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Distributed and Parallel Databases

, Volume 32, Issue 4, pp 535–581 | Cite as

Quality-aware schedulers for weak consistency key-value data stores

  • Chen Xu
  • Mohamed A. Sharaf
  • Xiaofang Zhou
  • Aoying ZhouEmail author
Article

Abstract

Highly distributed NoSQL key-value data stores are rapidly becoming the favorite choice for hosting modern web applications. Such platforms rely on data partitioning, replication and relaxed consistency to achieve high levels of performance and scalability. However, these design choices often exhibit a trade-off between latency (i.e., Quality of Service (QoS)) and consistency (i.e., Quality of Data (QoD)). In this work, in addition to latency-based SLAs, we also adopt the application tolerance to data staleness as another requirement determining the end-user satisfaction and our goal is to strike a fine balance between both the QoS and QoD provided to the end-user. Towards achieving that goal, we propose a suite of quality-aware schedulers for efficiently allocating the necessary computational resources between the foreground user-queries and the background system-updates at data store nodes. This suite of schedulers features our proposed Freshness/Tardiness (FIT) mechanism, which introduces a novel selective approach for scheduling the execution of queries and updates. Our experimental results show that FIT provides significant improvements in balancing the trade-off between QoS and QoD under both the state-transfer and operation-transfer update propagation models employed in current key-value data stores.

Keywords

NoSQL Key-value data stores Distributed database Scheduling Consistency SLA FLA Quality of data Quality of service 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions. This work is partially supported by the Shanghai Knowledge Services Platform for Trustworthy Internet of Things (ZF1213), grants from Australian Research Council (DP110102777, DP120102829), MoST of China (2010CB731402, 2012AA011001, 2012AA011003) and NSFC grants (61021004, 60925008).

References

  1. 1.
    Abadi, D.: Consistency tradeoffs in modern distributed database system design: cap is only part of the story. Computer 45(2), 37–42 (2012) MathSciNetCrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    Agrawal, D., Abbadi, A.E., Antony, S., Das, S.: Data management challenges in cloud computing infrastructures. In: DNIS, pp. 1–10 (2010) Google Scholar
  4. 4.
    Aiyer, A.S., Anderson, E., Li, X., Shah, M.A., Wylie, J.J.: Consistability: describing usually consistent systems. In: HotDep (2008) Google Scholar
  5. 5.
    Bailis, P., Venkataraman, S., Franklin, M.J., Hellerstein, J.M., Stoica, I.: Probabilistically bounded staleness for practical partial quorums. Proc. VLDB Endow. 5(8), 776–787 (2012) CrossRefGoogle Scholar
  6. 6.
    Bateni, M., Golab, L., Hajiaghayi, M.T., Karloff, H.J.: Scheduling to minimize staleness and stretch in real-time data warehouses. In: SPAA, pp. 29–38 (2009) CrossRefGoogle Scholar
  7. 7.
    Bouzeghoub, M., Peralta, V.: A framework for analysis of data freshness. In: IQIS, pp. 59–67 (2004) CrossRefGoogle Scholar
  8. 8.
    Brinch Hansen, P. (ed.): Classic Operating Systems: from Batch Processing to Distributed Systems. Springer, New York (2000) Google Scholar
  9. 9.
    Buttazzo, G.C., Spuri, M., Sensini, F.: Value vs. deadline scheduling in overload conditions. In: RTSS, pp. 90–99 (1995) Google Scholar
  10. 10.
    Cattell, R.: Scalable sql and nosql data stores. SIGMOD Rec. 39(4), 12–27 (2010) CrossRefGoogle Scholar
  11. 11.
    Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., Gruber, R.E., Bigtable: A distributed storage system for structured data. ACM Trans. Comput. Syst. 26(2) (2008) Google Scholar
  12. 12.
    Chen, X., Zhang, X.: Coordinated data prefetching for web contents. Comput. Commun. 28(17), 1947–1958 (2005) CrossRefGoogle Scholar
  13. 13.
    Chi, Y., Moon, H.J., Hacigümüs, H.: Icbs: incremental cost-based scheduling under piecewise linear slas. Proc. VLDB Endow. 4(9), 563–574 (2011) CrossRefGoogle Scholar
  14. 14.
    Chi, Y., Moon, H.J., Hacigümüs, H., Tatemura, J.: Sla-tree: a framework for efficiently supporting sla-based decisions in cloud computing. In: EDBT, pp. 129–140 (2011) Google Scholar
  15. 15.
    Cho, J., Garcia-Molina, H.: Synchronizing a database to improve freshness. In: SIGMOD Conference, pp. 117–128 (2000) Google Scholar
  16. 16.
    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. Proc. VLDB Endow. 1(2), 1277–1288 (2008) CrossRefGoogle Scholar
  17. 17.
    Cooper, B.F., Silberstein, A., Tam, E., Ramakrishnan, R., Sears, R.: Benchmarking cloud serving systems with ycsb. In: SoCC, pp. 143–154 (2010) Google Scholar
  18. 18.
    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) CrossRefGoogle Scholar
  19. 19.
    Gilbert, S., Lynch, N.A.: Brewer’s conjecture and the feasibility of consistent, available, partition-tolerant web services. SIGACT News 33(2), 51–59 (2002) CrossRefGoogle Scholar
  20. 20.
    Golab, L., Johnson, T., Shkapenyuk, V.: Scheduling updates in a real-time stream warehouse. In: ICDE, pp. 1207–1210 (2009) Google Scholar
  21. 21.
    Golab, W.M., Li, X., Shah, M.A.: Analyzing consistency properties for fun and profit. In: PODC, pp. 197–206 (2011) Google Scholar
  22. 22.
    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
  23. 23.
    Guo, H., Larson, P., Ramakrishnan, R., Goldstein, J.: Relaxed currency and consistency: how to say “good enough” in sql. In: SIGMOD Conference, pp. 815–826 (2004) Google Scholar
  24. 24.
    Gustafsson, T., Hansson, J.: Dynamic on-demand updating of data in real-time database systems. In: SAC, pp. 846–853 (2004) Google Scholar
  25. 25.
    Haritsa, J.R., Livny, M., Carey, M.J.: Earliest deadline scheduling for real-time database systems. In: RTSS, pp. 232–242 (1991) Google Scholar
  26. 26.
    Haritsa, J.R., Carey, M.J., Livny, M.: Value-based scheduling in real-time database systems. VLDB J. 2(2), 117–152 (1993) CrossRefGoogle Scholar
  27. 27.
    Hewitt, E.: Cassandra: the Definitive Guide. O’Reilly Media, Sebastopol (2010) Google Scholar
  28. 28.
    Kang, K.D., Son, S.H., Stankovic, J.A., Abdelzaher, T.F.: A qos-sensitive approach for timeliness and freshness guarantees in real-time databases. In: ECRTS, pp. 203–212 (2002) Google Scholar
  29. 29.
    Kang, K.D., Son, S.H., Stankovic, J.A.: Managing deadline miss ratio and sensor data freshness in real-time databases. IEEE Trans. Knowl. Data Eng. 16(10), 1200–1216 (2004) CrossRefGoogle Scholar
  30. 30.
    Labrinidis, A., Roussopoulos, N.: Update propagation strategies for improving the quality of data on the web. In: VLDB, pp. 391–400 (2001) Google Scholar
  31. 31.
    Labrinidis, A., Qu, H., Xu, J.: Quality contracts for real-time enterprises. In: BIRTE, pp. 143–156 (2006) Google Scholar
  32. 32.
    Lakshman, A., Malik, P.: Cassandra: a decentralized structured storage system. Oper. Syst. Rev. 44(2), 35–40 (2010) CrossRefGoogle Scholar
  33. 33.
    Li, W., Zhang, W., Liberatore, V., Penkrot, V., Beaver, J., Sharaf, M.A., Roychowdhury, S., Chrysanthis, P.K., Pruhs, K.: An optimized multicast-based data dissemination middleware. In: ICDE, pp. 762–764 (2003) Google Scholar
  34. 34.
    Pang, H., Carey, M.J., Livny, M.: Multiclass query scheduling in real-time database systems. IEEE Trans. Knowl. Data Eng. 7(4), 533–551 (1995) CrossRefGoogle Scholar
  35. 35.
    Qu, H., Labrinidis, A.: Preference-aware query and update scheduling in web-databases. In: ICDE, pp. 356–365 (2007) Google Scholar
  36. 36.
    Qu, H., Labrinidis, A., Mossé, D.: Unit: User-centric transaction management in web-database systems. In: ICDE, p. 33 (2006) Google Scholar
  37. 37.
    Ramakrishnan, R.: Cap and cloud data management. Computer 45(2), 43–49 (2012) CrossRefGoogle Scholar
  38. 38.
    Saito, Y., Shapiro, M.: Optimistic replication. ACM Comput. Surv. 37(1), 42–81 (2005) CrossRefGoogle Scholar
  39. 39.
    Sharaf, M.A., Chrysanthis, P.K.: Facilitating mobile decision making. In: Workshop Mobile Commerce, pp. 45–53 (2002) Google Scholar
  40. 40.
    Sharaf, M.A., Sismanis, Y., Labrinidis, A., Chrysanthis, P.K., Roussopoulos, N.: Efficient dissemination of aggregate data over the wireless web. In: WebDB, pp. 93–98 (2003) Google Scholar
  41. 41.
    Sharaf, M.A., Guirguis, S., Labrinidis, A., Pruhs, K., Chrysanthis, P.K.: Poster session: asets: a self-managing transaction scheduler. In: ICDE Workshops, pp. 56–62 (2008) Google Scholar
  42. 42.
    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
  43. 43.
    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) CrossRefGoogle Scholar
  44. 44.
    Silberstein, A., Terrace, J., Cooper, B.F., Ramakrishnan, R.: Feeding frenzy: selectively materializing users’ event feeds. In: SIGMOD Conference, pp. 831–842 (2010) Google Scholar
  45. 45.
    Sivasubramanian, S., Pierre, G., van Steen, M., Alonso, G.: Analysis of caching and replication strategies for web applications. IEEE Internet Comput. 11(1), 60–66 (2007) CrossRefGoogle Scholar
  46. 46.
    Thiele, M., Bader, A., Lehner, W.: Multi-objective scheduling for real-time data warehouses. In: BTW, pp. 307–326 (2009) Google Scholar
  47. 47.
    Thomas, R.H.: A majority consensus approach to concurrency control for multiple copy databases. ACM Trans. Database Syst. 4(2), 180–209 (1979) CrossRefGoogle Scholar
  48. 48.
    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
  49. 49.
    Xu, C., Sharaf, M.A., Zhou, M., Zhou, A., Zhou, X.: Adaptive query scheduling in key-value data stores. In: DASFAA, vol. 1, pp. 86–100 (2013) Google Scholar
  50. 50.
    Zhu, Y., Sharaf, M.A., Zhou, X.: Scheduling with freshness and performance guarantees for web applications in the cloud. In: ADC, pp. 133–142 (2011) Google Scholar
  51. 51.
    Zhu, Y., Yu, P.S., Wang, J.: Recods: replica consistency-on-demand store. In: ICDE, pp. 1360–1363 (2013) Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Chen Xu
    • 1
  • Mohamed A. Sharaf
    • 2
  • Xiaofang Zhou
    • 2
  • Aoying Zhou
    • 1
    Email author
  1. 1.Software Engineering InstituteEast China Normal UniversityShanghaiChina
  2. 2.School of Information Technology & Electrical EngineeringThe University of QueenslandBrisbaneAustralia

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