Multi-State Reconciliation Finite State Machines for Cloud-Native Control
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Solution Overview
Problem
Implementing multi-state Finite State Automata (FSA) models in cloud computing environments is challenging due to the lack of a comprehensive framework for deploying FSA as cloud-native models, which fails to effectively handle intricate state transitions and provide sufficient granularity in state processes, making it difficult to provide reliable, cloud-based, level-triggered structures for monitoring and managing various applications.
Innovation Solution
A cloud-native framework is provided that manages multi-state FSA models using State Transition and State Delay Matrices to guide dynamic state changes and control transition timing, incorporating a multi-state, level-triggered infrastructure that simplifies deployment, automation, and state management by utilizing matrices to track state transitions and timing, and includes handlers for application-specific states and timing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional FSA implementations are used in cloud environments, then deployment is simpler, but they fail to handle intricate state transitions and provide insufficient granularity in state monitoring
Solution Approach 1:
The patent segments the FSA implementation into distinct components: cloud-native operators, reconciliation mechanisms, state transition managers, and event handlers. This segmentation allows each component to handle specific aspects of state monitoring independently, achieving fine-grained monitoring capability while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces a new dimensional layer of abstraction by implementing cloud-native operators that operate above the traditional FSA model. This additional dimension enables sophisticated state monitoring and reconciliation without directly complicating the core FSA state transition logic, allowing high-level orchestration while preserving low-level simplicity.
2Reliability
If multi-state FSA models are implemented with fine-grained state tracking, then state monitoring capability improves, but system complexity and deployment difficulty increase
Solution Approach 1:
The patent implements self-service mechanisms through automated reconciliation processes that continuously compare actual system state with expected state defined by the FSA model. The system automatically detects and corrects state drift without manual intervention, ensuring high reliability while simplifying deployment because the system self-configures and self-validates upon deployment.
Solution Approach 2:
The patent establishes feedback loops where state transition events are continuously monitored, validated against the FSA model, and reconciled automatically. This feedback mechanism ensures reliable state transitions by detecting anomalies and correcting them, while the automated nature of the feedback process reduces deployment complexity compared to manual state validation approaches.
3Extent of automation
If cloud-native framework with reconciliation is implemented, then state management automation improves, but initial framework complexity increases
Solution Approach 1:
The patent creates universal cloud-native operators that can handle multiple FSA operations (state monitoring, transition validation, event routing, reconciliation) through a single unified framework. This multi-functionality achieves high automation across diverse state management tasks while reducing overall framework complexity by eliminating the need for separate specialized components for each function.
Solution Approach 2:
The patent merges previously separate concerns (state monitoring, transition validation, event handling, and reconciliation) into an integrated cloud-native operator framework. This consolidation achieves comprehensive automation through unified processing logic while simplifying the framework structure by reducing the number of discrete components that would otherwise need to be coordinated.
Data Source
AI summary
The technical solutions disclosed are directed to a multi-state reconciliation finite state automata operator framework. The system and methods can identify one or more states between an initial state and a final state of an application executed by a service and one or more parameters corresponding to timing of implementation of the one or more states. The systems and method can provide a model configured to manage progress corresponding to the one or more states of the application to determine, using a first matrix, a current state of the one or more states of the application and determine, using a second matrix, a parameter of the one or more parameters corresponding to a timing of implementation of the current state. The systems and method can provide to the service an indication of the progress of the application.