Distributed Coordination Engine for One-Copy Storage Equivalence
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Solution Overview
Problem
Existing storage systems struggle with determining data consistency across distributed, heterogeneous storage environments, where local consistency checks are insufficient for ensuring equivalence across geographically dispersed and asynchronous systems.
Innovation Solution
A Distributed Coordination Engine (DConE) is implemented to manage consensus and ordering of transactions across replicated state machines, using the Paxos algorithm to ensure global sequence numbering and automated recovery, enabling real-time consistency checks and equivalence determination across diverse storage systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If local consistency checks are used in distributed storage systems, then operational simplicity is maintained, but data consistency equivalence across geographically dispersed systems cannot be ensured
Solution Approach 1:
A coordination engine is introduced as an intermediary component that manages consistency checks across distributed storage systems. This engine receives data from multiple storage systems, performs centralized consistency verification, and coordinates recovery operations, thereby enabling global consistency equivalence without requiring complex peer-to-peer verification mechanisms between all storage nodes.
Solution Approach 2:
Multiple distributed storage systems are merged into a unified coordination framework where a single coordination engine aggregates data from heterogeneous storage systems. This merging approach allows the system to treat geographically dispersed storage units as a cohesive entity, ensuring data equivalence across all locations through centralized coordination rather than distributed complexity.
2Productivity
If automated recovery mechanisms are implemented, then system availability is improved, but recovery time and system complexity increase
Solution Approach 1:
The coordination engine performs preliminary actions by continuously monitoring data consistency and pre-positioning recovery information across the distributed system. When failures are detected, the engine has already prepared recovery data and coordination plans, enabling immediate restoration of failed nodes without extensive computation or data retrieval during the recovery phase.
Solution Approach 2:
The system implements self-service recovery mechanisms where the coordination engine automatically detects failures, retrieves missing data from surviving storage systems, and restores failed nodes without human intervention. This automated self-service approach minimizes recovery time while maintaining system availability, as the engine handles all recovery operations autonomously based on pre-established consistency protocols.
Data Source
AI summary
First replicated state machines may execute a sequence of ordered agreements to make mutations to data stored in a first data storage of a first type and second replicated state machines may execute the sequence of ordered agreements to make mutations to data stored in a second data storage of a second type. First metadata of the mutated data stored in the first data storage may then be received and stored, as may second metadata of the mutated data stored in the second data storage. The first and second metadata may be compared when the data stored in the first data storage that corresponds to the first metadata and the data stored in the second data storage that corresponds to the second metadata have settled according to the predetermined one of the sequence of ordered agreements. A selected action may then be carried out depending upon the comparison.