Distributed State-Transition Computing for Fault-Tolerant High Bandwidth
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Solution Overview
Problem
Current decentralized technologies face scalability limitations, inefficiencies in bandwidth and scale, high computational resource and energy costs, and lack of standardized interoperability, making them unsuitable for high-bandwidth, high-rate applications and hindering the widespread adoption of Web 3.0 concepts.
Innovation Solution
A system combining two distributed computing sub-systems with redundant data storage and consensus mechanisms to ensure state-coherence and fault tolerance, enabling efficient processing of actions across interacting entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If decentralized technologies are used to achieve fault tolerance and security, then system reliability is improved, but bandwidth and processing speed deteriorate
Solution Approach 1:
The system is divided into multiple independent distributed computing sub-systems, each capable of autonomous operation. This segmentation allows the system to maintain decentralization and fault tolerance while enabling parallel processing that increases overall bandwidth and throughput.
Solution Approach 2:
Multiple distributed computing sub-systems are combined to form a unified resilient computing system. The merging of these sub-systems creates a synergistic effect where the collective bandwidth and processing power exceed what individual systems could achieve alone, while maintaining the reliability benefits of decentralization.
2Reliability
If decentralized technologies are used to ensure security and trust, then system reliability is improved, but scalability deteriorates
Solution Approach 1:
The decentralized system is segmented into independent sub-systems that can be scaled individually. Each sub-system maintains the security and reliability properties of the overall system, allowing incremental scaling without requiring complete system redesign or sacrificing security guarantees.
Solution Approach 2:
The system transitions from a single-dimensional decentralized architecture to a multi-dimensional architecture with multiple independent sub-systems operating in parallel. This dimensional expansion enables scalability by adding capacity across multiple dimensions while preserving the security properties at each dimension.
3Reliability
If consensus mechanisms are implemented for state-coherence, then fault tolerance is improved, but computational resource consumption deteriorates
Solution Approach 1:
The consensus mechanism is implemented independently within each distributed computing sub-system rather than across the entire system. This segmentation reduces the computational overhead of consensus by limiting the scope of agreement protocols to smaller, more manageable groups, thereby reducing overall energy and resource consumption while maintaining fault tolerance.
Solution Approach 2:
Each distributed computing sub-system maintains its own copy of the consensus mechanism and state validation logic. This copying approach allows parallel execution of consensus protocols across multiple sub-systems, distributing the computational burden and reducing the resource consumption of any single consensus operation while maintaining overall system reliability.
Data Source
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.