Sliding Window Cache for Data Grid Consistency

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Solution Overview

Problem

Distributed computing environments face data inconsistency and server overload due to high volumes of conflicting data from multiple client devices, which can lead to bottlenecks and increased maintenance costs when expanding or adding servers or databases.

Innovation Solution

Implementing a sliding window cache within a data grid that uses a sampling technique to store and synchronize data entries, allowing for efficient data consistency and statistical analysis while managing resource thresholds.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If data is stored in multiple copies across different nodes to ensure resilience, then reliability is improved, but device complexity increases

Engineering Contradiction:

ImproveresilienceVSAvoiddata storage complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The sliding window cache acts as an intermediary layer between client devices and the distributed data grid nodes. It pre-processes and samples incoming data streams before distribution, reducing the complexity of managing multiple copies while maintaining resilience through selective replication of cached entries across nodes.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary actions by pre-caching and pre-processing data entries in the sliding window cache before they need to be distributed across the network. This advance preparation reduces the computational and coordination complexity required when actually replicating data to multiple nodes for resilience.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If servers are expanded or additional servers are added to improve data consistency, then data consistency is improved, but device complexity and maintenance costs increase

Engineering Contradiction:

Improvedata consistencyVSAvoidserver management complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The sliding window cache serves as a mediator that standardizes data preprocessing and sampling operations. This intermediary layer simplifies server management by providing a uniform mechanism for maintaining data consistency across existing nodes, eliminating the need for complex coordination protocols when adding or expanding servers.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

Instead of replicating complex server infrastructure and data consistency mechanisms, the system creates simplified copies of cached data entries across nodes. This copying approach maintains data consistency while reducing the complexity associated with managing expanded server infrastructure.

Inventive Principle:

Principle #26Copying

3Reliability

If multiple databases are added to improve data consistency, then data consistency is improved, but maintenance complexity and costs increase

Engineering Contradiction:

Improvedata consistencyVSAvoiddatabase maintenance complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The sliding window cache merges multiple data streams from different client devices into a unified cached structure. This consolidation allows the system to maintain data consistency across the distributed grid without requiring multiple separate databases, thereby reducing maintenance complexity while preserving reliability.

Inventive Principle:

Principle #5Merging (Combining)

4Measurement precision

If a cache stores all data entries to ensure data accuracy, then measurement precision is improved, but loss of time increases due to synchronization overhead

Engineering Contradiction:

Improvedata accuracyVSAvoidsynchronization time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The sliding window cache implements partial action by storing only a sampled subset of data entries rather than all incoming data. This selective caching maintains sufficient data accuracy for statistical measurements while significantly reducing the synchronization overhead and time required to keep the cache updated across the distributed system.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system changes the parameter of data retention by implementing a sliding window mechanism that automatically expires old entries and retains only recent data within a defined time window. This parameter adjustment maintains data accuracy for current measurements while reducing synchronization time by eliminating the need to manage and update the entire historical dataset.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11847354B2Data storage using a sliding window cache in a data grid

Publication Date: 2023.12.19 RED HAT LLC

AI summary

A sliding window cache can be used for data storage in a data grid. For example, a computing device can receive a request from a client device for storing a data entry in a data grid. The computing device can store the data entry in a first data set including a plurality of data entries distributed across a plurality of nodes of the data grid. The computing device can also store the data entry in a second data set in a sliding window cache that is embedded in the data grid. The second data set can include a subset of the plurality of data entries synchronized with the plurality of data entries of the first data set. The computing device can determine a statistic measurement associated with the sliding window cache and output the statistic measurement to the client device.