AI-Enabled Process Execution Platform for Multi-Cloud Optimization
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Solution Overview
Problem
Conventional information processing systems lack an adequate mechanism for optimizing multi-cloud deployments in real-time using artificial intelligence, leading to inefficiencies and the need for human intervention in process execution.
Innovation Solution
An information processing system is configured with a processing platform that includes a modelling language extension module to implement AI-based decision points, a process engine to convert these points into input for a process optimization algorithm, and an optimization engine to determine an overall execution path, thereby enabling intelligent and automated process execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional process automation systems are used for multi-cloud deployments, then system simplicity is maintained, but optimization capability and automation level are insufficient
Solution Approach 1:
The system segments the complex optimization task into distinct functional modules: a process modeling language extension module that defines decision points, a process engine that executes workflows, and an optimization engine that performs AI-based optimization. Each module handles a specific aspect of process automation, allowing the system to achieve high automation levels while managing complexity through modular design.
Solution Approach 2:
The patent introduces a process modeling language extension as an intermediary layer between the process engine and the optimization engine. This extension provides standardized decision point definitions and context attribute specifications, enabling seamless integration and data exchange between components without requiring direct complex interactions, thus facilitating automation while controlling system complexity.
2Measurement precision
If AI-based decision points are integrated into process flow, then decision-making accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent changes the parameters of process decision-making by introducing AI-based decision points with context attributes that capture relevant process state information. The optimization engine uses these parameters to make accurate decisions by evaluating multiple factors simultaneously, improving decision-making accuracy while the standardized parameter structure helps manage the associated complexity.
Solution Approach 2:
The process modeling language extension performs preliminary action by pre-defining decision point structures and context attribute schemas before execution. This preparation work establishes a standardized framework that guides the AI optimization process, ensuring accurate decision-making while reducing the complexity of real-time processing by having the structural framework already in place.
3Productivity
If real-time optimization is implemented using AI, then process efficiency is improved, but computational resource requirements increase
Solution Approach 1:
The optimization engine implements partial optimization by focusing computational resources on optimizing specific decision points and execution paths rather than analyzing the entire process flow simultaneously. This selective approach improves overall process efficiency while reducing total computational resource consumption by concentrating AI processing power where it provides the greatest marginal benefit.
Data Source
AI summary
An apparatus in one embodiment comprises a processing platform that includes a plurality of processing devices each comprising a processor coupled to a memory. The processing platform is configured to implement at least a portion of at least a first cloud-based system. The processing platform comprises a modelling language extension module configured to implement artificial intelligence-based decision points into a process flow and compile context attributes associated with the artificial intelligence-based decision points based on data from artificial intelligence systems. The processing platform also comprises a process engine configured to convert the artificial intelligence-based decision points and context attributes to input to a process optimization algorithm, and an optimization engine configured to determine, by applying the process optimization algorithm to the converted input, an overall execution path within the process flow, and output a decision to a first of the artificial intelligence-based decision points based on the overall execution path.