A Cost-aware Buffer Management Policy for Flash-based Storage Devices

Jiang, Zhiwen; Zhang, Yong; Jin, Wang; Xing, Chunxiao

doi:10.1007/978-3-319-18120-2_11

Cited by 10 publications

(2 citation statements)

References 18 publications

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“…Due to the limited memory space, when the buffer pool is full, the buffer manager must perform a page replacement policy to select a page, namely, victim page, which will be evicted out of the buffer [9,10]. The design of replacement strategies has a significant impact on the performance of the buffer manager [11,12].…”

Section: Related Work 21 Traditional Buffer Managementmentioning

confidence: 99%

Adaptive Multi-Grained Buffer Management for Database Systems

Wang

Jin

2021

Future Internet

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The traditional page-grained buffer manager in database systems has a low hit ratio when only a few tuples within a page are frequently accessed. To handle this issue, this paper proposes a new buffering scheme called the AMG-Buffer (Adaptive Multi-Grained Buffer). AMG-Buffer proposes to use two page buffers and a tuple buffer to organize the whole buffer. In this way, the AMG-Buffer can hold more hot tuples than a single page-grained buffer. Further, we notice that the tuple buffer may cause additional read I/Os when writing dirty tuples into disks. Thus, we introduce a new metric named clustering rate to quantify the hot-tuple rate in a page. The use of the tuple buffer is determined by the clustering rate, allowing the AMG-Buffer to adapt to different workloads. We conduct experiments on various workloads to compare the AMG-Buffer with several existing schemes, including LRU, LIRS, CFLRU, CFDC, and MG-Buffer. The results show that AMG-Buffer can significantly improve the hit ratio and reduce I/Os compared to its competitors. Moreover, the AMG-Buffer achieves the best performance on a dynamic workload as well as on a large data set, suggesting its adaptivity and scalability to changing workloads.

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Section: Related Work 21 Traditional Buffer Managementmentioning

confidence: 99%

Adaptive Multi-Grained Buffer Management for Database Systems

Wang

Jin

2021

Future Internet

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“…When the cold region size reaches the min_lc, the hot region is selected for eviction to ensure that the data in the cold region can gradually become hot. In the processing of hot page and cold page recognition, page-level algorithms, such as CARF [28] and PR-LRU [29], recognize hot data based on the page access history information. CARF converts the corresponding page information into page weights and manages the data pages by the weights.…”

Section: Buffer Management Algorithmmentioning

confidence: 99%

An Advanced Adaptive Least Recently Used Buffer Management Algorithm for SSD

et al. 2019

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The traditional solid-state drive buffer management algorithm generally adopts fixed structures and parameters, leading to their poor adaptability. For example, after the underlying flash translation layer (FTL) or the upper workload is changed, the traditional algorithm's performance fluctuates significantly. Focusing on this problem, based on the cross-layer aware method, we propose an Advanced Adaptive Least Recently Used buffer management algorithm (AALRU). The core idea of the AALRU is that by sensing the characteristics of the upper workload and the status of the underlying FTL, the AALRU adaptively adjusts its structure, parameters, and write-back strategy to optimize the buffer's performance. First, the AALRU divides the buffer into two parts: read buffer and write buffer, and their proportion is adjusted by sensing the read-write characteristics of the workload and the underlying read-write latency. Second, the AALRU employs different granularities to manage the buffer. On one hand, for data loading and migrating, the AALRU adopts page-level granularity, which can avoid the problem of hot and cold data page entanglement in block management, and thus improve the buffer hit ratio. On the other hand, for data writing back to the FTL, the AALRU adopts block-level granularity, which can enhance the continuity of write requests and thus reduce the underlying FTL's garbage collection overhead. Finally, when the clustered data are written back, by sensing the underlying FTL's garbage collection status, the AALRU adaptively adjusts the page-padding trigger threshold to reconstruct the continuity of the write-back data, which can mitigate the underlying FTL's garbage collection overhead. The experimental results show that the AALRU has the best adaptability to different FTLs and test workloads, and it can achieve optimal or near-optimal results. INDEX TERMS Solid-state drive, buffer management, adaptive algorithm, cross-layer aware. I. INTRODUCTION The performance of processors has increased rapidly in accordance with Moore's Law over the past few decades. However, the performance improvement of a storage system based on a hard disk drive (HDD) has been slow, which has led to an increasing performance gap between computing and storage [1]. Compared with the traditional HDD, the solidstate drive (SSD) based on the NAND flash memory has many excellent features such as its low power consumption, fast reading and writing speed, light weight, small size, shock resistance, and noise-free performance [2], [3]. The associate editor coordinating the review of this manuscript and approving it for publication was Guan Gui.

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