Distributed Store Consistency Model for Load Balancing
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Solution Overview
Problem
Current storage systems face challenges in handling the growing amount of data, particularly in distributed environments where data resides across different tiers with varying semantics and access patterns, and existing replication solutions are not scalable or optimized for high latency requirements.
Innovation Solution
The implementation of a customized read/write consistency model in a distributed store using a Common Availability Substrate (CAS) that detects system requirements for load balancing and criticality, allowing for dynamic replica creation and management of primary/secondary nodes, enabling strong or weak consistency policies based on system needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in a distributed store across multiple nodes, then data availability and scalability are improved, but system complexity and consistency management difficulty increase
Solution Approach 1:
The system dynamically changes consistency parameters based on operation type and system state. Different consistency levels (strong, eventual, causal) are applied to different operations, allowing the system to maintain high availability while managing complexity through parameterized consistency models rather than a single rigid approach
Solution Approach 2:
The distributed store is segmented into multiple independent nodes that can operate autonomously. Each node manages its own data partitions and can serve requests independently, which improves availability while the segmentation itself helps manage complexity by distributing the system's state across multiple manageable units rather than a monolithic structure
2Reliability
If strong consistency is enforced in a distributed store, then data consistency is improved, but performance and response time deteriorate
Solution Approach 1:
The system allows dynamic adjustment of consistency parameters based on the operation type, data criticality, and system load. Non-critical operations can use weaker consistency models for better performance, while critical operations enforce strong consistency, thus resolving the trade-off between consistency and performance through contextual parameter selection
3Reliability
If replication is implemented for high availability, then system availability is improved, but bandwidth usage and write latency increase
Solution Approach 1:
Different replication strategies are applied to different data based on its availability requirements and access patterns. Hot data with high read throughput requirements may use different replication factors than cold data, optimizing bandwidth usage while maintaining necessary availability levels for each data type
Solution Approach 2:
The system implements partial replication where not all data is replicated to all nodes. Instead, replication is selective based on data importance and access patterns, reducing overall bandwidth consumption while maintaining system availability through strategic placement of replicated data
4Reliability
If primary node handles all writes, then write consistency is improved, but load on primary node increases causing bottlenecks
Solution Approach 1:
The system segments write operations and distributes them across multiple nodes through partitioning. Each node becomes responsible for specific data partitions and can independently handle writes to its partitions, eliminating the single primary node bottleneck while maintaining consistency through coordinated segment management
Solution Approach 2:
An intermediary coordination layer manages consistency across segmented write operations. This mediator handles the coordination of distributed writes, allowing parallel processing while ensuring consistency, thus resolving the conflict between write consistency and throughput
Data Source
AI summary
Systems and methods that designate read/write consistency models based on requirements of a distributed store to increase performance or scale. Such sever loads can be determined via a plurality of mechanisms, including delays in responses by the primary node; setting predetermined threshold limits that if exceeded results in contacting secondary nodes; polling services of the distributed cache periodically and maintaining track of loads on the servers, and the like. The weak or scalable read can occur when read requests are directed to a secondary node, and upon over loading of the primary node.