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Adaptive Query Scheduling in Key-Value Data Stores

  • Chen Xu
  • Mohamed A. Sharaf
  • Minqi Zhou
  • Aoying Zhou
  • Xiaofang Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7825)

Abstract

Large-scale distributed systems such as Dynamo at Amazon, PNUTS at Yahoo!, and Cassandra at Facebook, are rapidly becoming the data management platform of choice for most web applications. Those key-value data stores rely on data partitioning and replication to achieve higher levels of availability and scalability. Such design choices typically exhibit a trade-off in which data freshness is sacrificed in favor of reduced access latencies. Hence, it is indispensable to optimize resource allocation in order to minimize: 1) query tardiness, i.e., maximize Quality of Service (QoS), and 2) data staleness, i.e., maximize Quality of Data (QoD). That trade-off between QoS and QoD is further manifested at the local-level (i.e., replica-level) and is primarily shaped by the resource allocation strategies deployed for managing the processing of foreground user queries and background system updates. To this end, we propose the AFIT scheduling strategy, which allows for selective data refreshing and integrates the benefits of SJF-based scheduling with an EDF-like policy. Our experiments demonstrate the effectiveness of our method, which does not only strike a fine trade-off between QoS and QoD but also automatically adapts to workload settings.

Keywords

Data Item Query Execution Earliest Deadline First First Come First Serve Total Penalty 
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.

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References

  1. 1.
    Abadi, D.: Consistency tradeoffs in modern distributed database system design: Cap is only part of the story. IEEE Computer 45(2), 37–42 (2012)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Abbott, R.K., Garcia-Molina, H.: Scheduling real-time transactions: A performance evaluation. ACM Trans. Database Syst. 17(3), 513–560 (1992)CrossRefGoogle Scholar
  3. 3.
    Adelberg, B., Garcia-Molina, H., Kao, B.: Applying update streams in a soft real-time database system. In: SIGMOD Conference, pp. 245–256 (1995)Google Scholar
  4. 4.
    Becchetti, L., Leonardi, S., Marchetti-Spaccamela, A., Pruhs, K.R.: Online weighted flow time and deadline scheduling. In: Goemans, M.X., Jansen, K., Rolim, J.D.P., Trevisan, L. (eds.) APPROX-RANDOM 2001. LNCS, vol. 2129, pp. 36–47. Springer, Heidelberg (2001)Google Scholar
  5. 5.
    Cooper, B.F., Ramakrishnan, R., Srivastava, U., Silberstein, A., Bohannon, P., Jacobsen, H.-A., Puz, N., Weaver, D., Yerneni, R.: Pnuts: Yahoo!’s hosted data serving platform. PVLDB 1(2), 1277–1288 (2008)Google Scholar
  6. 6.
    DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., Vogels, W.: Dynamo: amazon’s highly available key-value store. In: SOSP, pp. 205–220 (2007)Google Scholar
  7. 7.
    Guirguis, S., Sharaf, M.A., Chrysanthis, P.K., Labrinidis, A., Pruhs, K.: Adaptive scheduling of web transactions. In: ICDE, pp. 357–368 (2009)Google Scholar
  8. 8.
    Labrinidis, A., Roussopoulos, N.: Exploring the tradeoff between performance and data freshness in database-driven web servers. VLDB J. 13(3), 240–255 (2004)CrossRefGoogle Scholar
  9. 9.
    Lakshman, A., Malik, P.: Cassandra: structured storage system on a p2p network. In: PODC, p. 5 (2009)Google Scholar
  10. 10.
    Qu, H., Labrinidis, A.: Preference-aware query and update scheduling in web-databases. In: ICDE, pp. 356–365 (2007)Google Scholar
  11. 11.
    Saito, Y., Shapiro, M.: Optimistic replication. ACM Comput. Surv. 37(1), 42–81 (2005)CrossRefGoogle Scholar
  12. 12.
    Sharaf, M.A., Chrysanthis, P.K., Labrinidis, A., Amza, C.: Optimizing I/O-intensive transactions in highly interactive applications. In: SIGMOD Conference, pp. 785–798 (2009)Google Scholar
  13. 13.
    Sharaf, M.A., Xu, C., Zhou, X.: Finding the silver lining for data freshness on the cloud: [extended abstract]. In: CloudDB, pp. 49–50 (2012)Google Scholar
  14. 14.
    Wada, H., Fekete, A., Zhao, L., Lee, K., Liu, A.: Data consistency properties and the trade-offs in commercial cloud storage: the consumers’ perspective. In: CIDR, pp. 134–143 (2011)Google Scholar
  15. 15.
    Zhu, Y., Sharaf, M.A., Zhou, X.: Scheduling with freshness and performance guarantees for web applications in the cloud. In: ADC (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Chen Xu
    • 1
  • Mohamed A. Sharaf
    • 2
  • Minqi Zhou
    • 1
  • Aoying Zhou
    • 1
  • Xiaofang Zhou
    • 2
  1. 1.East China Normal UniversityShanghaiChina
  2. 2.The Universtiy of QueenslandBrisbaneAustralia

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