Advertisement

Optimized Analytics Query Allocation at the Edge of the Network

  • Anna Karanika
  • Madalena Soula
  • Christos Anagnostopoulos
  • Kostas KolomvatsosEmail author
  • George Stamoulis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11874)

Abstract

The new era of the Internet of Things (IoT) provides the space where novel applications will play a significant role in people’s daily lives through the adoption of multiple services that facilitate everyday activities. The huge volumes of data produced by numerous IoT devices make the adoption of analytics imperative to produce knowledge and support efficient decision making. In this setting, one can identify two main problems, i.e., the time required to send the data to Cloud and wait for getting the final response and the distributed nature of data collection. Edge Computing (EC) can offer the necessary basis for storing locally the collected data and provide the required analytics on top of them limiting the response time. In this paper, we envision multiple edge nodes where data are stored being the subject of analytics queries. We propose a methodology for allocating queries, defined by end users or applications, to the appropriate edge nodes in order to save time and resources in the provision of responses. By adopting our scheme, we are able to ask the execution of queries only from a sub-set of the available nodes avoiding to demand processing activities that will lead to an increased response time. Our model envisions the allocation to specific epochs and manages a batch of queries at a time. We present the formulation of our problem and the proposed solution while providing results of an extensive evaluation process that reveals the pros and cons of the proposed model.

Keywords

Internet of Things Edge Computing Large scale data Queries management 

Notes

Acknowledgment

This research received funding from the European’s Union Horizon 2020 research and innovation programme under the grant agreement No. 745829 & the Greek Secretariat for Research Funding under the project ENFORCE.

References

  1. 1.
    Apiletti, D., et al.: Frequent itemsets mining for big data: a comparative analysis. Big Data Res. 9, 67–83 (2017)CrossRefGoogle Scholar
  2. 2.
    Bangui, H., et al.: Moving to the edge-cloud-of-things: recent advances and future research directions. Electronics 7, 309 (2018)CrossRefGoogle Scholar
  3. 3.
    Bowden, D., et al.: A cloud-to-edge architecture for predictive analytics. In: Workshops of the EDBT/ICDT Conference (2019)Google Scholar
  4. 4.
    Chai, Z., et al.: Towards taming the resource and data heterogeneity in federated learning. In: USENIX Conference on Operational Machine Learning (2019)Google Scholar
  5. 5.
    Chandramouli, B., Goldstein, J., Quamar, A.: Scalable progressive analytics on big data in the cloud. VLDB Endow. 6(14), 1726–1737 (2013)CrossRefGoogle Scholar
  6. 6.
    Chatterjea, S., Havunga, P.: A taxonomy of distributed query management techniques for wireless sensor networks. IJCS 20(7), 889–908 (2007)Google Scholar
  7. 7.
    Chen, Y., Zhu, F., Lee, J.: Data quality evaluation and improvement for prognostic modeling using visual assessment based data partitioning method. Comput. Ind. 64(3), 214–225 (2013)CrossRefGoogle Scholar
  8. 8.
    Condie, T., et al.: MapReduce online. In: The 7th Conference on Networked Systems Design and Implementation (2010)Google Scholar
  9. 9.
    Cummins, R., et al.: A Polya urn document language model for improved information retrieval. ACM TIS 9(4), 21 (2010)Google Scholar
  10. 10.
    Hellerstein, J.M., Avnur, R.: Informix under CONTROL: online query processing. Data Min. Knowl. Discovery J. 4, 281–314 (2000)CrossRefGoogle Scholar
  11. 11.
    Huang, Z., Zhong, A., Li, G.: On-demand processing for remote sensing big data analysis. In: IEEE ISPDPA (2017)Google Scholar
  12. 12.
    Jermaine, C., et al.: Scalable approximate query processing with the DBO engine. In: SIGMOD (2007)Google Scholar
  13. 13.
    Khan, W., et al.: Edge computing: a survey. FGCS 97, 219–235 (2019)CrossRefGoogle Scholar
  14. 14.
    Kolomvatsos, K., Anagnostopoulos, C.: Multi-criteria optimal task allocation at the edge. FGCS 93, 358–372 (2019)CrossRefGoogle Scholar
  15. 15.
    Kolomvatsos, K., Anagnostopoulos, C.: An edge-centric ensemble scheme for queries assignment. In: 8th CIMA Workshop (2018)Google Scholar
  16. 16.
    Kolomvatsos, K.: An intelligent scheme for assigning queries. Appl. Intell. 48, 2730–2745 (2018)CrossRefGoogle Scholar
  17. 17.
    Kolomvatsos, K., Anagnostopoulos, C.: Reinforcement machine learning for predictive analytics in smart cities. Informatics 4, 16 (2017)CrossRefGoogle Scholar
  18. 18.
    Kolomvatsos, K., Hadjiefthymiades, S.: Learning the engagement of query processors for intelligent analytics. Appl. Intell. 46, 96–112 (2017)CrossRefGoogle Scholar
  19. 19.
    Logothetis, D., Yocum, K.: Ad-hoc data processing in the cloud. VLDB Endow. 1(2), 1472–1475 (2008)CrossRefGoogle Scholar
  20. 20.
    Munkres, J.: Algorithms for the assignment and transportation problems. JSIAM 5(1), 32–38 (1957)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Murphree, J.: Machine learning anomaly detection in large systems. In: IEEE AUTOTESTCON, pp. 1–9 (2016)Google Scholar
  22. 22.
    Phansalkar, S., Ahirrao, S.: Survey of data partitioning algorithms for big data stores. In: 4th ICPDGC (2016)Google Scholar
  23. 23.
    Yu, W., et al.: A survey on the edge computing for the Internet of Things. IEEE Access 6, 6900–6919 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anna Karanika
    • 1
  • Madalena Soula
    • 1
  • Christos Anagnostopoulos
    • 2
  • Kostas Kolomvatsos
    • 2
    Email author
  • George Stamoulis
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of ThessalyVolosGreece
  2. 2.School of Computing ScienceUniversity of GlasgowGlasgowUK

Personalised recommendations