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| Content Provider | ACM Digital Library |
|---|---|
| Author | Lui, John C.S. Li, Yongkun Lee, Patrick P.C. |
| Abstract | Solid state drives (SSDs) have seen wide deployment in mobiles, desktops,and data centers due to their high I/O performance and low energy consumption. As SSDs write data out-of-place, garbage collection (GC) is required to erase and reclaim space with invalid data. However, GC poses additional writes that hinder the I/O performance, while SSD blocks can only endure a finite number of erasures. Thus, there is a performance-durability tradeoff on the design space of GC. To characterize the optimal tradeoff, this paper formulates an analytical model that explores the full optimal design space of any GC algorithm. We first present a stochastic Markov chain model that captures the I/O dynamics of large-scale SSDs, and adapt the mean-field approach to derive the asymptotic steady-state performance. We further prove the model convergence and generalize the model for all types of workload. Inspired by this model, we propose a randomized greedy algorithm (RGA) that can operate along the optimal tradeoff curve with a tunable parameter. Using trace-driven simulation on DiskSim with SSD add-ons, we demonstrate how RGA can be parameterized to realize the performance-durability tradeoff. |
| Starting Page | 179 |
| Ending Page | 190 |
| Page Count | 12 |
| File Format | |
| ISSN | 01635999 |
| DOI | 10.1145/2494232.2465546 |
| Journal | ACM SIGMETRICS Performance Evaluation Review (PERV) |
| Volume Number | 41 |
| Issue Number | 1 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-01-10 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Garbage collection Cleaning cost Stochastic modeling Mean field analysis Solid-state drives Wear-leveling |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Networks and Communications Hardware and Architecture Software |
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