Cross-Channel Data Caching via Event Prediction
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Solution Overview
Problem
Remote databases or servers with limited hardware capacity face challenges in efficiently managing time-sensitive data due to limitations in central processing units, random access memory, and bandwidth, using conventional cache replacement policies like LRU and FIFO.
Innovation Solution
A system and method for triggering cross-channel data caching on network nodes, which involves monitoring historical and live event data to predict expected events, caching relevant data, and transmitting it to nodes associated with expected channels, where adapters format the data for compatibility, and maintaining it for an expected event period to reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cached data is maintained at remote databases using conventional replacement policies, then data availability is improved, but hardware capacity (CPU, RAM, bandwidth) is overwhelmed
Solution Approach 1:
The system segments the caching function by deploying distributed cache nodes across the network infrastructure rather than relying on a single remote database. Each cache node maintains a portion of the cached data, distributing the storage burden and reducing the hardware capacity requirements at any single location while improving overall data availability.
Solution Approach 2:
The patent introduces intermediary cache nodes that act as mediators between the central data source and remote databases. These intermediary nodes buffer and manage data transmissions, reducing the direct hardware capacity demands on remote databases while maintaining data availability through the distributed caching layer.
2Loss of time
If data is cached closer to channels, then network latency is reduced, but data format compatibility across different channels becomes complex
Solution Approach 1:
The cache nodes are designed with multi-functional capabilities to handle multiple data formats and serve different channel types. Each cache node can adaptively format data according to the specific requirements of connected channels, eliminating the need for separate caching systems for each channel type and reducing overall system complexity.
Solution Approach 2:
The system dynamically changes data format parameters at the cache nodes based on the destination channel requirements. Rather than maintaining fixed data formats, the cache nodes transform data parameters (format, structure, encoding) to match the specific needs of each channel, thereby reducing latency without requiring complex manual configuration.
3Reliability
If cached data is stored for extended periods, then data accessibility is improved, but storage resource consumption increases
Solution Approach 1:
The cache nodes implement periodic data validation and refresh operations. Instead of indefinitely storing all cached data, the system periodically assesses data freshness and relevance, refreshing or evicting data based on usage patterns and validity periods. This periodic action maintains data accessibility for active data while automatically freeing storage resources for inactive or expired data.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor data access patterns, freshness requirements, and storage utilization at cache nodes. This feedback drives dynamic adjustments to caching strategies, extending the retention period for frequently accessed data while automatically reducing storage allocation for rarely accessed data, thereby optimizing the balance between accessibility and resource consumption.
Data Source
AI summary
Embodiments of the present invention provide a system for triggering cross channel data caching on network nodes. Historical event data and live event data of a user may be monitored to determine an expected event that includes one or more expected channels. An expected period of time for the expected event may also be determined. Relevant user data may then be identified from one or more systems of record and cached or otherwise compressed. One or more nodes of a network associated with each of the one or more expected channels are then identified. The cached data is then transmitted to virtual data structures associated with each of the one or more nodes. The cached data is then generally maintained in these virtual data structures on the network for the expected period of time.