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Toward FPGA-Based Semantic Caching for Accelerating Data Analysis with Spark and HDFS

  • Marouan Maghzaoui
  • Laurent d’OrazioEmail author
  • Julien Lallet
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1040)

Abstract

With the increase of data, traditional methods of data processing have become time and power inefficient. As enhancement, we propose a new accelerated architecture for querying big Databases. This architecture combines the advantages of the HDFS for the management of huge amount of data and the fast processing of queries of Spark SQL. It also benefits of the processing efficiency of the hardware acceleration of FPGAs and of the semantic caching architecture to process recently used data stored in the cache.

Keywords

FPGA Spark HDFS Semantic caching 

References

  1. 1.
    Soomro, T.R., Shoro, A.G.: Big data analysis: Apache spark perspective. Glob. J. Comput. Sci. Technol. (2015)Google Scholar
  2. 2.
    Armbrust, M., et al.: Spark SQL: relational data processing in spark. In: SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, pp. 1383–1394 (2015)Google Scholar
  3. 3.
    Bansod, A.: Efficient big data analysis with apache spark in HDFS. Int. J. Eng. Adv. Technol. (IJEAT) 4(6), 313–316 (2015) Google Scholar
  4. 4.
    Becher, A., Ziener, D., Meyer-Wegener, K., Teich, J.: A co-design approach for accelerated SQL query processing via FPGA-based data filtering. In: International Conference on Field Programmable Technology (FPT), Queenstown, New Zealand, pp. 192–195 (2015)Google Scholar
  5. 5.
    Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mobile Netw. Appl. (MONET) 19(2), 171–209 (2014) CrossRefGoogle Scholar
  6. 6.
    Cisco Global Cloud: Cisco global cloud index: Forecast and methodology, 2016–2021 white paper. Technical report, Cisco (2010)Google Scholar
  7. 7.
    Dar, S., Franklin, M.J., Jónsson, B.T., Srivastava, D., Tan, M.: Semantic data caching and replacement. In: International Conference on Very Large Data Bases (VLDB), Mumbai (Bombay), India, pp. 330–341 (1996)Google Scholar
  8. 8.
    Dennl, C., Ziener, D., Teich, J.: On-the-fly composition of FPGA-based SQL query accelerators using a partially reconfigurable module library. In: International Symposium on Field-Programmable Custom Computing Machines (FCCM), Toronto, Ontario, Canada, pp. 45–52 (2012)Google Scholar
  9. 9.
    Esmaeilzadeh, H., Blem, E.R., Amant, R.S., Sankaralingam, K., Burger, D.: Dark silicon and the end of multicore scaling. IEEE Micro 32(3), 122–134 (2012)CrossRefGoogle Scholar
  10. 10.
    Ghemawat, S., Gobioff, H., Leung, S.-T.: The Google file system. In: Symposium on Operating Systems Principles (SOSP), Bolton Landing, NY, USA, pp. 29–43 (2003)Google Scholar
  11. 11.
    Jacobsen, M., Richmond, D., Hogains, M., Kastner, R.: RIFFA 2.1: a reusable integration framework for FPGA accelerators. ACM Trans. Reconfig. Technol. Syst. 8(4), 22:1–22:23 (2015)CrossRefGoogle Scholar
  12. 12.
    Manikandan, S.G., Ravi, S.: Big data analysis using Apache Hadoop. In: International Conference on IT Convergence and Security (ICITCS), Beijing, China (2014)Google Scholar
  13. 13.
    Ross, P.E.: Why CPU frequency stalled. IEEE Spectr. 45(4), 72 (2008)CrossRefGoogle Scholar
  14. 14.
    Sidler, D., István, Z., Owaida, M., Kara, K., Alonso, G.: doppioDB: a hardware accelerated database. In: International Conference on Management of Data, SIGMOD Conference 2017, Chicago, IL, USA, pp. 1659–1662 (2017)Google Scholar
  15. 15.
    Teubner, J.: FPGAs for data processing: current state. Inf. Technol. (IT) 59(3), 125 (2017)Google Scholar
  16. 16.
    Theis, T.N., Wong, H.P.: The end of Moore’s law: a new beginning for information technology. Comput. Sci. Eng. 19(2), 41–50 (2017)CrossRefGoogle Scholar
  17. 17.
    Vancea, A., Stiller, B.: CoopSC: a cooperative database caching architecture. In: 2010 International Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises (WETICE), Larissa, Greece, pp. 223–228 (2010)Google Scholar
  18. 18.
    Ziener, D., et al.: FPGA-based dynamically reconfigurable SQL query processing. ACM Trans. Reconfig. Technol. Syst. (TRETS) 9(4), 25:1–25:24 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Marouan Maghzaoui
    • 1
  • Laurent d’Orazio
    • 2
    Email author
  • Julien Lallet
    • 1
  1. 1.Nokia Bell LabsNozayFrance
  2. 2.Univ Rennes, CNRS, IRISALannionFrance

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