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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Apiletti, D., et al.: Frequent itemsets mining for big data: a comparative analysis. Big Data Res. 9, 67–83 (2017)
Bangui, H., et al.: Moving to the edge-cloud-of-things: recent advances and future research directions. Electronics 7, 309 (2018)
Bowden, D., et al.: A cloud-to-edge architecture for predictive analytics. In: Workshops of the EDBT/ICDT Conference (2019)
Chai, Z., et al.: Towards taming the resource and data heterogeneity in federated learning. In: USENIX Conference on Operational Machine Learning (2019)
Chandramouli, B., Goldstein, J., Quamar, A.: Scalable progressive analytics on big data in the cloud. VLDB Endow. 6(14), 1726–1737 (2013)
Chatterjea, S., Havunga, P.: A taxonomy of distributed query management techniques for wireless sensor networks. IJCS 20(7), 889–908 (2007)
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)
Condie, T., et al.: MapReduce online. In: The 7th Conference on Networked Systems Design and Implementation (2010)
Cummins, R., et al.: A Polya urn document language model for improved information retrieval. ACM TIS 9(4), 21 (2010)
Hellerstein, J.M., Avnur, R.: Informix under CONTROL: online query processing. Data Min. Knowl. Discovery J. 4, 281–314 (2000)
Huang, Z., Zhong, A., Li, G.: On-demand processing for remote sensing big data analysis. In: IEEE ISPDPA (2017)
Jermaine, C., et al.: Scalable approximate query processing with the DBO engine. In: SIGMOD (2007)
Khan, W., et al.: Edge computing: a survey. FGCS 97, 219–235 (2019)
Kolomvatsos, K., Anagnostopoulos, C.: Multi-criteria optimal task allocation at the edge. FGCS 93, 358–372 (2019)
Kolomvatsos, K., Anagnostopoulos, C.: An edge-centric ensemble scheme for queries assignment. In: 8th CIMA Workshop (2018)
Kolomvatsos, K.: An intelligent scheme for assigning queries. Appl. Intell. 48, 2730–2745 (2018)
Kolomvatsos, K., Anagnostopoulos, C.: Reinforcement machine learning for predictive analytics in smart cities. Informatics 4, 16 (2017)
Kolomvatsos, K., Hadjiefthymiades, S.: Learning the engagement of query processors for intelligent analytics. Appl. Intell. 46, 96–112 (2017)
Logothetis, D., Yocum, K.: Ad-hoc data processing in the cloud. VLDB Endow. 1(2), 1472–1475 (2008)
Munkres, J.: Algorithms for the assignment and transportation problems. JSIAM 5(1), 32–38 (1957)
Murphree, J.: Machine learning anomaly detection in large systems. In: IEEE AUTOTESTCON, pp. 1–9 (2016)
Phansalkar, S., Ahirrao, S.: Survey of data partitioning algorithms for big data stores. In: 4th ICPDGC (2016)
Yu, W., et al.: A survey on the edge computing for the Internet of Things. IEEE Access 6, 6900–6919 (2017)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Karanika, A., Soula, M., Anagnostopoulos, C., Kolomvatsos, K., Stamoulis, G. (2019). Optimized Analytics Query Allocation at the Edge of the Network. In: Montella, R., Ciaramella, A., Fortino, G., Guerrieri, A., Liotta, A. (eds) Internet and Distributed Computing Systems . IDCS 2019. Lecture Notes in Computer Science(), vol 11874. Springer, Cham. https://doi.org/10.1007/978-3-030-34914-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-34914-1_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34913-4
Online ISBN: 978-3-030-34914-1
eBook Packages: Computer ScienceComputer Science (R0)