Configurable State Processing in Blockchain Systems
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Solution Overview
Problem
Current blockchain platforms lack configurability in state management, leading to inefficiencies in processing state transitions due to varying levels of trust among peers and the need for concurrent state updates, which can hinder processing speed and flexibility.
Innovation Solution
A system and method that facilitate configurable internal versus external state management using machine learning to trace secure paths in state transition graphs, allowing for flexible management of incremental and end-to-end state variables through various storage and execution options, such as External-Internal, Internal-Internal, External-External, and Internal-External configurations, to optimize state processing based on specific use-cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If concurrent state processing is implemented to handle varying trust levels among peers, then flexibility and adaptability are improved, but processing speed deteriorates
Solution Approach 1:
The system dynamically configures state management modes (shared state vs. private state) based on trust levels between peers. The state management mode is not fixed but adapts to the specific trust requirements of each blockchain transaction, allowing the system to switch between concurrent processing (high trust) and sequential processing (low trust) as needed.
Solution Approach 2:
The system changes the parameter of state management configuration based on trust levels. By modifying the state management mode parameter from shared to private or vice versa, the system optimizes processing performance for different trust scenarios without requiring a complete system redesign.
2Adaptability or versatility
If configurable state management modes are implemented to support different use-cases, then adaptability is improved, but device complexity increases
Solution Approach 1:
The state management system is designed as a universal component that handles multiple use-cases through a single configurable framework. Instead of implementing separate processing systems for different trust scenarios, the same system supports all scenarios by configuring the state management mode, reducing overall system complexity.
Solution Approach 2:
Different use-cases are supported by changing configuration parameters rather than implementing separate systems. The state management mode parameter can be adjusted to match different trust levels and use-case requirements, allowing a single system to serve multiple purposes without proportional increase in complexity.
3Reliability
If shared state processing is used to ensure consistency across peers, then reliability is improved, but processing efficiency deteriorates
Solution Approach 1:
The system dynamically selects between shared state processing (for high reliability requirements) and private state processing (for high efficiency requirements). This dynamic adaptation allows the system to optimize the balance between reliability and efficiency based on the specific trust levels and use-case requirements of each transaction.
Solution Approach 2:
The state management mode parameter can be changed to switch between shared and private state processing. This parameter change allows the system to adjust the level of consistency enforcement, thereby optimizing processing efficiency while maintaining adequate reliability for the given use-case.
Data Source
AI summary
The present invention provides a method and system for facilitating configurable state processing in blockchain systems. The system may be configured to receive a set of data packets corresponding to state variables in any or a combination of incremental transitions and end to end transaction from an initial E2E-BState to an end E2E-BState. from a first computing device, extract a first set of attributes from the set of data packets received corresponding to one or more paths in a state transition graph starting from the initial E2E-BState to the end E2E-BState. With the help of the ML engine, the system may trace a predefined path based that may be secure and any or a combination of the incremental transitions and the end-to-end state transitions may be configurable and then capture, record or store changes to the state variables in a database.