Cloud Flow Engine Using Data-Ready Execution Without Global State
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Solution Overview
Problem
Current cloud computing platforms face challenges in simplifying the process of designing and executing automated processes, particularly in providing domain-appropriate building blocks, code reuse, and codeless development, due to the need for customized scripts in platform as a service (PaaS) technologies.
Innovation Solution
A system and method for creating and executing flow plans within a cloud-based developmental platform, which includes a service hub system for constructing action flow plans and a flow engine that executes run-time versions of these plans without relying on global state, allowing operations to be executed when input values are ready and predecessor operations have completed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If customized scripts are used to build automated processes in PaaS, then flexibility and functionality are improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The system segments automated process building into discrete, pre-defined building blocks (triggers, actions, conditions) that can be independently selected and combined. This segmentation eliminates the need for custom scripting while maintaining flexibility, as users assemble processes from modular components rather than writing code from scratch.
Solution Approach 2:
The patent implements universal building blocks that can serve multiple functions across different automated processes. Pre-defined triggers, actions, and conditions can be reused across various workflows, providing adaptability without requiring separate custom scripts for each process, thereby reducing overall system complexity.
2Adaptability or versatility
If customized scripts are used to build automated processes, then code flexibility is improved, but ease of operation and codeless development deteriorate
Solution Approach 1:
The system enables codeless development through self-service building blocks that automatically handle process logic. Users configure processes by selecting and parameterizing pre-built components rather than writing scripts, allowing non-programmers to create flexible automated processes through configuration rather than coding.
Solution Approach 2:
The patent applies preliminary action by pre-defining triggers, actions, and conditions before users need to build processes. These pre-configured building blocks contain embedded logic and functionality, allowing users to assemble flexible processes without performing the complex task of scripting, thereby improving ease of operation while maintaining adaptability.
3Reliability
If global state is used to manage flow execution, then coordination between operations is improved, but device complexity and scalability deteriorate
Solution Approach 1:
The patent extracts the global state dependency from the flow execution model. Instead of relying on shared global state to coordinate operations, the system uses explicit data flow dependencies where operations declare their input and output data requirements. This extraction eliminates the complexity of managing global state while maintaining reliable coordination through data-driven dependencies.
Solution Approach 2:
The system introduces data as an intermediary between operations, replacing global state as the coordination mechanism. Operations are coordinated through their data dependencies - input data flows from predecessor operations to successor operations, providing reliable coordination without requiring shared global state, thereby reducing system complexity and improving scalability.
4Manufacturing precision
If input value readiness monitoring is implemented, then execution precision is improved, but use of energy and processing overhead increase
Solution Approach 1:
The system applies partial action by monitoring only the specific input values that are actually required for each operation, rather than continuously monitoring all possible state variables. The flow engine checks readiness of input data on an as-needed basis, achieving precise execution control without the excessive processing overhead of comprehensive state monitoring.
Data Source
AI summary
Creating and executing flow plans by performing at least the following: obtaining a run-time flow plan that comprises a trigger, a first operation, and a second operation, wherein the first operation precedes the second operation within the run-time flow plan and one or more input values of the second operation are linked to the first operation, determining whether one or more conditions of the trigger are met, execute the first operation based at least on the determination that the one or more conditions of the trigger are met, monitoring whether the second operation is ready for execution based at least on a determination that the one or more input values of a second action operation are ready, and executing the second action operation when the second action operation has been identified as ready for execution.