Advertisement

Energy Efficiency Is Not Enough, Energy Proportionality Is Needed!

  • Theo Härder
  • Volker Hudlet
  • Yi Ou
  • Daniel Schall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6637)

Abstract

Due to the energy consumption/resource utilization characteristics of todays centralized DB servers, the fastest configuration is also the most energy-efficient one. Extensive use of SSDs alone cannot enable a fundamental change of this overall picture, because the storage-related energy consumption is typically only a little fraction of the overall energy budget. Even, when this storage-related share is (almost) completely reduced by optimized flash-aware buffer management, the saving effect achieved may be limited by less than ~10%. Therefore, we have designed a cluster of wimpy computing nodes called WattDB, where the individual nodes are dynamically attached and detached to the cluster on demand – depending on the current workload needs –, thereby aiming at energy-proportional DB management.

Keywords

Query Processing Computing Node System Utilization Cluster Coordination Energy Proportionality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Boncz, P.A., Manegold, S., Kersten, M.L.: Database Architecture Evolution: Mammals Flourished long before Dinosaurs became Extinct. PVLDB 2(2), 1648–1653 (2009)Google Scholar
  2. 2.
    Greenplum. Driving the Future of Data Warehousing and Analytics (2009), http://www.greenplum.com/
  3. 3.
    VoltDB. Fast, Scalable, Open-Source SQL DBMS with ACID (2010), http://voltdb.com/
  4. 4.
    Plattner, H.: SanssouciDB: An In-Memory Database for Processing Enterprise Workloads. In: Proc. BTW. LNI - P, vol. 180, pp. 2–21 (2011)Google Scholar
  5. 5.
    Härder, T., Schmidt, K., Ou, Y., Bächle, S.: Towards Flash Disk Use in Databases - Keeping Performance While Saving Energy? In: Proc. BTW. LNI - P, vol. 144, pp. 167–186 (2009)Google Scholar
  6. 6.
    Hudlet, V., Schall, D.: SSD!= SSD - An Empirical Study to Identify Common Properties and Type-specific Behavior. In: Proc. BTW. LNI - P, vol. 180, pp. 430–441 (2011)Google Scholar
  7. 7.
    Ou, Y., Härder, T., Schall, D.: Performance and Power Evaluation of Flash-Aware Buffer Algorithms. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010. LNCS, vol. 6261, pp. 183–197. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Bouganim, L., Jónsson, B.T., Bonnet, P.: uFLIP: Understanding Flash IO Patterns. In: CIDR (2009)Google Scholar
  9. 9.
    Graefe, G.: The five-minute rule 20 years later (and how flash memory changes the rules). Commun. ACM 52(7), 48–59 (2009)CrossRefGoogle Scholar
  10. 10.
    Schall, D., Hudlet, V., Härder, T.: Enhancing Energy Efficiency of Database Applications Using SSDs. In: C3S2E, pp. 1–9 (2010)Google Scholar
  11. 11.
    Effelsberg, W., Härder, T.: Principles of Database Buffer Management. ACM TODS 9(4), 560–595 (1984)CrossRefGoogle Scholar
  12. 12.
    Ou, Y., Härder, T., Jin, P.: CFDC: A Flash-Aware Buffer Management Algorithm for Database Systems. In: Catania, B., Ivanović, M., Thalheim, B. (eds.) ADBIS 2010. LNCS, vol. 6295, pp. 435–449. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Park, S., Jung, D., et al.: CFLRU: a Replacement Algorithm for Flash Memory. In: CASES, pp. 234–241 (2006)Google Scholar
  14. 14.
    Jung, H., Shim, H., et al.: LRU-WSR: Integration of LRU and Writes Sequence Reordering for Flash Memory. Trans. on Cons. Electr. 54(3), 1215–1223 (2008)CrossRefGoogle Scholar
  15. 15.
    Seo, D., Shin, D.: Recently-evicted-first Buffer Replacement Policy for Flash Storage Devices. Trans. on Cons. Electr. 54(3), 1228–1235 (2008)CrossRefGoogle Scholar
  16. 16.
    Tsirogiannis, D., Harizopoulos, S., Shah, M.A.: Analyzing the Energy Efficiency of a Database Server. In: SIGMOD, pp. 231–242 (2010)Google Scholar
  17. 17.
    Barroso, L.A., Hölzle, U.: The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Synthesis Lectures on Computer Architecture. Morgan & Claypool Publishers (2009)Google Scholar
  18. 18.
    Albers, S.: Energy-efficient Algorithms. Commun. ACM 53(5), 86–96 (2010)CrossRefGoogle Scholar
  19. 19.
    Rahm, E.: Evaluation of Closely Coupled Systems for High-Performance Database Processing. In: ICDCS, pp. 301–310 (1993)Google Scholar
  20. 20.
    Szalay, A.S., Bell, G.C., Huang, H.H., Terzis, A., White, A.: Low-power Amdahl-balanced Blades for Data-intensive Computing. SIGOPS Oper. Syst. Rev. 44Google Scholar
  21. 21.
    Li, X., Li, Z., Zhou, Y., Adve, S.: Performance-Directed Energy Management for Main Memory and Disks. Trans. Storage 1, 346–380 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Theo Härder
    • 1
  • Volker Hudlet
    • 1
  • Yi Ou
    • 1
  • Daniel Schall
    • 1
  1. 1.Databases and Information Systems GroupUniversity of KaiserslauternGermany

Personalised recommendations