Predictive Block Allocation for Storage Garbage Collection
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Solution Overview
Problem
Garbage collection in storage systems can hinder host performance and is inefficient due to its reliance on fixed thresholds and algorithms that do not adapt to usage scenarios, leading to suboptimal block allocation and increased wear on memory blocks.
Innovation Solution
A predictive block allocation method that determines usage scenarios through pattern matching and machine learning to adjust block allocation strategies, altering the timing and frequency of garbage collection operations based on the likelihood of block invalidation and rewrite frequency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed threshold garbage collection algorithms are used, then garbage collection can be performed, but host performance is hindered and block allocation is suboptimal
Solution Approach 1:
The patent implements dynamic block allocation that adapts to different usage scenarios (sequential vs. random writes) by adjusting allocation strategies in real-time. The system transitions from fixed threshold algorithms to usage-aware dynamic allocation, where blocks are allocated based on predicted validity periods and rewrite patterns, thereby improving garbage collection efficiency without harming host performance
Solution Approach 2:
The system changes allocation parameters based on detected usage patterns. For sequential writes, it uses one allocation strategy; for random writes, it uses another. This parameter adaptation allows the system to optimize garbage collection timing and block selection dynamically, resolving the contradiction between garbage collection efficiency and host performance
2Productivity
If aggressive block allocation is used to improve garbage collection, then garbage collection frequency increases, but memory wear increases
Solution Approach 1:
The system performs preliminary actions by predicting which blocks will become invalid soon based on usage patterns. It proactively allocates fresh blocks before invalidation occurs and schedules garbage collection optimally, preventing excessive wear from reactive aggressive collection while maintaining high garbage collection effectiveness
Solution Approach 2:
The system uses feedback from monitoring write patterns and block validity status to continuously adjust allocation strategies. This feedback mechanism prevents overly aggressive garbage collection by learning from actual usage, thereby protecting memory endurance while maintaining effective garbage collection throughput
3Quantity of substance
If more blocks are allocated for garbage collection, then garbage collection capacity increases, but memory over-provisioning increases
Solution Approach 1:
The system dynamically adjusts the number of blocks allocated for garbage collection based on actual usage scenarios. Instead of static over-provisioning, it adapts allocation parameters to match workload characteristics, maintaining sufficient garbage collection capacity while minimizing excess block allocation and improving memory utilization efficiency
Data Source
AI summary
A storage system and method for predictive block allocation for efficient garbage collection are provided. One method involves determining whether a memory in a storage system is being used in a first usage scenario or a second usage scenario; in response to determining that the memory is being used in the first usage scenario, using a first block allocation method; and in response to determining that the memory is being used in the second usage scenario, using a second block allocation method, wherein the first block allocation method allocates blocks that are closer to needing garbage collection than the second block allocation method.