A mean field model for a class of garbage collection algorithms in flash-based solid state drives

Garbage collection (GC) algorithms play a key role in reducing the write amplification in flash‐based solid state drives, where the write amplification affects the lifespan and speed of the drive. This paper introduces a mean field model to assess the write amplification and the distribution of the number of valid pages per block for a class $\mathcal{C}$ of GC algorithms. Apart from the Random GC algorithm, class $\mathcal{C}$ includes two novel GC algorithms: the $d$ ‐Choices GC algorithm, that selects $d$ blocks uniformly at random and erases the block containing the least number of valid pages among the $d$ selected blocks, and the Random++ GC algorithm, that repeatedly selects another block uniformly at random until it finds a block with a lower than average number of valid blocks. Using simulation experiments, we show that the proposed mean field model is highly accurate in predicting the write amplification (for drives with $N=50,000$ blocks). We further show that the $d$ ‐Choices GC algorithm has a write amplification close to that of the Greedy GC algorithm even for small $d$ values, e.g., $d=10$ , and offers a more attractive trade‐off between its simplicity and its performance than the Windowed GC algorithm introduced and analyzed in earlier studies. The Random++ algorithm is shown to be less effective as it is even inferior to the FIFO algorithm when the number of pages $b$ per block is large (e.g., for $b\ge 64$ ).