Hybrid Memory Data Migration Using Access Temperature Sampling
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Solution Overview
Problem
Current memory access systems struggle with inefficient data migration due to long statistical cycles of access frequencies, leading to suboptimal storage of frequently and infrequently accessed data, which affects system performance and delays.
Innovation Solution
A method for migrating memory data that involves sampling access requests in a hybrid memory system with multiple types of memory media, using periodic sampling modes to determine access temperatures and trigger data migration based on these temperatures, ensuring accurate and rapid identification of frequently and infrequently accessed data blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If access frequencies are collected using traditional statistical methods, then measurement accuracy of access patterns is improved, but the statistical cycle becomes too long to provide timely data for data migration decisions
Solution Approach 1:
The system performs preliminary sampling of access requests and maintains a running statistical record of access frequencies in advance. This preliminary statistical collection enables the processor to make data migration decisions based on real-time access patterns rather than waiting for complete statistical cycles, thus reducing the time loss while maintaining measurement accuracy.
2Measurement precision
If data migration is delayed due to long statistical cycles, then measurement accuracy is maintained, but system productivity and data processing speed deteriorate
Solution Approach 1:
The system implements a feedback mechanism where the processor continuously monitors access frequencies through sampling and uses this real-time feedback to dynamically adjust data migration decisions. The access temperature information is fed back to the memory management system, enabling continuous optimization of data placement between memory media based on current access patterns, thus improving productivity without sacrificing measurement precision.
3Reliability
If traditional statistical collection methods are used, then data accuracy is ensured, but the complexity of the statistical system increases and real-time monitoring becomes difficult
Solution Approach 1:
The system extracts only the essential access frequency information from access requests through sampling, rather than collecting and processing complete detailed statistics. By taking out only the necessary data elements (access counts per page) and discarding redundant information, the system maintains data reliability while significantly reducing the complexity of the statistical collection mechanism.
Data Source
AI summary
A method for migrating memory data, including obtaining a sampling result by sampling obtained access requests according to a sampling rule, where the access request indicates an operation performed by an application run by a processor in a computer device on a memory medium, where the processor is associated with at least two memory media of different types, the sampling rule indicates a periodic sampling mode, collecting statistics on access frequencies of pages indicated by the sampling result, where the access frequencies of the pages are associated with determining access temperatures of the pages, and triggering migration of memory data between the at least two memory media of different types based on the access frequencies.