Abstract
Erasure coding is widely used in storage systems since it can offer higher reliability at lower redundancy than data replication. However, erasure-coded storage systems have to perform a partial write to an entire erasure coding group for a small write, which causes a time-consuming write-after-read. This paper presents an efficient data delta based hybrid write scheme, named DABRI, which supports fast partial writes for erasure-coded storage systems. DABRI uses data deltas that are the differences between latest data values and original data values to bypass the computation of parity deltas and the read of old data. For a series of n partial writes to the same data, DABRI performs log-based data and parity updates for the first write, and takes in-place data updates and log-based parity updates for the last \(n-1\) writes. This enables new data to be written into data nodes and parity nodes in parallel, and the overhead of data reads and parity updates can be mitigated. Based on DABRI, we design and implement an erasure-coded prototype storage system that can deliver high-performance for small-write-intensive applications. Experimental results on real-world traces show that DABRI can successfully improve the I/O throughput by up to 77.41%, compared with the state-of-the-art cited in this paper.
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References
Chan, J.C., Ding, Q., Lee, P.P., Chan, H.H.: Parity logging with reserved space: towards efficient updates and recovery in erasure-coded clustered storage. In: 12th USENIX Conference on File and Storage Technologies, pp. 163–176 (2014)
Chen, Y.L., Mu, S., Li, J., Huang, C., Li, J., Ogus, A., Phillips, D.: Giza: erasure coding objects across global data centers. In: 2017 USENIX Annual Technical Conference, pp. 539–551 (2017)
Ghemawat, S., Gobioff, H., Leung, S.T.: The google file system. In: Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, pp. 29–43 (2003)
Hu, Y., Cheng, L., Yao, Q., Lee, P.P., Wang, W., Chen, W.: Exploiting combined locality for wide-stripe erasure coding in distributed storage. In: 19th USENIX Conference on File and Storage Technologies, pp. 233–248 (2021)
Huang, C., et al.: Erasure coding in windows azure storage. In: 2012 USENIX Annual Technical Conference, pp. 15–26 (2012)
Huang, J., Xia, J., Qin, X., Cao, Q., Xie, C.: Optimization of small updates for erasure-coded in-memory stores. Comput. J. 62(6), 869–883 (2019)
Jin, C., Feng, D., Jiang, H., Tian, L.: RAID6L: a log-assisted raid6 storage architecture with improved write performance. In: 2011 IEEE 27th Symposium on Mass Storage Systems and Technologies, pp. 1–6. IEEE (2011)
Kadekodi, S., Rashmi, K., Ganger, G.R.: Cluster storage systems gotta have HeART: improving storage efficiency by exploiting disk-reliability heterogeneity. In: 17th USENIX Conference on File and Storage Technologies, pp. 345–358 (2019)
Li, X., Li, R., Lee, P.P., Hu, Y.: OpenEC: toward unified and configurable erasure coding management in distributed storage systems. In: 17th USENIX Conference on File and Storage Technologies, pp. 331–344 (2019)
Plank, J.S., Greenan, K.M., Miller, E.L.: Screaming fast Galois field arithmetic using Intel SIMD instructions. In: 11th USENIX Conference on File and Storage Technologies, pp. 299–306 (2013)
Shen, J., Zhang, K., Gu, J., Zhou, Y., Wang, X.: Efficient scheduling for multi-block updates in erasure coding based storage systems. IEEE Trans. Comput. 67(4), 573–581 (2017)
Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The Hadoop distributed file system. In: 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies, pp. 1–10. IEEE (2010)
Silberstein, M., Ganesh, L., Wang, Y., Alvisi, L., Dahlin, M.: Lazy means smart: reducing repair bandwidth costs in erasure-coded distributed storage. In: Proceedings of International Conference on Systems and Storage, pp. 1–7 (2014)
Subedi, P., Huang, P., Young, B., He, X.: FINGER: a novel erasure coding scheme using fine granularity blocks to improve Hadoop write and update performance. In: 2015 IEEE International Conference on Networking, Architecture and Storage, pp. 255–264. IEEE (2015)
Wang, Y., Pei, X., Ma, X., Xu, F.: TA-Update: an adaptive update scheme with tree-structured transmission in erasure-coded storage systems. IEEE Trans. Parallel Distrib. Syst. 29(8), 1893–1906 (2017)
Wei, B., et al.: A self-tuning client-side metadata prefetching scheme for wide area network file systems. Sci. China Inf. Sci. 65(3), 1–17 (2021). https://doi.org/10.1007/s11432-019-2833-1
Wei, B., Xiao, L., Zhou, B., Qin, G., Yan, B., Huo, Z.: Fine-grained management of I/O optimizations based on workload characteristics. Front. Comput. Sci. 15(3), 1–14 (2021)
Ye, L., Feng, D., Hu, Y., Wei, X.: Hybrid codes: flexible erasure codes with optimized recovery performance. ACM Trans. Storage 16(4), 1–26 (2020)
Zhou, T., Tian, C.: Fast erasure coding for data storage: a comprehensive study of the acceleration techniques. ACM Trans. Storage 16(1), 1–24 (2020)
Acknowledgment
This work was supported in part by the China Postdoctoral Science Foundation under Grant No. 2021M690733, the Key-Area Research and Development Program of Guangdong Province under Grant 2019B010121001, and the National Natural Science Foundation of China under Grant No. 62072118.
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Huang, Q., Chen, H., Wei, B., Wu, J., Xiao, L. (2022). Data Delta Based Hybrid Writes for Erasure-Coded Storage Systems. In: Cérin, C., Qian, D., Gaudiot, JL., Tan, G., Zuckerman, S. (eds) Network and Parallel Computing. NPC 2021. Lecture Notes in Computer Science(), vol 13152. Springer, Cham. https://doi.org/10.1007/978-3-030-93571-9_14
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DOI: https://doi.org/10.1007/978-3-030-93571-9_14
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