Unified Automation Platform Resource Manager for RPA SLA Compliance
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Solution Overview
Problem
The complexity of monitoring, controlling, and managing multiple robotic process automation (RPA) platforms and bots across various enterprises becomes resource-intensive as the number of platforms and bots increases, leading to challenges in ensuring compliance with service level agreements (SLAs) and efficient resource allocation.
Innovation Solution
A unified automation platform (UAP) with a smart resource manager (SRM) that receives process and resource data, determines process ranks based on multiple parameters, calculates the number of resources required to meet SLAs, and provisions additional resources through platform adapters, enabling real-time monitoring and allocation of resources to maintain SLA compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of RPA platforms and bots increases, then the automation capability and productivity improve, but the complexity of monitoring, controlling, and managing these systems increases and becomes resource-intensive
Solution Approach 1:
The patent merges multiple RPA platforms under a unified management architecture that consolidates monitoring, controlling, and resource allocation functions. This centralization reduces the overall complexity of managing disparate platforms while maintaining their individual automation capabilities, directly addressing the contradiction between increased productivity and management complexity.
Solution Approach 2:
The management system implements universal interfaces and standardized protocols that enable a single system to manage multiple different RPA platforms. This multi-functional approach allows the system to handle diverse automation tools through common management mechanisms, reducing the resource intensity of managing increasingly complex RPA environments.
2Reliability
If real-time monitoring and resource allocation is implemented to ensure SLA compliance, then the reliability and service quality improve, but the computational resources and system overhead increase
Solution Approach 1:
The system performs preliminary analysis of process patterns, resource requirements, and SLA constraints to pre-determine optimal resource allocation strategies. By calculating resource needs in advance based on historical data and predicted workloads, the system can maintain SLA compliance without requiring continuous heavy computational resources for real-time adjustments, thus improving reliability while controlling resource consumption.
Solution Approach 2:
The resource management system implements self-adjusting mechanisms that automatically allocate and reallocate resources based on current workload and SLA requirements without requiring intensive external computational intervention. The system monitors its own performance and makes autonomous decisions to maintain compliance, reducing the overall computational overhead while ensuring reliability.
Data Source
AI summary
Implementations directed to managing resources executing in one or more RPA platforms, and include actions of receiving process data representative of two or more processes executed by resources in a RPA platform, and resource data representative of the resources in the RPA platform, determining a process rank for each of the two or more processes, respectively, for at least one process, calculating a number of resources required to complete the process in conformance with a SLA governing the process, and transmitting instructions through a platform adapter to provision at least one additional resource to execute the process within the RPA platform, the platform adapter being specific to the RPA platform, and being one of a plurality of platform adapters.