LC-CB: Low Computational Victim Selection Policy in Garbage Collection

NAND flash memory has a disadvantage in that additional work in the flash translation layer (FTL) is required for compatibility with the block interface. In particular, FTL should periodically perform garbage collection (GC) to reclaim free data blocks. Unfortunately, GC includes an expensive erase operation and can cause write amplification (WA), which writes more pages than the system requested. Therefore, it is essential to design a victim selection policy to reduce WA during GC. Greedy and Cost-Benefit (CB) are the most widely known victim selection policies. However, Greedy does not consider locality, and CB suffers computational overhead. This paper proposes Low Computational Cost-Benefit (LC-CB), a novel victim selection policy compensating for these shortcomings. Unlike the existing methods to improve CB, LC-CB changes the operation itself used in the victim selection metric to low computational. This paper describes the constraint required to make a victim selection policy with a low computational overhead and explains that LCCB can consider locality while satisfying these constraints. The experimental results show that our proposed policy can reduce time overhead by 70% compared to CB and reduce WA by up to 30% compared to Greedy.