SSD internal cache management policies: A survey

Tripathy, Shivani; Satpathy, Manoranjan

doi:10.1016/j.sysarc.2021.102334

Cited by 15 publications

(3 citation statements)

References 71 publications

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“…In the evolving landscape of hybrid SSD architecture, two primary configurations have emerged as dominant: hard partitioning and soft partitioning. Hard partitioning involves the use of physically distinct memory chips such as SLC, MLC, TLC, or QLC within a single hybrid SSD, as detailed by Tripathy et al [12]. This configuration enables the effective utilization of different types of memory technologies within the same device, each serving specific storage roles based on their performance characteristics.…”

Section: Related Workmentioning

confidence: 99%

“…In the context of hybrid SSD architectures, a notable challenge arises due to the inverse relationship between cache size and available data storage space. As the allocated SLC cache size increases, the available space for data storage correspondingly decreases, therefore emphasizing the need for efficient and strategic management of the SLC cache [12,13]. This management becomes particularly critical when the SLC cache reaches its capacity, necessitating the initiation of processes such as garbage collection or the migration of certain data from the SLC cache to the slower QLC region (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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Data Placement Using a Classifier for SLC/QLC Hybrid SSDs

Cho,

Kim

2024

Applied Sciences

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In hybrid SSDs (solid-state drives) consisting of SLC (single-level cell) and QLC (quad-level cell), efficiently using the limited SLC cache space is crucial. In this paper, we present a practical data placement scheme, which determines the placement location of incoming write requests using a lightweight machine-learning model. It leverages information about I/O workload characteristics and SSD status to identify cold data that does not need to be stored in the SLC cache with high accuracy. By strategically bypassing the SLC cache for cold data, our scheme significantly reduces unnecessary data movements between the SLC and QLC regions, improving the overall efficiency of the SSD. Through simulation-based studies using real-world workloads, we demonstrate that our scheme outperforms existing approaches by up to 44%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Placement Using a Classifier for SLC/QLC Hybrid SSDs

Cho,

Kim

2024

Applied Sciences

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“…Compared with the one-stage algorithms based on YOLO [13] and SSD [14], the detection accuracy is not high, and it is not suitable for application scenarios with high precision requirements. Based on the two-stage algorithm Faster R-CNN, we submits a multi-scale fusion Faster R-CNN algorithm combining K-Means and soft-NMS, this method can generate more suitable anchor boxes and effectively reduce the missed detection rate and enhance the detection accuracy of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale small object detection based on improved Faster R-CNN

Qin²,

Li³

et al. 2023

Second International Conference on Green Communication, Network, and Internet of Things (CNIoT 2022)

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Aiming at the problem that the traditional Faster R-CNN is not sensitive to small targets and occluded targets, this paper submits an improved Faster R-CNN target detection algorithm. In this paper, using PASCAL VOC07+2012 to be the experimental data sample set. For the large differences in the targets to be detected in this set, the general anchor size and dimensions is not often used for detecting multi-category problems. For the purpose of increasing small objects detection accuracy, using K-means to improve this situation, the annotation information is centralized for clustering, and the clustering result is replaced by the anchor scale and size in the original RPN. Finally, missed detection and false detection caused by partial overlap of objects in the image, this paper uses the improved soft-NMS algorithm. The experimental results show that, compared with the traditional Faster R-CNN algorithm, the average mean precision (mAP) of the algorithm under the PASCAL VOC07+2012 dataset can reach 80.7%, and it is enhanced by 6.5 percentage points.

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