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Joint Operator Scaling and Placement for Distributed Stream Processing Applications in Edge Computing

  • Qinglan Peng
  • Yunni XiaEmail author
  • Yan Wang
  • Chunrong Wu
  • Xin LuoEmail author
  • Jia Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11895)

Abstract

Distributed Stream Processing (DSP) systems are well acknowledged to be potent in processing huge volume of real-time stream data with low latency and high throughput. Recently, the edge computing paradigm shows great potentials in supporting and boosting the DSP applications, especially the time-critical and latency-sensitive ones, over the Internet of Things (IoT) or mobile devices by means of offloading the computation from remote cloud to edge servers for further reduced communication latencies. Nevertheless, various challenges, especially the joint operator scaling and placement, are yet to be properly explored and addressed. Traditional efforts in this direction usually assume that the data-flow graph of a DSP application is pre-given and static. The resulting models and methods can thus be ineffective and show bad user-perceived quality-of-service (QoS) when dealing with real-world scenarios with reconfigurable data-flow graphs and scalable operator placement. In contrast, in this paper, we consider that the data-flow graphs are configurable and hence propose the joint operator scaling and placement problem. To address this problem, we first build a queuing-network-based QoS estimation model, then formulate the problem into an integer-programming one, and finally propose a two-stage approach for finding the near-optimal solution. Experiments based on real-world DSP test cases show that our method achieves higher cost effectiveness than traditional ones while meeting the user-defined QoS constraints.

Keywords

Edge computing Distributed stream processing Operator placement Operator replication 

References

  1. 1.
    Amarasinghe, G., de Assuno, M.D., Harwood, A., Karunasekera, S.: A data stream processing optimisation framework for edge computing applications. In: 2018 IEEE 21st International Symposium on Real-Time Distributed Computing (ISORC), pp. 91–98. IEEE (2018)Google Scholar
  2. 2.
    Benoit, A., Dobrila, A., Nicod, J.M., Philippe, L.: Scheduling linear chain streaming applications on heterogeneous systems with failures. Future Gener. Comput. Syst. 29(5), 1140–1151 (2013)CrossRefGoogle Scholar
  3. 3.
    Cai, X., Kuang, H., Hu, H., Song, W., Lü, J.: Response time aware operator placement for complex event processing in edge computing. In: Pahl, C., Vukovic, M., Yin, J., Yu, Q. (eds.) ICSOC 2018. LNCS, vol. 11236, pp. 264–278. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03596-9_18CrossRefGoogle Scholar
  4. 4.
    Cardellini, V., Grassi, V., Lo Presti, F., Nardelli, M.: Optimal operator replication and placement for distributed stream processing systems. ACM SIGMETRICS Perform. Eval. Rev. 44(4), 11–22 (2017)CrossRefGoogle Scholar
  5. 5.
    Cardellini, V., Lo Presti, F., Nardelli, M., Russo Russo, G.: Optimal operator deployment and replication for elastic distributed data stream processing. Concurr. Comput. Pract. Exp. 30(9), e4334 (2018)CrossRefGoogle Scholar
  6. 6.
    Gen, M., Lin, L.: Genetic algorithms. In: Wiley Encyclopedia of Computer Science and Engineering, pp. 1–15 (2007)Google Scholar
  7. 7.
    Gibert Renart, E., da Silva Veith, A., Balouek-Thomert, D., Dias de Assuncao, M., Lefèvre, L., Parashar, M.: Distributed operator placement for IoT data analytics across edge and cloud resources. In: CCGrid 2019 - 19th Annual IEEE/ACM International Symposium in Cluster, Cloud, and Grid Computing, pp. 1–10. IEEE/ACM (2019)Google Scholar
  8. 8.
    Hidalgo, N., Rosas, E.: Self-adaptive processing graph with operator fission for elastic stream processing. J. Syst. Softw. 127, 205–216 (2017)CrossRefGoogle Scholar
  9. 9.
    Hiessl, T., Karagiannis, V., Hochreiner, C., Schulte, S., Nardelli, M.: Optimal placement of stream processing operators in the fog. In: 2019 IEEE 3rd International Conference on Fog and Edge Computing (ICFEC), pp. 1–10. IEEE (2019)Google Scholar
  10. 10.
    Hu, W., et al.: quantifying the impact of edge computing on mobile applications. In: Proceedings of the 7th ACM SIGOPS Asia-Pacific Workshop on Systems, p. 5. ACM (2016)Google Scholar
  11. 11.
    Kaur, N., Sood, S.K.: Efficient resource management system based on 4vs of big data streams. Big Data Res. 9, 98–106 (2017)CrossRefGoogle Scholar
  12. 12.
    Mai, L., et al.: Chi: a scalable and programmable control plane for distributed stream processing systems. Proc. VLDB Endow. 11(10), 1303–1316 (2018)CrossRefGoogle Scholar
  13. 13.
    Myrvold, W., Ruskey, F.: Ranking and unranking permutations in linear time. Inf. Process. Lett. 79(6), 281–284 (2001)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Nardelli, M., Cardellini, V., Grassi, V., Presti, F.L.: Efficient operator placement for distributed data stream processing applications. IEEE Trans. Parallel Distrib. Syst. 30(8), 1753–1767 (2019) CrossRefGoogle Scholar
  15. 15.
    Networking, C.V.: Cisco Global Cloud Index: Forecast and Methodology, 2016–2021. Cisco Public, San Jose (2018). White paperGoogle Scholar
  16. 16.
    Pietzuch, P., Ledlie, J., Shneidman, J., Roussopoulos, M., Welsh, M., Seltzer, M.: Network-aware operator placement for stream-processing systems. In: 22nd International Conference on Data Engineering (ICDE 2006), pp. 49–49. IEEE (2006)Google Scholar
  17. 17.
    Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L.: Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016)CrossRefGoogle Scholar
  18. 18.
    da Silva Veith, A., de Assunção, M.D., Lefèvre, L.: Latency-aware placement of data stream analytics on edge computing. In: Pahl, C., Vukovic, M., Yin, J., Yu, Q. (eds.) ICSOC 2018. LNCS, vol. 11236, pp. 215–229. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03596-9_14CrossRefGoogle Scholar
  19. 19.
    Taneja, M., Davy, A.: Resource aware placement of iot application modules in fog-cloud computing paradigm. In: 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), pp. 1222–1228. IEEE (2017)Google Scholar
  20. 20.
    Yang, S.: Iot stream processing and analytics in the fog. IEEE Commun. Mag. 55(8), 21–27 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Software Theory and Technology Chongqing Key LabChongqing UniversityChongqingChina
  2. 2.Department of ComputingMacquarie UniversitySydneyAustralia
  3. 3.Chinese Academy of SciencesChongqing Institute of Green and Intelligent TechnologyChongqingChina

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