Skip to main content
Springer Nature Link
Log in

A Novel Auction-Based Query Pricing Schema

  • Published:
International Journal of Parallel Programming Aims and scope Submit manuscript

Abstract

As a common processing method, query is widely used in many areas, such as graph processing, machine learning, statistics. However, queries are usually priced according to vendor-specified fixed views (API) or number of transactions, which ignores query heterogeneity(computing resource consumption for query and information that the answer brings) and violates the microeconomic principles. In this work we study the relational query pricing problem and design efficient auctions by taking into account both information (i.e., data) value and query resource consumption. Different from the existing query pricing schemes, query auction determines data prices that reflect the demand–supply of shared computing resources and information value (i.e., price discovery). We target query auction that runs in polynomial time and achieves near-optimal social welfare with a good approximation ratio, while elicits truthful bids from consumers. Towards these goals, we adapt the posted pricing framework in game-theoretic perspective by casting the query auction design into an Integer Linear Programming problem, and design a primal-dual algorithm to approximate the NP-hard optimization problem. Theoretical analysis and empirical studies driven by a real-world data market benchmark verify the efficiency of our query auction schema.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. https://datamarket.azure.com/browse/data. Accessed 28 March 2017

  2. http://www.infochimps.com/infochimps-cloud/cloud-services/cloud-queries/. Accessed 28 March 2017

  3. https://www.aggdata.com/. Accessed 28 March 2017

  4. https://aws.amazon.com/ec2/spot/. Accessed 28 March 2017

  5. http://hbase.apache.org/. Accessed 28 March 2017

  6. http://www.postgresql.org/docs/current/static/runtime-config-query.html. Accessed 28 March 2017

  7. Ahmad, M., Duan, S.: Predicting completion times of batch query workloads using interaction-aware models and simulation. In: Proceedings of the 14th International Conference on Extending Database Technology, pp. 449–460. ACM (2011)

  8. Akdere, M., Çetintemel, U., Riondato, M., Upfal, E., Zdonik, S.B.: Learning-based query performance modeling and prediction. In: ICDE 2012, pp. 390–401. IEEE (2012)

  9. Balazinska, M., Howe, B., Suciu, D.: Data markets in the cloud: an opportunity for the database community. Proc. VLDB Endow. 4, 12 (2011)

    Google Scholar 

  10. Cahoon, B., McKinley, K.S., Lu, Z.: Evaluating the performance of distributed architectures for information retrieval using a variety of workloads. ACM Trans. Inf. Syst. (TOIS) 18(1), 1–43 (2000)

    Article  Google Scholar 

  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), 205–218 (2008)

    Article  Google Scholar 

  12. Duggan, J., Cetintemel, U., Papaemmanouil, O., Upfal, E.: Performance prediction for concurrent database workloads. In: SIGMOD 2011, pp. 337–348. ACM (2011)

  13. Ganapathi, A., Kuno, H., Dayal, U., Wiener, J.L., Fox, A., Jordan, M., Patterson, D.: Predicting multiple metrics for queries: better decisions enabled by machine learning. In: ICDE 2009, pp. 592–603. IEEE (2009)

  14. Giceva, J., Alonso, G., Roscoe, T., Harris, T.: Deployment of query plans on multicores. Proc. VLDB Endow. 8(3), 233–244 (2014)

    Article  Google Scholar 

  15. Graefe, G., McKenna, W.J.: The volcano optimizer generator: extensibility and efficient search. In: Ninth International Conference on Data Engineering, 1993. Proceedings, pp. 209–218. IEEE (1993)

  16. Kellerer, H., Pferschy, U., Pisinger, D.: Introduction to NP-Completeness of knapsack problems. In: Knapsack Problems, pp. 483–493. Springer, Berlin, Heidelberg (2004)

  17. Koutris, P., Upadhyaya, P., Balazinska, M., Howe, B., Suciu, D.: Query-based data pricing. In: Proceedings of the 31st Symposium on Principles of Database Systems, pp. 167–178. ACM (2012)

  18. Koutris, P., Upadhyaya, P., Balazinska, M., Howe, B., Suciu, D.: Toward practical query pricing with querymarket. In: SIGMOD 2013, pp. 613–624. ACM (2013)

  19. Li, C., Li, D.Y., Miklau, G., Suciu, D.: A theory of pricing private data. ACM Trans. Database Syst. (TODS) 39(4), 34 (2014)

    Article  MathSciNet  Google Scholar 

  20. Li, C., Miklau, G.: Pricing aggregate queries in a data marketplace. In: WebDB, pp. 19–24 (2012)

  21. Li, J., König, A.C., Narasayya, V., Chaudhuri, S.: Robust estimation of resource consumption for sql queries using statistical techniques. Proc. VLDB Endow. 5(11), 1555–1566 (2012)

    Article  Google Scholar 

  22. Li, J., Naughton, J., Nehme, R.V.: Resource bricolage for parallel database systems. Proc. VLDB Endow. 8(1), 25–36 (2014)

    Article  Google Scholar 

  23. Li, Z., Li, B., Zhu, Y.: Designing truthful spectrum auctions for multi-hop secondary networks. IEEE Trans. Mob. Comput. 14(2), 316–327 (2015)

    Article  Google Scholar 

  24. Liu, Y., Zhou, C., Gao, J., Fan, Z.: Giraphasync: supporting online and offline graph processing via adaptive asynchronous message processing. In: ACM International on Conference on Information and Knowledge Management, pp. 479–488 (2016)

  25. Lorie, R.A.: XRM: An extended (N-ary) relational memory. IBM (1974)

  26. Lu, Z., McKinley, K.S.: Partial collection replication for information retrieval. Inf. Retr. 6(2), 159–198 (2003)

    Article  Google Scholar 

  27. Mei, Q., Fang, H., Zhai, C.: A study of poisson query generation model for information retrieval. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 319–326. ACM (2007)

  28. Mozafari, B., Curino, C., Madden, S.: Dbseer: resource and performance prediction for building a next generation database cloud. In: CIDR (2013)

  29. Myerson, R.B.: Optimal auction design. Math. Oper. Res. 6(1), 58–73 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  30. Nisan, N., Roughgarden, T., Vazirani, V.V.: Algorithmic Game Theory, vol. 1. Cambridge University Press, Cambridge (2007)

    Book  MATH  Google Scholar 

  31. Padala, P., Shin, K.G., Zhu, X., Uysal, M., Wang, Z., Singhal, S., Merchant, A., Salem, K.: Adaptive control of virtualized resources in utility computing environments. In: ACM SIGOPS Operating Systems Review, vol. 41, pp. 289–302. ACM (2007)

  32. Portosa, A., Rafique, M.M., Kotoulas, S., Foschini, L., Corradi, A.: Heterogeneous cloud systems monitoring using semantic and linked data technologies. In: 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), pp. 497–503. IEEE (2015)

  33. Shi, W., Zhang, L., Wu, C., Li, Z., Lau, F.: An online auction framework for dynamic resource provisioning in cloud computing. ACM SIGMETRICS Perform. Eval. Rev. 42(1), 71–83 (2014)

    Article  Google Scholar 

  34. Soror, A.A., Minhas, U.F., Aboulnaga, A., Salem, K., Kokosielis, P., Kamath, S.: Automatic virtual machine configuration for database workloads. ACM Trans. Database Syst. (TODS) 35(1), 7 (2010)

    Article  Google Scholar 

  35. Varian, H.R.: Pricing Information Goods, pp. 190–202 (1998). http://amitre.synthasite.com/resources/varian_Hal_price-info-goods.pdf

  36. Wu, W., Chi, Y., Zhu, S., Tatemura, J., Hacigumus, H., Naughton, J.F.: Predicting query execution time: are optimizer cost models really unusable? In: ICDE 2013, pp. 1081–1092. IEEE (2013)

  37. Xiong, P., Chi, Y., Zhu, S., Moon, H.J., Pu, C., Hacigümüş, H.: Intelligent management of virtualized resources for database systems in cloud environment. In: ICDE 2011, pp. 87–98. IEEE (2011)

  38. Yan, Y., Chen, L.J., Zhang, Z.: Error-bounded sampling for analytics on big sparse data. Proc. Vldb Endow. 7(13), 1508–1519 (2014)

    Article  Google Scholar 

  39. Zhang, L., Li, Z., Wu, C.: Dynamic resource provisioning in cloud computing: a randomized auction approach. In: Proceedings—IEEE INFOCOM, pp. 433–441 (2014)

  40. Zhang, X., Wu, C., Li, Z., Lau, F.: A truthful (1-\(\varepsilon \))-optimal mechanism for on-demand cloud resource provisioning. In: INFOCOM 2015, pp. 1053–1061. IEEE (2015)

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC) (Grant No. 61772228), National Key Research and Development Program Of China (Grant Nos. 2016YFB0201503 and 2016YFB0701101), Major Special Research Project of Science and Technology Department of Jilin Province (20160203008GX), Jilin Scientific and Technological Development Program (20170520066JH) and Graduate Innovation Fund of Jilin University (2017069).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun City, 130012, China

    Xingwang Wang, Xiaohui Wei, Shang Gao & Yuanyuan Liu

  2. Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada

    Zongpeng Li

Authors
  1. Xingwang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaohui Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shang Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuanyuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zongpeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shang Gao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Wei, X., Gao, S. et al. A Novel Auction-Based Query Pricing Schema. Int J Parallel Prog 47, 759–780 (2019). https://doi.org/10.1007/s10766-017-0534-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10766-017-0534-x

Keywords