RPA Robot Cloud Provisioning Using Queue-Triggered VM Scaling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robotic process automation (RPA) technologies face challenges in facilitating the programming and deployment of software robots designed to execute in cloud environments, requiring expertise in RPA tools, cloud management, and virtualization, which exceeds the competence of average RPA developers.
Innovation Solution
A method employing at least one hardware processor to determine provisioning conditions for RPA jobs and robots, initiating automatic provisioning of virtual machines on selected host platforms using VM templates, and connecting RPA robots to an orchestrator for job assignment, with automatic termination of VMs when conditions are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automatic provisioning and management of virtual machines is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system implements self-service through automatic provisioning of virtual machines based on queue conditions, self-management of robot instantiation and termination, and automated connection to orchestrators. The hardware processor autonomously monitors job queues, provisions VMs when thresholds are met, and terminates them when no longer needed, eliminating manual intervention and simplifying operation for users.
2Productivity
If dynamic provisioning of virtual machines is implemented, then productivity is improved, but use of energy increases
Solution Approach 1:
The system dynamically adjusts computational resources by provisioning virtual machines when job queue thresholds are met and terminating them when conditions change. This dynamic provisioning allows the system to scale processing capacity up or down based on actual workload demands, optimizing productivity while managing energy consumption appropriately.
Solution Approach 2:
The system changes operational parameters by monitoring job queue lengths and robot pool sizes, and adjusts resource allocation accordingly. When the queue exceeds a threshold, the system provisions additional VMs; when the queue is clear, it terminates VMs. This parameter-based control enables flexible resource management that balances productivity with energy efficiency.
3Extent of automation
If automated robot provisioning is implemented, then extent of automation is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring job queue lengths and robot pool sizes, comparing these against predefined thresholds, and automatically adjusting resource allocation based on the feedback. The hardware processor detects queue conditions and robot availability, and uses this information to trigger provisioning or termination actions, creating a closed-loop automated system that is easy to monitor through simple threshold comparisons.
Data Source
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 to 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.