Dynamic RPA Robot Provisioning for Cloud Job Queue Scaling
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Solution Overview
Problem
Performing robotic process automation (RPA) in cloud computing environments poses challenges due to the need for expertise in RPA tools, cloud management, and virtualization, making it difficult for users without advanced programming skills to facilitate the programming and deployment of software robots.
Innovation Solution
A method employing a computer system's hardware processor to determine provisioning conditions and automatically provision virtual machines on a selected RPA host platform, connecting RPA robots to an orchestrator for job assignment, and terminating VMs when necessary, thereby simplifying RPA activities and extending their reach to users with limited technical expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If RPA activities are performed in cloud computing environments, then RPA reach and accessibility are improved, but the complexity of programming and deployment increases beyond average RPA developer competence
Solution Approach 1:
An automated provisioning system acts as an intermediary between RPA developers and cloud infrastructure. The system automatically provisions virtual machines, configures RPA software environments, and manages cloud resources based on job queue conditions, eliminating the need for developers to manually perform complex cloud management tasks while enabling cloud-based RPA execution
Solution Approach 2:
The system implements self-service automation where the provisioning infrastructure automatically monitors job queue conditions and dynamically provisions or deprovisions virtual machines as needed. The system serves itself by automatically managing cloud resource allocation without human intervention, simplifying the user experience while maintaining cloud-based RPA capabilities
2Productivity
If virtual machines are dynamically provisioned and terminated based on job queue conditions, then resource utilization efficiency is improved, but system complexity increases
Solution Approach 1:
The provisioning system continuously monitors job queue conditions and uses this feedback to automatically trigger virtual machine provisioning when jobs are present and deprovisioning when queues are empty. This closed-loop feedback mechanism enables dynamic resource adjustment that improves utilization efficiency while keeping the control logic centralized and manageable
Solution Approach 2:
The system transitions from static virtual machine allocation to dynamic provisioning based on real-time job queue conditions. Virtual machines are created, activated, and terminated automatically according to workload demands, enabling the system to adapt its resource capacity dynamically rather than maintaining fixed infrastructure
Data Source
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AI summary
In some embodiments, an automation optimizer is configured to determine whether a provisioning condition is satisfied, for instance according to a current length of a job queue, or according to a current workload of a selected RPA host platform executing a plurality of software robots. When the provisioning condition is satisfied, some embodiments automatically provision additional VMs onto the respective RPA host platform, and automatically remove VMs when automation demand is low. Exemplary RPA hosts include cloud computing platforms and on-premises servers, among others.