Eventually Consistent Sharing Model Using Revision Diagrams
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Solution Overview
Problem
Existing systems face challenges in maintaining eventual consistency in distributed data systems, particularly when dealing with conflicting updates from multiple sources, often relying on manual conflict resolution or special-purpose code that treats data changes as serializable transactions.
Innovation Solution
The Eventually Consistent Sharing Model employs fork-join automata based on revision diagrams to track updates and uses cloud types to enable fully automatic conflict resolution, allowing mobile devices to share structured data with guaranteed eventual consistency across replicas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual conflict resolution or serializable transactions are used to maintain data consistency, then data consistency is improved, but system complexity and programming difficulty increase
Solution Approach 1:
The system automatically resolves conflicts between concurrent updates using a conflict resolution module that detects and arbitrates conflicting updates without requiring manual intervention. The conflict resolution module examines the revision diagrams and automatically applies resolution rules to merge conflicting updates, making the system self-sufficient in maintaining consistency.
Solution Approach 2:
The patent introduces a conflict resolution module as an intermediary component that mediates between multiple update sources and the shared data structure. This module acts as a mediator that receives conflicting updates, resolves them according to predefined rules, and applies the resolved updates to maintain consistency without requiring manual intervention.
2Reliability
If updates are immediately applied to all replicas to maintain strong consistency, then data consistency is improved, but system scalability and availability deteriorate
Solution Approach 1:
The system dynamically adjusts the consistency model based on the specific update scenario. Instead of always applying updates immediately to all replicas, the system allows replicas to diverge temporarily when conflicts are detected, and then dynamically resolves conflicts when replicas reconnect. This dynamic approach maintains scalability while ensuring eventual consistency.
Solution Approach 2:
The patent segments the update propagation process into independent replica updates that can occur concurrently without requiring immediate coordination across all replicas. Each replica can independently apply updates from its local cache, and conflict resolution is segmented into a separate phase that occurs when replicas synchronize, allowing the system to scale independently.
3Productivity
If weak consistency model is used to improve scalability, then system scalability is improved, but programming difficulty and risk of data invariants breaking increase
Solution Approach 1:
The system performs preliminary actions by pre-defining conflict resolution rules and data type schemas before runtime conflicts occur. The cloud types and their associated conflict resolution rules are established in advance, so when conflicts arise, the system can automatically apply the pre-defined resolution logic without requiring programmers to handle conflicts manually during runtime.
Solution Approach 2:
The conflict resolution module automatically detects and resolves conflicts without requiring programmer intervention, making the weak consistency model as easy to use as strong consistency models. The system self-services by monitoring update conflicts and applying resolution rules automatically, eliminating the programming burden typically associated with weak consistency models.
4Ease of operation
If cloud types with automatic conflict resolution are implemented, then ease of programming is improved, but system complexity increases
Solution Approach 1:
The patent merges the conflict resolution functionality directly into the cloud type definitions. Instead of implementing conflict resolution as a separate complex system, the conflict resolution rules are combined with the data type schemas, allowing the type system itself to guide conflict resolution. This integration reduces the perceived system complexity by unifying data definition and conflict handling.
Data Source
AI summary
An “Eventually Consistent Sharing Model” provides various techniques for using “revision diagrams” to determine both arbitration and visibility of changes or updates to shared data (e.g., data, databases, lists, etc.) without requiring a causally consistent partial order for visibility, and without requiring change or update timestamps for arbitration. In particular, the Eventually Consistent Sharing Model provides fork-join automata based on revision diagrams to track the forking and joining of data versions, thereby tracking updates made to replicas of that data by one or more sources. “Cloud types” are used to define a structure of the shared data that enables fully automatic conflict resolution when updating the shared data. These concepts enable mobile devices (or other computing devices that may periodically go “offline”) to share structured data in cloud-based environments in a manner that provides local data replicas for offline operation while guaranteeing eventually consistent convergence of the data replicas.