Abstract
Runtime IoT data fluctuation brings challenges for optimizing the resource allocation for a data stream processing (DSP) flow in a cloud-edge environment. It can result in extra high latency for a flow. Optimized strategy of dynamic resource allocation is still hard to design to timely dealing with the IoT data fluctuation. In this paper, the above challenge is abstracted and redefined as the service deployment problem. An improved GA optimization algorithm, integrating with the IoT data fluctuation prediction ability, is proposed to handle IoT data fluctuations during the running of a DSP flow. Effectiveness of the proposed approach is evaluated based on the real datasets from a real application.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Renart, E.G., Diaz-Montes, J., Parashar, M.: Data-driven stream processing at the edge. In: IEEE International Conference on Fog and Edge Computing, Madrid, Spain, pp. 31–40. IEEE (2017)
Zhang, S., Chen, L., Han, Y., et al.: Seamless Integration of Cloud and Edge with a service-based approach. In: 2018 IEEE International Conference on Web Services, San Francisco, CA, USA, pp. 155–162. IEEE (2018)
Xu, X., Huang, S., Feagan, L., et al.: EAaaS: edge analytics as a service. In: 2017 IEEE International Conference on Web Services, Honolulu, HI, pp. 349–356. IEEE (2017)
Varghese, B., Wang, N., Li, J., et al.: Edge-as-a-service: towards distributed cloud architectures. Adv. Parallel Comput. 32, 784–793 (2017)
Moussa, H., Yen, I.L., Bastani, F.: Service management in the edge cloud for stream processing of IoT data. In: 2020 IEEE 13th International Conference on Cloud Computing, Beijing, China, pp. 91–98. IEEE (2020)
Huang, Z., Lin, K.J., Tsai, B.L., et al.: Building edge intelligence for online activity recognition in service-oriented IoT systems. Futur. Gener. Comput. Syst. 87, 557–567 (2018)
Pallewatta, S., Kostakos, V., Buyya, R.: Microservices-based IoT application placement within heterogeneous and resource constrained fog computing environments. In: The 12th IEEE/ACM International Conference on Utility and Cloud Computing, New York, United States, pp. 71–81. ACM (2019)
Barika, M., Garg, S., Chan, A., et al.: Scheduling algorithms for efficient execution of stream workflow applications in multicloud environments. IEEE Trans. Serv. Comput. (2019)
Veith, A., Assuncao, M., Lefèvre, L.: Latency-aware placement of data stream analytics on edge computing. Service-Orient. Comput.11236, 215–229 (2018)
Veith, A., Renart, E.G., Balouek-Thomert, D., et al.: Distributed operator placement for IoT data analytics across edge and cloud resources. In: IEEE/ACM International Symposium in Cluster, Cloud, and Grid Computing, Larnaca, Cyprus, pp. 459–468. ACM (2019)
Salaht, F.A., Desprez, F., Lebre, A., et al.: Service placement in fog computing using constraint programming. In: 2019 IEEE International Conference on Services Computing, pp. 19–27. IEEE (2019)
de Souza, F.R., Da Silva Veith, A., Dias de Assunção, M., Caron, E.: Scalable joint optimization of placement and parallelism of data stream processing applications on cloud-edge infrastructure. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds.) ICSOC 2020. LNCS, vol. 12571, pp. 149–164. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65310-1_12
Maia, A.M., Ghamri-Doudane, Y., Vieira, D., et al.: Dynamic service placement and load distribution in edge computing. In: 16th International Conference on Network and Service Management, Izmir, Turkey, pp. 1–9. IEEE (2020)
Gao, X., Huang, X., Bian, S., et al.: PORA: predictive offloading and resource allocation in dynamic fog computing systems. IEEE Internet Things J. 7(1), 72–87 (2020)
Lambert, T., Guyon, D., Ibrahim, S.: Rethinking operators placement of stream data application in the edge. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, New York, NY, USA. Association for Computing Machinery, pp. 2101–2104. ACM (2020)
Chen, X., Tang, S., Lu, Z., et al.: iDiSC: a new approach to IoT-data-intensive service components deployment in edge-cloud-hybrid system. IEEE Access 99(1–1) (2019)
Mohtadi, A., Gascon-Samson, J.: Poster: dependency-aware operator placement of distributed stream processing IoT applications deployed at the edge. In: 2020 IEEE/ACM Symposium on Edge Computing, San Jose, California, USA, pp. 161–163. ACM (2020)
Han, Y., Liu, C., Su, S., et al.: A proactive service model facilitating stream data fusion and correlation. Int. J. Web Serv. Res. 14(3), 1–16 (2017)
Taylor, S.J., Letham, B.: Forecasting at scale. Am. Stat. 72(1), 37–45 (2018)
Barika, M., Garg, S., Zomaya, A., et al.: Online scheduling technique to handle data velocity changes in stream workflows. IEEE Trans. Parallel Distrib. Syst. 99, 1 (2021)
Acknowledgement
This work is supported by the Key Program of National Natural Science Foundation of China Research on Big Service Theory and Methods in Big Data Environment (No. 61832004).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, S., Liu, C., Li, H., Zhao, Z., Li, X. (2021). Service Deployment with Predictive Ability for Data Stream Processing in a Cloud-Edge Environment. In: Hacid, H., Kao, O., Mecella, M., Moha, N., Paik, Hy. (eds) Service-Oriented Computing. ICSOC 2021. Lecture Notes in Computer Science(), vol 13121. Springer, Cham. https://doi.org/10.1007/978-3-030-91431-8_55
Download citation
DOI: https://doi.org/10.1007/978-3-030-91431-8_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-91430-1
Online ISBN: 978-3-030-91431-8
eBook Packages: Computer ScienceComputer Science (R0)