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Adaptive Channel-Aware Garbage Collection Control for Multi-Channel SSDsopen access

Authors
Mun, HyunhoHong, Youpyo
Issue Date
Dec-2025
Publisher
MDPI
Keywords
solid-state drive; garbage-collection; multi-channel SSD; NAND flash memory
Citation
Electronics, v.14, no.23, pp 1 - 19
Pages
19
Indexed
SCIE
SCOPUS
Journal Title
Electronics
Volume
14
Number
23
Start Page
1
End Page
19
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/62611
DOI
10.3390/electronics14234741
ISSN
2079-9292
2079-9292
Abstract
Solid-State Drives (SSDs) have become the dominant storage medium in performance-sensitive systems due to their high throughput, reliability, and energy efficiency. However, inherent constraints in NAND flash memory-such as out-of-place writes, block-level erase operations, and data fragmentation-necessitate frequent garbage collection (GC), which can significantly degrade user I/O performance when not properly managed. This paper presents a channel-aware GC control mechanism for multi-channel SSD architectures that limits GC concurrency based on real-time storage utilization. Unlike conventional controllers that allow GC to proceed simultaneously across all channels-often leading to complete I/O stalls-our approach adaptively throttles the number of GC-active channels to preserve user responsiveness. The control logic uses a dynamic thresholding function that increases GC aggressiveness only as the SSD approaches full capacity, allowing the system to balance space reclamation with quality-of-service guarantees. We implement the proposed mechanism in an SSD simulator and evaluate its performance under a range of real-world workloads. Experimental results show that the proposed adaptive GC control significantly improves SSD responsiveness across various workloads. Across all workloads, the proposed adaptive GC control achieved an average latency improvement factor of 4.86x, demonstrating its effectiveness in mitigating GC-induced interference. Even when excluding extreme outlier cases, the method maintained an average improvement of 1.55x, with a standard deviation of 1.17, confirming its consistency and robustness across diverse workload patterns.
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