Advertisement

A Data-Aware Energy-Saving Storage Management Strategy for On-Site Astronomical Observation at Dome A

  • Xiaoxiao Lu
  • Chao Sun
  • Ce Yu
  • Jizhou Sun
  • Ming Che
  • Zijun Xia
  • Zhaohui Shang
  • Yi Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11335)

Abstract

The high energy consumption of storage system has always been a thorny issue especially when power supply is limited, e.g. the case of astronomical observation at Dome A in the Antarctic. Many general-purpose energy-efficient strategies are designed to be applied in common data centers, which is still quite different from disk array at Dome A where extreme restrictions would influence the effect of solutions. Besides, maintaining the reliability is as important as saving energy because most of the time, nobody is there to solve the disk failure problem. In this paper we propose a data-aware energy-saving storage management strategy, named DAES, for astronomical observation whose purpose is to reduce the energy consumed while mitigating the loss of the reliability of disks. A metric named hit index is designed for each disk from the perspective of astronomy to manage the power state of disks more accurately. A customized file scheduler is also drafted to improve data layout dynamically. Simulation experiments show that it reduces energy consumption by up to 56.6% and cuts down the switches of power state by up to 66.8% compared with common energy-saving strategies.

Keywords

Astronomical observation data Disk array Disk reliability Energy efficient Storage system 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (11573019, 61602336), the Joint Research Fund in Astronomy (U1531111) under cooperative agreement between the National Natural Science Foundation of China (NSFC) and Chinese Academy of Sciences (CAS).

References

  1. 1.
    WD blue PC hard drives specifications data sheet (2015). https://www.wdc.com/content/dam/wdc/website/downloadable_assets/eng/spec_data_sheet/2879-771436.pdf. Accessed 31 July 2018
  2. 2.
    Al Assaf, M.M., Jiang, X., Abid, M.R., Qin, X.: Eco-storage: a hybrid storage system with energy-efficient informed prefetching. J. Signal Process. Syst. 72(3), 165–180 (2013)CrossRefGoogle Scholar
  3. 3.
    Boru, D., Kliazovich, D., Granelli, F., Bouvry, P., Zomaya, A.Y.: Energy-efficient data replication in cloud computing datacenters. Clust. Comput. 18(1), 385–402 (2015)CrossRefGoogle Scholar
  4. 4.
    Chai, Y., Du, Z., Bader, D.A., Qin, X.: Efficient data migration to conserve energy in streaming media storage systems. IEEE Trans. Parallel Distrib. Syst. 23(11), 2081–2093 (2012)CrossRefGoogle Scholar
  5. 5.
    Colarelli, D., Grunwald, D.: Massive arrays of idle disks for storage archives, pp. 1–11. IEEE Computer Society Press (2002)Google Scholar
  6. 6.
    Graham, M.J., Djorgovski, S.G., Mahabal, A., Donalek, C., Drake, A., Longo, G.: Data challenges of time domain astronomy. Distrib. Parallel Databases 30(5–6), 371–384 (2012)CrossRefGoogle Scholar
  7. 7.
    Jensen, R., Cornelis, C.: Fuzzy-rough nearest neighbour classification. In: Peters, J.F., Skowron, A., Chan, C.-C., Grzymala-Busse, J.W., Ziarko, W.P. (eds.) Transactions on Rough Sets XIII. LNCS, vol. 6499, pp. 56–72. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-18302-7_4CrossRefGoogle Scholar
  8. 8.
    Lee, D.K., Koh, K.: PDC-NH: popular data concentration on NAND flash and hard disk drive. In: 2009 10th IEEE/ACM International Conference on Grid Computing, pp. 196–200. IEEE (2009)Google Scholar
  9. 9.
    Luo, X., Xin, G., Wang, Y., Zhang, Z., Wang, H.: Superset: a non-uniform replica placement strategy towards perfect load balance and fine-grained power proportionality. Clust. Comput. 18(3), 1127–1140 (2015)CrossRefGoogle Scholar
  10. 10.
    Manzanares, A., Bellam, K., Qin, X.: A prefetching scheme for energy conservation in parallel disk systems. In: IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2008, pp. 1–5. IEEE (2008)Google Scholar
  11. 11.
    Manzanares, A., Qin, X., Ruan, X., Yin, S.: PRE-BUD: prefetching for energy-efficient parallel I/O systems with buffer disks. ACM Trans. Storage (TOS) 7(1), 3 (2011)Google Scholar
  12. 12.
    Manzanares, A., et al.: Energy efficient prefetching with buffer disks for cluster file systems. In: 2010 39th International Conference on Parallel Processing (ICPP), pp. 404–413. IEEE (2010)Google Scholar
  13. 13.
    Manzanres, A., Ruan, X., Yin, S., Nijim, M., Luo, W., Qin, X.: Energy-aware prefetching for parallel disk systems: algorithms, models, and evaluation. In: Eighth IEEE International Symposium on Network Computing and Applications, NCA 2009, pp. 90–97. IEEE (2009)Google Scholar
  14. 14.
    Nijim, M., Qin, X., Yin, S., Ruan, X., Manzanres, A., Luo, W.: Energy-aware prefetching for parallel disk systems: algorithms, models, and evaluation. In: 2009 Eighth IEEE International Symposium on Network Computing and Applications, pp. 90–97 (2009)Google Scholar
  15. 15.
    Ou, J., Shu, J., Lu, Y., Yi, L., Wang, W.: EDM: an endurance-aware data migration scheme for load balancing in SSD storage clusters. In: 2014 IEEE 28th International Parallel and Distributed Processing Symposium, pp. 787–796. IEEE (2014)Google Scholar
  16. 16.
    Pinheiro, E., Bianchini, R.: Energy conservation techniques for disk array-based servers, pp. 369–379. ACM (2014)Google Scholar
  17. 17.
    Shehabi, A., et al.: United states data center energy usage report (2016)Google Scholar
  18. 18.
    Sun, C., et al.: MCS-B: an energy efficient storage system for astronomical observation data based on logical block replacement strategy. In: 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), pp. 198–205. IEEE (2017)Google Scholar
  19. 19.
    Yan, J., Yu, C., Sun, C., Shang, Z., Hu, Y., Feng, J., Sun, J., Xiao, J.: Optimized data layout for spatio-temporal data in time domain astronomy. In: Ibrahim, S., Choo, K.-K.R., Yan, Z., Pedrycz, W. (eds.) ICA3PP 2017. LNCS, vol. 10393, pp. 431–440. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-65482-9_30CrossRefGoogle Scholar
  20. 20.
    Yuan, X., et al.: The AST3 project: Antarctic survey telescopes for Dome A. In: Ground-based and Airborne Telescopes V, vol. 9145, p. 91450F. International Society for Optics and Photonics (2014)Google Scholar
  21. 21.
    Zhang, G., Chiu, L., Dickey, C., Liu, L., Muench, P., Seshadri, S.: Automated lookahead data migration in SSD-enabled multi-tiered storage systems. In: 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), pp. 1–6. IEEE (2010)Google Scholar
  22. 22.
    Zhao, X., Li, Z., Zeng, L.: FDTM: block level data migration policy in tiered storage system. In: Ding, C., Shao, Z., Zheng, R. (eds.) NPC 2010. LNCS, vol. 6289, pp. 76–90. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15672-4_8CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.National Supercomputer Center in TianjinTianjinChina
  3. 3.National Astronomical ObservatoriesChinese Academy of SciencesBeijingChina

Personalised recommendations