Version 1
: Received: 23 January 2024 / Approved: 23 January 2024 / Online: 23 January 2024 (13:38:41 CET)
How to cite:
Chang, H.-P.; Su, W.-M.; Chang, D.-W. A New Readahead Framework for SSD-based Caching Storage in IoT Systems. Preprints2024, 2024011695. https://doi.org/10.20944/preprints202401.1695.v1
Chang, H.-P.; Su, W.-M.; Chang, D.-W. A New Readahead Framework for SSD-based Caching Storage in IoT Systems. Preprints 2024, 2024011695. https://doi.org/10.20944/preprints202401.1695.v1
Chang, H.-P.; Su, W.-M.; Chang, D.-W. A New Readahead Framework for SSD-based Caching Storage in IoT Systems. Preprints2024, 2024011695. https://doi.org/10.20944/preprints202401.1695.v1
APA Style
Chang, H. P., Su, W. M., & Chang, D. W. (2024). A New Readahead Framework for SSD-based Caching Storage in IoT Systems. Preprints. https://doi.org/10.20944/preprints202401.1695.v1
Chicago/Turabian Style
Chang, H., Wei-Ming Su and Da-Wei Chang. 2024 "A New Readahead Framework for SSD-based Caching Storage in IoT Systems" Preprints. https://doi.org/10.20944/preprints202401.1695.v1
Abstract
In an IoT system, the sheer volume of data generated by numerous sensing devices necessitates a well-designed scheme for storing and retrieving data efficiently, enabling streamlined data processing and analytics. One promising storage architecture involves utilizing solid-state drives (SSDs) to cache data from hard disk drives (HDDs), thereby creating an SSD-based caching storage system. To further enhance access performance, it is possible to employ readahead techniques to minimize data access latency. However, the existing Linux readahead scheme falls short in fully leveraging SSD-based caching storage systems. We address this limitation by introducing a novel cross-layered readahead architecture that effectively communicates with the VFS layer, the file system layer, and the block I/O layer. This communication facilitates the acquisition of readahead timing, readahead data continuity, and readahead data location, respectively. To guide prefetching decisions, our architecture analyzes the degree of data access sequentiality, the performance model of the target storage device, and the access patterns of the I/O workload on the corresponding storage device. The implementation of this new architecture in the Linux kernel yields promising experimental results, demonstrating its robustness by consistently outperforming the stock Linux kernel. Notably, our architecture reduces the total execution time of the stock Linux kernel by up to 49%, except in cases of random workloads where both the stock Linux kernel and our architecture exhibit similar performance.
Keywords
data retrieval; prefetching; readahead; SSD-based caching storage systems; IoTs; Linux
Subject
Computer Science and Mathematics, Software
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
