RPA Autoscaling Strategies for Speed-Cost Workload Allocation
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Solution Overview
Problem
Conventional robotic process automation (RPA) methods for allocating machines to execute RPA workflows are cumbersome and do not effectively balance speed and cost, as they lack consideration of workload factors such as pending jobs.
Innovation Solution
Implementing RPA autoscaling strategies that dynamically allocate computing environments, including virtual machines, based on selected strategies such as speed over cost, balanced, cost over speed, custom, and dynamic, to optimize resource allocation for completing RPA workloads, considering factors like maximum machines, licensed robots, and workload requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual allocation of machines is used for RPA robots, then user control over machine allocation is maintained, but the process becomes cumbersome and requires extensive user knowledge of contextual information
Solution Approach 1:
The system automatically allocates computing environments to RPA robots based on workload analysis without requiring manual user intervention. The allocation engine self-manages the matching of robots to computing environments by analyzing workload characteristics, robot capabilities, and current system state, thereby eliminating the cumbersome manual allocation process while maintaining optimal resource distribution
Solution Approach 2:
The system dynamically adjusts allocation parameters such as robot selection criteria, computing environment specifications, and allocation timing based on real-time workload conditions. By changing these parameters automatically according to system state, the system resolves the contradiction between ease of operation and process complexity without requiring user knowledge of contextual information
2Productivity
If machine allocation is based on computing resources such as CPU usage and memory usage, then resource utilization is optimized, but workload factors such as pending jobs are not considered
Solution Approach 1:
The allocation system segments the decision-making process into multiple independent analysis components: one component evaluates computing resource metrics (CPU, memory) while another evaluates workload characteristics (pending jobs, job priorities). These segmented analyses are then integrated by the allocation engine to produce a comprehensive allocation decision that considers both resource utilization and workload factors simultaneously
Solution Approach 2:
The system adds a new dimension to the allocation decision space by incorporating workload factors (such as pending job queues and job priorities) as additional evaluation criteria alongside traditional computing resource metrics. This multi-dimensional approach allows the system to optimize both resource utilization and workload appropriateness without sacrificing either dimension
3Speed
If more computing environments are allocated to complete RPA workload faster, then job completion speed is improved, but costs increase
Solution Approach 1:
The system dynamically determines the number of computing environments to allocate based on real-time workload analysis rather than using fixed or static allocation rules. The allocation engine continuously adjusts the allocated resources according to current job priorities, workload characteristics, and cost parameters, enabling the system to optimize the trade-off between job completion speed and the quantity of computing environments used
Solution Approach 2:
The system changes the allocation parameter (number of computing environments) based on multiple factors including job priority, estimated completion time, and cost constraints. By dynamically adjusting this parameter rather than maintaining a fixed allocation, the system achieves faster job completion when necessary while reducing the number of computing environments when cost optimization is the priority
Data Source
AI summary
Systems and methods for allocating computing environments for completing an RPA (robotic process automation) workload are provided. A request for completing an RPA workload is received. A number of computing environments to allocate for completing the RPA workload is calculated based on a selected one of a plurality of RPA autoscaling strategies. The calculated number of computing environments is allocated for allocating one or more RPA robots to complete the RPA workload. The computing environments may be virtual machines.