Abstract
Software systems need to be maintained and frequently updated to provide the best possible service to the end-users. However, updates sometimes, cause the system or part of it to restart and disconnect, causing downtime and potentially reducing the quality of service.
In this work we studied and analyzed the case of a large Nordic company running a service-oriented system running on edge nodes, and providing services to 270K IoT devices. To update the system while minimizing downtime, we develop a smart edge service update scheduler for a service-oriented architecture, which suggests the best possible update schedule that minimizes the loss of connections for IoT devices.
Our approach was validated by applying the scheduling algorithm to the whole system counting 270k edge nodes distributed among 800 locations.
By taking into account the topology of the software system and its real-time utilization, it is possible to optimize the updates in a way that substantially minimizes downtime.
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
Notes
- 1.
For reasons of NDA, we are not allowed to disclose the name of the company, nor the low-level details of the use case.
References
Satyanarayanan, M.: The emergence of edge computing. Computer 50(1), 30–39 (2017)
Kherraf, N., Alameddine, H.A., Sharafeddine, S., Assi, C.M., Ghrayeb, A.: Optimized provisioning of edge computing resources with heterogeneous workload in IoT networks. IEEE Trans. Netw. Serv. Manage. 16(2), 459–474 (2019)
Cao, X., Tang, G., Guo, D., Li, Y., Zhang, W.: Edge federation: towards an integrated service provisioning model. IEEE/ACM Trans. Netw. 28(3), 1116–1129 (2020)
Xu, X., Cao, H., Geng, Q., Liu, X., Dai, F., Wang, C.: Dynamic resource provisioning for workflow scheduling under uncertainty in edge computing environment. Concurr. Comput. Pract. Exp. 34, e5674 (2020)
Ogbuachi, M.C., Gore, C., Reale, A., Suskovics, P., Kovács, B.: Context-aware K8S scheduler for real time distributed 5G edge computing applications. In: 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1–6. IEEE (2019)
Guo, J., Li, C., Chen, Y., Luo, Y.: On-demand resource provision based on load estimation and service expenditure in edge cloud environment. J. Netw. Comput. Appl. 151, 102506 (2020)
Li, C., Bai, J., Ge, Y., Luo, Y.: Heterogeneity-aware elastic provisioning in cloud-assisted edge computing systems. Futur. Gener. Comput. Syst. 112, 1106–1121 (2020)
Schult, D.A.: Exploring network structure, dynamics, and function using NetworkX. In: Proceedings of the 7th Python in Science Conference (SciPy). Citeseer (2008)
Legg, P., Hui, G., Johansson, J.: A simulation study of LTE intra-frequency handover performance. In: 2010 IEEE 72nd Vehicular Technology Conference - Fall, pp. 1–5 (2010)
Acknowledgments
This work was partially supported by the Adoms grant from the Ulla Tuominen Foundation (Finland) and the MuFAno grant from the Academy of Finland (grant n. 349488).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Moreschini, S., Lomio, F., Hästbacka, D., Taibi, D. (2023). Smart Edge Service Update Scheduler: An Industrial Use Case. In: Troya, J., et al. Service-Oriented Computing – ICSOC 2022 Workshops. ICSOC 2022. Lecture Notes in Computer Science, vol 13821. Springer, Cham. https://doi.org/10.1007/978-3-031-26507-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-26507-5_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26506-8
Online ISBN: 978-3-031-26507-5
eBook Packages: Computer ScienceComputer Science (R0)