CLDM: A Cache Cleaning Algorithm for Host Aware SMR Drives

  • Wenguo Liu
  • Lingfang ZengEmail author
  • Dan Feng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11334)


Host aware SMR (HA-SMR) drives can effectively increase the capacity of hard disk drives. However, the cache cleaning algorithms implemented in the HA-SMR drives need to be improved. Current cache cleaning algorithms do not consider the characteristics of applications and usually bring too much data migration. In this paper, we propose a new cache cleaning algorithm called CLDM, which takes the characteristics of applications into account. It uses the “zone heat” to reflect the access frequency in the disk cache of a zone, and the “zone data migration” to reflect the data migration of a zone when cache cleaning is performed on the zone. When CLDM is performed, it first computes the “zone heat” for each zone which is currently buffered in the disk cache, and then computes the “average zone heat” for all the buffered zones. After that, CLDM computes the “zone data migration” for each buffered zone, and sorts all the buffered zones in the ascending order of their “zone data migration”s. CLDM first cleans the zones which satisfy the condition “the zone heat of a zone is less than the average zone heat”. And then it cleans the zones with less “zone data migration”s. Experimental results show that CLDM can effectively reduce the amount of migrated data during both the cache cleaning process and garbage collection process, and improve the performance of HA-SMR drives.


Host aware SMR Disk cache cleaning Zone heat Zone data migration 


  1. 1.
    Wood, R., Williams, M., Kavcic, A., Miles, J.: The feasibility of magnetic recording at 10 terabits per square inch on conventional media. IEEE Trans. Magn. 45(2), 917–923 (2009)CrossRefGoogle Scholar
  2. 2.
    Cassuto, Y., Sanvido, M.A.A., Guyot, C., Hall, D.R., Bandic, Z.Z.: Indirection systems for shingled-recording disk drives. In Proceedings of 26th IEEE Symposium on Mass Storage Systems and Technologies (MSST), May 2010, pp. 1–14 (2010)Google Scholar
  3. 3.
    Venkataraman, K.S., Dong, G., Zhang, T.: Techniques mitigating updateinduced latency overhead in shingled magnetic recording. IEEE Trans. Magn. 48(5), 1899–1905 (2012)CrossRefGoogle Scholar
  4. 4.
    Amer, A., Holliday, J., Long, D.D.E., Miller, E.L., Paris, J.-F., Schwarz, T.: Data management and layout for shingled magnetic recording. IEEE Trans. Magn. 47(10), 3691–3697 (2011)CrossRefGoogle Scholar
  5. 5.
    Feldman, T., Gibson, G.: Shingled magnetic recording areal density increase requires new data management. USENIX; Login: Mag. 38(3) (2013)Google Scholar
  6. 6.
    INCITS T10 Technical Committee, Information technology - zoned block commands (ZBC). Draft Standard T10/BSR INCITS 536. American National Standard Institute Inc., December 2015Google Scholar
  7. 7.
    INCITS T13 Technical Committee, Information technology - zoned device ATA command set. Draft Standard T13/BSR INCITS 537. American National Standard Institute Inc., December 2015Google Scholar
  8. 8.
    Wu, F., Yang, M.-C., Fan, Z., Zhang, B., Ge, X., Du, D.H.C.: Evaluating host aware SMR drives. In: Proceedings of the 8th USENIX Workshop on Hot Topics in Storage and File Systems, pp. 31–35. USENIX Association, June 2016Google Scholar
  9. 9.
    Fenggang, W., Fan, Z., Yang, M.-C., Zhang, B., Ge, X., Du, D.H.C.: Performance evaluation of host aware shingled magnetic recording (HA-SMR) drives. IEEE Trans. Comput. 66(11), 1932–1945 (2017)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Aghayev, A., Shafaei, M., Desnoyers, P.: Skylight - a window on shingled disk operation. ACM Trans. Storage (TOS) 11(4), 16–30 (2015)Google Scholar
  11. 11.
    Bucy, J.S., Schindler, J., Schlosser, S.W., Ganger, G.R.: The DiskSim simulation environment version 4.0 reference manual. Technical report. Carnegie Mellon University, May 2008Google Scholar
  12. 12.
    Narayanan, D., Donnelly, A., Rowstron, A.: Write off-loading: practical power management for enterprise storage. ACM Trans. Storage 4(3), 10–23 (2008)CrossRefGoogle Scholar
  13. 13.
    Jones, S.N., Amer, A., Miller, E.L., Long, D.D.E., Pitchumani, R., Strong, C.R.: Classifying data to reduce long-term data movement in shingled write disks. ACM Trans. Storage 12(1), 2–17 (2016)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Key Laboratory of Information Storage System, MoE Wuhan National Laboratory for Optoelectronics School of ComputerHuazhong University of Science and TechnologyWuhanChina

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