Replicated State Machine Node Induction via Distributed Consensus
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Solution Overview
Problem
Conventional distributed multi-site collaborative computing solutions face limitations in real-time synchronization, scalability, reliability, and fault-tolerance, leading to delays and increased risk in software development and delivery, particularly due to issues with replicated state machines and information repositories.
Innovation Solution
A replicated state machine architecture with a proposal manager, agreement manager, collision/back-off timer, and storage reclaimer is implemented, allowing for coordinated execution of proposals, preventing repeated pre-emptions, and reclaiming persistent storage, while ensuring all repository replicas are in sync in real-time, eliminating the need for a central transaction coordinator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional distributed multi-site collaborative computing solutions are used, then distributed resource utilization is achieved, but real-time synchronization capability is lost
Solution Approach 1:
The system segments the centralized transaction coordinator functionality into distributed proposal managers at each node. Each node independently manages its own proposals and participates in the agreement protocol, eliminating the single point of failure while maintaining synchronization through distributed consensus.
Solution Approach 2:
The system performs preliminary actions by pre-establishing the agreement protocol and proposal mechanisms at each node before actual transactions occur. This allows nodes to be pre-configured with the capability to synchronize in real-time, rather than relying on centralized coordination during operation.
2Reliability
If conventional replicated state machine implementations are used, then state replication is achieved, but scalability is adversely impacted due to repeated pre-emption of proposers
Solution Approach 1:
The collision/back-off timer implements preliminary action by detecting potential conflicts before they occur and preemptively delaying proposer actions. This prevents repeated pre-emptions and maintains system scalability while ensuring reliable state replication through controlled conflict resolution.
3Reliability
If conventional replicated state machine implementations are used, then state replication is achieved, but complexity increases due to weak leader optimization requiring leader election
Solution Approach 1:
The system extracts the leader election mechanism from the state replication process. By removing the complex leader election and weak leader optimization requirements, the system achieves state replication through simpler distributed agreement protocols, reducing overall system complexity while maintaining reliability.
4Productivity
If conventional distributed multi-site collaborative computing solutions are used, then distributed computing capability is achieved, but fault-tolerance is limited
Solution Approach 1:
The system segments the coordination functionality into distributed proposal managers at each node, eliminating the single point of failure inherent in centralized coordinators. Each node can independently propose and participate in agreement, providing fault-tolerance while maintaining distributed computing capability.
Solution Approach 2:
The storage reclaimer implements discarding and recovering by automatically identifying and reclaiming persistent storage that is no longer needed for state replication. This maintains fault-tolerance by ensuring storage availability while supporting distributed computing operations.
5Productivity
If conventional distributed multi-site collaborative computing solutions are used, then distributed resource utilization is achieved, but storage needs grow continuously without bound
Solution Approach 1:
The storage reclaimer implements automatic discarding and recovering of persistent storage by identifying state data that is no longer needed for replication and reclaiming it. This prevents storage from growing without bound while maintaining the distributed resource utilization capability, as the system can dynamically manage storage resources across the distributed network.
Data Source
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AI summary
A messaging model and node induction methods and corresponding devices and systems are disclosed herein that are effective to enable as inductor node to induct: an inductee node into a distributed computing system and to enable the inducted node to carry out predetermined tasks.