Abstract
When originally introduced, flash based solid state drives (SSD) exhibited a very high random read throughput with low sub-millisecond latencies. However, in addition to their steep prices, SSDs suffered from slow write rates and reliability concerns related to cell wear. For these reasons, they were relegated to a niche status in the consumer and personal computer market. Since then, several architectural enhancements have been introduced that led to a substantial increase in random write operations as well as a reasonable improvement in reliability. From a purely performance point of view, these high I/O rates and improved reliability make the SSDs an ideal choice for enterprise On-Line Transaction Processing (OLTP) applications. However, from a price/performance point of view, the case for SSDs may not be clear. Enterprise class SSD Price/GB, continues to be at least 10x higher than conventional magnetic hard disk drives (HDD) despite considerable drop in Flash chip prices.
We show that a complete replacement of traditional HDDs with SSDs is not cost effective. Further, we demonstrate that the most cost efficient use of SSDs for OLTP workloads is as an intermediate persistent cache that sits between conventional HDDs and memory, thus forming a three-level memory hierarchy. We also discuss two implementations of such cache: hardware or software. For the software approach, we discuss our implementation of such a cache in an in-house database system. We also describe off-the shelf hardware solutions. We will develop a Total Cost of Ownership (TCO) model for All-SSD and All-HDD configurations. We will also come up with a modified OLTP benchmark that can scale IO density to validate this model. We will also show how such SSD cache implementations could increase the performance of OLTP applications while reducing the overall system cost.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lee, S.-W., Moon, B., Park, C., Kim, J.-M., Kim, S.-W.: A Case for Flash Memory SSD in Enterprise Database Applications. In: Proceedings of the ACM SIGMOD, pp. 1075–1086 (2008)
Tsirogiannis, D., Harizopoulos, S., Shah, M.A., Wiener, J.L., Graefe, G.: Query processing techniques for solid state drives. In: Proceedings of the 35th SIGMOD International Conference on Management of Data, Providence, Rhode Island, USA, June 29-July 02 (2009)
Janukowicz, J., Reinsel, D., Rydning, J.: Worldwide solid state drive 2008-2012 forecast and analysis. IDC, Technical Report 212736 (2008)
Hetzler, S.R.: The storage chasm: Implications for the future of HDD and solid state storage (December 2008), http://www.idema.org
Koltsidas, I., Viglas, S.D.: Flashing up the storage layer. Proc. VLDB Endow. 1(1), 514–525 (2008)
Kgil, T., Roberts, D., Mudge, T.: Improving NAND Flash Based Disk Caches. In: Proceedings of the 35th International Symposium on Computer Architecture, June 21-25, pp. 327–338. IEEE Computer Society, Washington (2008)
Miller, E.L., Brandt, S.A., Long, D.D.: HeRMES: High-Performance Reliable MRAM-Enabled Storage. In: Proceedings of the Eighth Workshop on Hot Topics in Operating Systems, HOTOS, May 20-22, p. 95. IEEE Computer Society, Washington (2001)
Lin, S., Zeinalipour-Yazti, D., Kalogeraki, V., Gunopulos, D., Najjar, W.A.: Efficient indexing data structures for flash-based sensor devices. Trans. Storage 2(4), 468–503 (2006)
Moshayedi, M., Wilkison, P.: Enterprise SSDs. Queue 6(4), 32–39 (2008)
Narayanan, D., Thereska, E., Donnelly, A., Elnikety, S., Rowstron, A.: Migrating server storage to SSDs: analysis of tradeoffs. In: Proceedings of the 4th ACM European Conference on Computer Systems, EuroSys 2009, Nuremberg, Germany, April 01 - 03, pp. 145–158. ACM, New York (2009)
Adaptec MaxIQTM SSD Cache, http://www.adaptec.com/en-US/products/CloudComputing/MAXIQ/
Lee, S.-W., Moon, B.: Design of Flash-based DBMS: an In-Page Logging Approach. In: Proceedings of the ACM SIGMOD, pp. 55–66 (2007)
Park, S.-Y., Jung, D., Kang, J.-U., Kim, J.-S., Lee, J.: CFLRU: a Replacement Algorithm for Flash Memory. In: The 2006 International Conference on Compilers, Architecture and Synthesis for Embedded Systems (CASES 2006), pp. 234–241 (October 2006)
Poess, M., Nambiar, R.O.: Energy Cost, The Key Challenge of Today’s Data Centers: A Power Consumption Analysis of TPC-C Results. In: Proceedings of VLDB (2008)
Intel X25 SSD drives, http://www.intel.com/design/flash/nand/extreme/index.htm
Kavalanekar, S., Narayanan, D., Sankar, S., Thereska, E., Vaid, K., Worthington, B.: Measuring Database Performance in Online Services: A Trace-Based Approach. In: Nambiar, R., Poess, M. (eds.) TPCTC 2009. LNCS, vol. 5895, pp. 132–145. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Khessib, B.M., Vaid, K., Sankar, S., Zhang, C. (2011). Using Solid State Drives as a Mid-Tier Cache in Enterprise Database OLTP Applications. In: Nambiar, R., Poess, M. (eds) Performance Evaluation, Measurement and Characterization of Complex Systems. TPCTC 2010. Lecture Notes in Computer Science, vol 6417. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18206-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-18206-8_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18205-1
Online ISBN: 978-3-642-18206-8
eBook Packages: Computer ScienceComputer Science (R0)