Efficient Garbage Collection Algorithm for Low Latency SSD

Abstract

Solid-state drives (SSDs) are rapidly replacing hard disk drives (HDDs) in many applications owing to their numerous advantages such as higher speed, low power consumption, and small size. NAND flash memories, the memory devices used for SSDs, require garbage collection (GC) operations to reclaim wasted storage space due to obsolete data. The GC is the major source of performance degradation because it greatly increases the latency for SSDs. The latency for read or write operations is sometimes significantly long if the operations are requested by users while GC operations are in progress. Reducing the frequency of GC invocation while maintaining the storage space requirement may be an ideal solution to remedy this problem, but there is a minimal number of GC operations to reserve storage space. The other approach is to reduce the performance overhead due to GC rather than reducing GC frequency. In this paper, following the latter approach, we propose a new GC scheme that reduces GC overhead by intelligently controlling the priorities among read/write and GC operations. The experimental results show the proposed scheme consistently improve the overall latency for various workloads.

On-Demand Garbage Collection Algorithm with Prioritized Victim Blocks for SSDs

“…We implemented the conventional GC and proposed GC scheme and compared their performances. As a framework to test GC performance, we selected the plane-blocking GC (PBGC) scheme presented in [11] and block-blocking GC (BBGC) scheme from [13]. GC was enabled if 70% of the entire SSD storage was full for all schemes.…”

“…The existing approaches for reducing GC overhead include fine-grained blocking of operations and interrupting GC if a new RW operation is required; however, these approaches have limitations in terms of data hazards and pre-emptive schedule management [13]. Another approach is to distribute data over multiple flash memory chips in a way that reduces the frequency of GC, but this requires careful management of the hotness of data and proper placement of data.…”