Decentralized Microservice Architecture with Validator Nodes
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Solution Overview
Problem
Current decentralized technologies face scalability limitations, inefficiencies in bandwidth and scale, and lack standardization and secure interoperability, making them unsuitable for widespread adoption in Web 3.0 applications, which require high-bandwidth and latency-critical operations.
Innovation Solution
A system combining two distributed computing sub-systems using validator computing nodes for redundant state storage and consensus mechanisms to ensure state-coherence, enabling resilient and fault-tolerant operations across multiple entities, facilitating efficient processing and secure data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If decentralized technologies are used to achieve security and trust, then reliability is improved, but device complexity increases
Solution Approach 1:
The system is divided into multiple independent validator computing nodes that each maintain separate data storage spaces. Each node independently validates actions and reaches consensus through distributed protocols, eliminating single points of failure while distributing system complexity across multiple manageable components rather than concentrating it in a centralized authority.
Solution Approach 2:
The patent introduces intermediary consensus mechanisms and validation protocols that mediate between individual validator nodes and the overall system state. These intermediaries coordinate state transitions and ensure agreement across the distributed network without requiring direct peer-to-peer communication between all nodes, thereby managing complexity through structured interaction patterns.
2Reliability
If distributed computing sub-systems are combined for fault tolerance, then reliability is improved, but productivity decreases
Solution Approach 1:
The validator computing nodes are segmented into different groups or clusters that can process actions in parallel. By dividing the validation workload across multiple independent sub-systems rather than requiring all nodes to validate every action sequentially, the system achieves fault tolerance through redundancy while maintaining processing efficiency through parallel execution of validation tasks.
Solution Approach 2:
The system implements partial validation where not all validator nodes need to fully validate every action to reach consensus. Instead, a threshold number of validators (excessive action beyond minimum requirements) can confirm state transitions, allowing the system to achieve fault tolerance with fewer validation steps than complete universal validation would require, thereby improving processing efficiency.
3Reliability
If redundant state storage is implemented across validator nodes, then reliability is improved, but loss of substance increases
Solution Approach 1:
The system creates redundant copies of the state data across multiple validator computing nodes, with each node maintaining a copy in its data storage space. This copying approach ensures state coherence and fault tolerance as long as a threshold number of nodes agree on the state, while the overhead is managed through efficient replication strategies rather than requiring every node to store complete independent copies of all data.
Solution Approach 2:
The redundant state storage across validator nodes serves multiple functions simultaneously: it provides fault tolerance through replication, enables distributed consensus through shared state verification, and allows parallel processing by different node groups. This multi-functionality justifies the storage overhead by deriving multiple benefits from the same redundant data structures.
Data Source
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AI summary
An action involving at least an initiating entity and other affected entities is processed in parts by at least two distributed computing sub-systems associated respectively with the entities, in which the first part in processing the action includes each of a plurality of validator nodes, in one of the computing sub-system associated with the initiating entity, independently validating/processing/recording the request at the initiating side, the second part includes the validator nodes of the initiating entity sending triggering messages to the computing sub-system/s associated with the other entities, and the third part includes each of a plurality of validator nodes, in the computing subsystem/s associated with the other involved entities, independently receiving the messages, and consequently processing and recording the request at all affected sides, thereby implementing a resilient high bandwidth state-transition computer. Consensus mechanisms are used to ensure state-coherence in conjunction with changing states across the distributed computing sub-systems.