Computing Replica Action Scheduling With Consensus-Gated Execution
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Solution Overview
Problem
Managing complex and repeatable actions across multiple replicas of computing nodes in a computing environment is challenging, especially as the number of actions increases or becomes more intricate.
Innovation Solution
A consensus-based approach where a replica generates an action notification, receives additional notifications from other replicas, determines consensus, identifies associated rules, and initiates actions on a data structure based on these rules, using protocols like Byzantine Fault Tolerant consensus to ensure synchronized state updates across replicas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If actions are implemented independently at each replica, then system performance and scalability are improved, but data consistency and integrity deteriorate
Solution Approach 1:
The patent introduces a consensus protocol as an intermediary mechanism between replicas. Each replica independently processes actions locally, but the consensus protocol mediates coordination by collecting action notifications from multiple replicas and determining consensus before execution. This mediator enables independent processing while ensuring data consistency through coordinated action selection.
Solution Approach 2:
The system implements feedback mechanisms where replicas continuously exchange action notifications and consensus status. The feedback loop ensures that actions are only executed when sufficient feedback from multiple replicas indicates consensus, thereby maintaining data consistency while allowing high-performance independent processing when conditions are met.
2Reliability
If a consensus protocol is implemented to ensure data consistency, then data integrity is improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent segments the consensus process into distinct phases: action notification generation, action notification collection, consensus determination, and action execution. This segmentation divides the complex coordination task into manageable steps, reducing the complexity burden on any single replica while maintaining data integrity through structured coordination.
Solution Approach 2:
The system performs preliminary actions by collecting action notifications from multiple replicas before executing any actions. This preliminary phase consolidates information about intended actions, allowing the consensus determination to be made based on pre-gathered data rather than real-time coordination during execution, thereby reducing overall system complexity.
3Loss of time
If actions are executed immediately without consensus, then response time is improved, but data discrepancies and conflicts increase
Solution Approach 1:
The patent applies partial action by executing actions only after sufficient consensus is reached, rather than immediately or universally. This partial execution approach ensures that actions are performed only when supported by adequate evidence from multiple replicas, preventing data discrepancies while maintaining acceptable response times by avoiding unnecessary delays from full consensus requirements.
Data Source
AI summary
Embodiments of the present invention provide systems, methods, and software to manage actions across a cluster of replica computing nodes for a data structure. In one implementation, a first replica in the cluster generates a first action notification and receives one or more additional action notifications from at least one other replica in the cluster. The first replica further determines that consensus exists for the first action notification based on the one or more additional action notifications and, in response to determining that consensus exists, identifies one or more rules associated with the first action notification and initiates one or more actions corresponding to the one or more rules.