Adaptive Data Pattern Tracking for Memory Deduplication
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Solution Overview
Problem
Existing memory apparatuses face challenges in efficiently tracking and deduplicating data patterns due to limited storage space in local memory, resource-intensive tracking methods, and difficulty in predicting frequently used data patterns, leading to reduced performance and resource inefficiency.
Innovation Solution
Implementing a data structure with entries containing data patterns, reference counters, measure flags, clear flags, and lock flags to adaptively manage and promote or demote data patterns based on access frequency, allowing efficient deduplication and improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data patterns are tracked using traditional methods, then deduplication can be achieved, but the storage space required in local memory increases and system resources are consumed
Solution Approach 1:
The patent changes the parameters of data pattern tracking by introducing a threshold value and adaptive promotion/demotion mechanism. Data patterns are promoted to higher tracking precision levels when they exceed the threshold, allowing the system to dynamically adjust storage requirements based on actual usage patterns rather than uniformly tracking all data patterns.
Solution Approach 2:
The system dynamically manages the tracking data structure by promoting frequently accessed data patterns to higher priority levels and demoting less frequently accessed patterns. This dynamic reorganization allows the system to adapt to changing data access patterns and optimize storage space utilization over time.
2Productivity
If comprehensive tracking of all data patterns is implemented, then deduplication performance improves, but system complexity and resource consumption increase
Solution Approach 1:
The tracking system dynamically adjusts its behavior based on data access patterns. Data patterns are automatically promoted or demoted based on their frequency of access, allowing the system to maintain high deduplication performance for frequently used patterns while reducing complexity for rarely used patterns.
Solution Approach 2:
The system introduces a threshold parameter that dynamically determines which data patterns warrant comprehensive tracking. By adjusting this threshold and associated tracking parameters based on observed access patterns, the system optimizes the balance between performance and complexity.
3Ease of manufacture
If static tracking methods are used, then implementation is simple, but the system cannot adapt to changing data patterns and access frequencies
Solution Approach 1:
The patent implements a dynamic tracking mechanism that automatically adapts to changing data patterns. The system monitors access frequencies and dynamically promotes or demotes data patterns based on observed behavior, enabling it to adapt to evolving data access patterns without requiring manual reconfiguration.
Solution Approach 2:
The system incorporates feedback mechanisms where access patterns are continuously monitored and used to adjust tracking priorities. This feedback loop enables the system to learn from actual usage behavior and automatically optimize its tracking strategy, bridging the gap between simple implementation and adaptive behavior.
Data Source
AI summary
In some implementations, a memory apparatus may receive a command to write data to volatile memory of the memory system. The memory apparatus may compare the data to one or more data patterns to identify whether the data matches a data pattern of the one or more data patterns. The memory apparatus may increment a reference counter associated with the data pattern based on identifying that the data matches the data pattern. The memory apparatus may write, based on identifying that a measure flag associated with the data pattern is set, the data to the volatile memory.