TY - GEN
T1 - Offline and Online Algorithms for SSD Management
AU - Lange, Tomer
AU - Naor, Joseph Seffi
AU - Yadgar, Gala
N1 - Publisher Copyright: © 2022 Owner/Author.
PY - 2022/6/6
Y1 - 2022/6/6
N2 - The abundance of system-level optimizations for reducing SSD write amplification, which are usually based on experimental evaluation, stands in contrast to the lack of theoretical algorithmic results in this problem domain. To bridge this gap, we explore the problem of reducing write amplification from an algorithmic perspective, considering it in both offline and online settings. In the offline setting, we present a near-optimal algorithm. In the online setting, we first consider algorithms that have no prior knowledge about the input. We present a worst case lower bound and show that the greedy algorithm is optimal in this setting. Then we design an online algorithm that uses predictions about the input. We show that when predictions are pretty accurate, our algorithm circumvents the above lower bound. We complement our theoretical findings with an empirical evaluation of our algorithms, comparing them with the state-of-the-art scheme. The results confirm that our algorithms exhibit an improved performance for a wide range of input traces.
AB - The abundance of system-level optimizations for reducing SSD write amplification, which are usually based on experimental evaluation, stands in contrast to the lack of theoretical algorithmic results in this problem domain. To bridge this gap, we explore the problem of reducing write amplification from an algorithmic perspective, considering it in both offline and online settings. In the offline setting, we present a near-optimal algorithm. In the online setting, we first consider algorithms that have no prior knowledge about the input. We present a worst case lower bound and show that the greedy algorithm is optimal in this setting. Then we design an online algorithm that uses predictions about the input. We show that when predictions are pretty accurate, our algorithm circumvents the above lower bound. We complement our theoretical findings with an empirical evaluation of our algorithms, comparing them with the state-of-the-art scheme. The results confirm that our algorithms exhibit an improved performance for a wide range of input traces.
KW - flash translation layer
KW - garbage collection
UR - http://www.scopus.com/inward/record.url?scp=85132161240&partnerID=8YFLogxK
U2 - https://doi.org/10.1145/3489048.3522630
DO - https://doi.org/10.1145/3489048.3522630
M3 - منشور من مؤتمر
T3 - SIGMETRICS/PERFORMANCE 2022 - Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems
SP - 89
EP - 90
BT - SIGMETRICS/PERFORMANCE 2022 - Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems
T2 - 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS/PERFORMANCE 2022
Y2 - 6 June 2022 through 10 June 2022
ER -