A mean field model for a class of garbage collection algorithms in flash-based solid state drives
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Abstract
Garbage collection (GC) algorithms play a key role in reducing the write amplification in flash-based solid state drives, where the write amplification affects the lifespan and speed of the drive. This paper introduces a mean field model to assess the write amplification and the distribution of the number of valid pages per block for a class \(\mathcal {C}\) of GC algorithms. Apart from the Random GC algorithm, class \(\mathcal {C}\) includes two novel GC algorithms: the \(d\)-Choices GC algorithm, that selects \(d\) blocks uniformly at random and erases the block containing the least number of valid pages among the \(d\) selected blocks, and the Random++ GC algorithm, that repeatedly selects another block uniformly at random until it finds a block with a lower than average number of valid blocks. Using simulation experiments, we show that the proposed mean field model is highly accurate in predicting the write amplification (for drives with \(N=50{,}000\) blocks). We further show that the \(d\)-Choices GC algorithm has a write amplification close to that of the Greedy GC algorithm even for small \(d\) values, e.g., \(d = 10\), and offers a more attractive trade-off between its simplicity and its performance than the Windowed GC algorithm introduced and analyzed in earlier studies. The Random++ algorithm is shown to be less effective as it is even inferior to the FIFO algorithm when the number of pages \(b\) per block is large (e.g., for \(b \ge 64\)).
Keywords
Mean field model Garbage collection Flash-based solid state drives Write amplification \(d\)-ChoicesMathematics Subject Classification
60K30 68W20 68M20 68P20References
- 1.Agarwal, R., Marrow, M.: A closed-form expression for write amplification in NAND flash. In: IEEE GLOBECOM Workshops (GC Wkshps), pp. 1846–1850 (2010)Google Scholar
- 2.Azar, Y., Broder, A.Z., Karlin, A.R., Upfal, E.: Balanced allocations. SIAM J. Comput. 29(1), 180–200 (1999)Google Scholar
- 3.Ban, A.: Wear leveling of static areas in flash memory. US patent 6,732,221. Filed June 1, 2001; Issued May 4, 2004; Assigned to M-Systems (2004)Google Scholar
- 4.Benaïm, M., Le Boudec, J.: A class of mean field interaction models for computer and communication systems. Perform. Eval. 65(11–12), 823–838 (2008)CrossRefGoogle Scholar
- 5.Bux, W., Iliadis, I.: Performance of greedy garbage collection in flash-based solid-state drives. Perform. Eval. 67(11), 1172–1186 (2010)CrossRefGoogle Scholar
- 6.Chen, F., Koufaty, D.A., Zhang, X.: Understanding intrinsic characteristics and system implications of flash memory based solid state drives. ACM SIGMETRICS Perform. Eval. Rev. 37(1), 181–192 (2009)Google Scholar
- 7.Desnoyers, P.: Analytic modeling of SSD write performance. In: Proceedings of International Systems and Storage Conference (SYSTOR 2012) (2012)Google Scholar
- 8.Gast, N., Gaujal, B.: Markov chains with discontinuous drifts have differential inclusion limits. Perform. Eval. 69(12), 623–642 (2012)CrossRefGoogle Scholar
- 9.Grupp, L.M., Davis, J.D., Swanson, S.: The bleak future of NAND flash memory. In: Proceedings of USENIX Conference on File and Storage Technologies (2012)Google Scholar
- 10.Hu, X., Eleftheriou, E., Haas, R., Iliadis, I., Pletka, R.: Write amplification analysis in flash-based solid state drives. In: Proceedings of SYSTOR 2009: The Israeli Experimental Systems Conference, SYSTOR ’09, New York, NY, pp. 10:1–10:9 (2009)Google Scholar
- 11.Kang, J.-U., Jo, H., Kim, J.-S., Lee, J.: A superblock-based flash translation layer for NAND flash memory. In: Proceedings of the 6th ACM & IEEE International conference on Embedded software. EMSOFT ’06, New York, NY, pp. 161–170 (2006)Google Scholar
- 12.Li, Y., Lee, P.P.C., Lui, J.C.S.: Stochastic modeling of large-scale solid-state storage systems: analysis, design tradeoffs and optimization. ACM SIGMETRICS Perform. Eval. Rev. 41(1), 179–190 (2013)CrossRefGoogle Scholar
- 13.Menon, J.: A performance comparison of RAID-5 and log-structured arrays. In: Proceedings of the 4th IEEE International Symposium on High Performance Distributed Computing. HPDC ’95, Washington, DC, pp. 167–178 (1995)Google Scholar
- 14.Min, C., Kim, K., Cho, H., Lee, S., Eom, Y. I.: SFS: Random write considered harmful in solid state drives. In Proceedings of the USENIX Conference on File and Storage Technologies, pp. 139–155 (2012)Google Scholar
- 15.Mitzenmacher, M., Richa, A., Sitaraman, R.: The power of two random choices: a survey of techniques and results. In: Handbook of Randomized Computing, vol. 1. Kluwer Academic Publishers, Norwell (2001)Google Scholar
- 16.Robinson, J.T.: Analysis of steady-state segment storage utilizations in a log-structured file system with least-utilized segment cleaning. SIGOPS Oper. Syst. Rev. 30(4), 29–32 (1996)CrossRefGoogle Scholar
- 17.Rosenblum, M., Ousterhout, J.K.: The design and implementation of a log-structured file system. ACM Trans. Comput. Syst. 10(1), 26–52 (1992)CrossRefGoogle Scholar
- 18.Van Houdt, B.: A mean field model for a class of garbage collection algorithms in ash-based solid state drives. ACM SIGMETRICS Perform. Eval. Rev. 41(1), 191–202 (2013)CrossRefGoogle Scholar
- 19.Van Houdt, B.: Performance of garbage collection algorithms for ash-based solid state drives with hot/cold data. Perform. Eval. 70(10), 692–703 (2013)CrossRefGoogle Scholar
- 20.Vvedenskaya, N.D., Dobrushin, R.L., Karpelevich, F.I.: Queueing system with selection of the shortest of two queues: an asymptotic approach. Probl. Pereda. Inf. 32, 15–27 (1996)Google Scholar
- 21.Xiang, L., Kurkoski, B.: An improved analytical expression for write amplification in NAND flash. In: International Conference on Computing, Networking, and Communications (ICNC), pp. 497–501 (2012)Google Scholar