algorithm
    • Simple Linear Regression
    • Polynomial Regression
    • Multivariate Regression
    • Regression in Machine Learning
    • Hierarchical Clustering Analysis
    • Linear Regression Analysis
    • Support Vector Regression
    • Multiple Linear Regression
    • Linear Algebra in Machine Learning
    • Statistics for Machine Learning
    • What is Regression Analysis?
    • Clustering Methods
    • Backward Elimination
    • Ensemble Techniques
    • Bagging and Boosting
    • Linear Regression Modeling
    • What is Reinforcement Learning
  • Deep Learning
    • What Is Deep learning
    • Overviews Deep Learning
    • Application of Deep Learning
    • Careers in Deep Learnings
    • Deep Learning Frameworks
    • Deep Learning Model
    • Deep Learning Algorithms
    • Deep Learning Technique
    • Deep Learning Networks
    • Deep Learning Libraries
    • Deep Learning Toolbox
    • Types of Neural Networks
    • Convolutional Neural Networks
    • Create Decision Tree
    • Deep Learning for NLP
    • Caffe Deep Learning
    • Deep Learning with TensorFlow
  • RPA
    • What is RPA
    • What is Robotics?
    • Benefits of RPA
    • RPA Applications
    • Types of Robots
    • RPA Tools
    • Line Follower Robot
    • What is Blue Prism?
    • RPA vs BPM
  • Interview Questions
    • Deep Learning Interview Questions And Answer
    • Machine Learning Cheat Sheet

Related Courses

Machine Learning Training

Deep Learning Training

Artificial Intelligence Training

Automation Anywhere Architecture

Automation Anywhere Architecture

Introduction to Automation Anywhere Architecture

Automation anywhere architecture is defined as a collection of processes or rules that portrays the implementation of the systems of the automation anywhere tool. Automation anywhere is functionality along with a Robotic Process Automation tool that deals with easy to build and highly scalable software that enables mimic of human actions and based on the metaphorical software roots able to perform varied defined actions at a much faster rate and without much or very less errors. There are various RPA tools that enable automation of tasks as automation anywhere does, but this software particularly imparts better performance with flexibility to integrate with various platforms & scale at the same time.

Architecture of Automation Anywhere

Before we even start off with the architecture, we need to know the principle of architecture in automation anywhere is that of distributed architecture. In this type of architecture, there are different components which work synchronously and communicates over a network to attain the objective or goal.

This architecture has the following properties, and these are equally important for Automation Anywhere to inherit so that it can work according to the requirement and they are:

  • The processing of information is not restricted to a single machine and is rather distributed over other computers on the network.
  • The type of interaction that happens between the elements is of the type of client-server architecture and this client-server concept is the baseline to the formation of a multi-layer architecture.
  • Variety of technology architectures are present that supports the distributed architecture and some of them are .NET, AXIS Java Web services, J2EE etc.
  • The concept of middleware to provide a buffer between the application and network is the USP of the distributed architecture. This type of computer software is present in the middle of the system and enables management of the different elements in the distributed system and especially of that of the automation anywhere.

Due to its transparency, reliability and availability distributed architecture becomes the baseline principle for a lot of derived architecture.

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Keeping the above-listed characteristics, we finally come to the architecture of automation anywhere tool which is described by the below diagram.

Automation Anywhere Architecture

In the above diagram, we see that there are 3 elements that constitutes the automation anywhere architecture.

All in One Data Science Bundle(360+ Courses, 50+ projects)
Python TutorialMachine LearningAWSArtificial Intelligence
TableauR ProgrammingPowerBIDeep Learning
Price
View Courses
360+ Online Courses | 50+ projects | 1500+ Hours | Verifiable Certificates | Lifetime Access
4.7 (86,527 ratings)

Now, let’s see each of the elements and see their roles in making the architecture into a distributed architecture standard.

1. Bot Creators

As a part of the development process, the automation in the automation anywhere tool is performed by a Bot, and this desktop application is present that is responsible for creating, editing and executing of the bots. This element is licensed for each of the bots and the system includes a run time for execution and testing of the bot. The desktop-based application has the authority of uploading and downloading bots.

Once the bots are configured, respective developers can access their corresponding bots and create their individual tasks and execute all of them at once as well. In terms of the distributed architecture, the bot creator acts as a client and hence needs to be connected to an active control room about which we will be focusing on in the next point.

2. Control Room

This element happens to be the most important component in the architecture. This happens to be the server in the terminology of a distributed architecture. The bots that are created by the bot creator is controlled by the control room and this element provides various features like centralized user management, bot farm, license management, dashboard reflecting the values for which the tool runs for and source control.

Talking specifically about licenses, this tool provides 2 types of licenses as a part of the accessor to segregate who can access or who can edit. Dev license is given to users who needs to create or develop and run a bot, whereas run license is given to the users who only need to run the bots and not necessarily create them. Control room schedules and assigns bots for the execution of the tasks.

3. Bot Runners

Till now we have created a bot and also set up the room from there the execution will be handled, but in order to complete we would need this point with as much importance as that of the previous 2. The last element of the automation anywhere tool is to accomplish the task of running a bot. Here, these element helps in running the bots and if required they can be run parallelly as well. There is no chance that this element can create or update a bot.

This element is a run time client that is installed in the windows system that executes the bot that is created by the Bot creator and commanded by the control room and then reports backs the log of the execution along with the status back to the control room for audit purposes. The only requirement in terms of Bot Runners is that it needs to be registered and authenticated along with being identifiable by the control room in order to run the job which would provide the desired output. Another interesting feature of bot runner is its possibility of grouping and then dynamically allocated which gives a convenient opportunity to the users to scale up the system.

Conclusion

With this, we come to the conclusion and understanding the different elements that constitute the automation anywhere tool. We also gathered the information that architecture that governs the automation anywhere tool is that of the distributed architecture and different elements we went through explains the reason for the same.

Recommended Articles

This is a guide to Automation Anywhere Architecture. Here we discuss the introduction and architecture of automation anywhere. You may also have a look at the following articles to learn more –

  1. Neural Network Machine Learning
  2. Statistics for Machine Learning
  3. Machine Learning System
  4. Regularization Machine Learning
Popular Course in this category
Machine Learning Training (20 Courses, 29+ Projects)
  19 Online Courses |  29 Hands-on Projects |  178+ Hours |  Verifiable Certificate of Completion
4.7
Price

View Course

Related Courses

Deep Learning Training (18 Courses, 24+ Projects)4.9
Artificial Intelligence AI Training (5 Courses, 2 Project)4.8
0 Shares
Share
Tweet
Share
Primary Sidebar
Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

ISO 10004:2018 & ISO 9001:2015 Certified

© 2022 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

SPSS, Data visualization with Python, Matplotlib Library, Seaborn Package

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more