Abstract
The concept of Edge-to-Cloud Continuum aims to significantly reduce overall traffic to the cloud by enabling IoT data processing as close as possible to the data sources, either on near- or far-edge devices. In this highly dynamic environment, where IoT devices and edge nodes are constantly changing their state and location, services running on edge nodes have to be scheduled, deployed and managed to ensure high service availability with appropriate Quality of Service (QoS) parameters. However, once services are deployed in the edge-to-cloud continuum, the question arises how to ensure continuous data delivery from IoT devices to the appropriate services for further processing, either on edge devices or in the cloud. In this paper, we propose a general architecture for adaptive data-driven routing in the edge-to-cloud continuum and introduce an implementation of this architecture using the content-based publish/subscribe approach. We evaluate the given implementation against a real-world use case scenario for federated learning in an edge-to-cloud environment hosting digital twins. The performance evaluation of this scenario shows that our implementation efficiently adapts to service failures and reconfigures the edge-to-cloud environment with minimal latency and without data loss, while preserving data privacy and security. In addition, the experiments show that our solution is stable in an environment with IoT data sources generating data at high frequency.
This work has been supported by Croatian Science Foundation under the project IP-2019–04-1986.
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References
Antonić, A., Marjanović, M., Pripužić, K., Podnar Žarko, I.: A mobile crowd sensing ecosystem enabled by CUPUS: cloud-based publish/subscribe middleware for the Internet of Things. Futur. Gener. Comput. Syst. 56, 607–622 (2016)
Arulraj, J., Chatterjee, A., Daglis, A., Dhekne, A., Ramachandran, U.: eCloud: a vision for the evolution of the edge-cloud continuum. Computer 54(5), 24–33 (2021)
Bormann, C., Ersue, M., Keränen, A.: Terminology for Constrained-Node Networks. RFC 7228 (2014)
Cloud native computing foundation: K3s. https://k3s.io/
Giouroukis, D., Jestram, J., Zeuch, S., Markl, V.: Streaming data through the IoT via actor-based semantic routing trees. Open J. Internet Things 7(1), 59–70 (2021)
Gupta, A.K., Sahin, O.D., Agrawal, D., Abbadi, A.E.: Meghdoot: content-based publish/subscribe over P2P networks. In: Middleware, pp. 254–273 (2004)
Gupta, D., Kayode, O., Bhatt, S., Gupta, M., Tosun, A.S.: Hierarchical federated learning based anomaly detection using digital twins for smart healthcare (2021)
Karagiannis, V., Frangoudis, P.A., Dustdar, S., Schulte, S.: Context-aware routing in fog computing systems. IEEE Trans. Cloud Comput. 1–1 (2021)
Konečný, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency (2016)
Krivic, P., Kusek, M., Cavrak, I., Skocir, P.: Dynamic scheduling of contextually categorised Internet of Things services in fog computing environment. Sensors 22(2), 465 (2022)
Madden, S.R., Franklin, M.J., Hellerstein, J.M., Hong, W.: TinyDB: an acquisitional query processing system for sensor networks. ACM Trans. Database Syst. 30(1), 122–173 (2005)
Openfog Consortium: OpenFog Reference Architecture for Fog Computing (2017)
Pham, V.N., Nguyen, V., Nguyen Tri, T., Huh, E.N.: Efficient edge-cloud publish/subscribe broker overlay networks to support latency-sensitive wide-scale IoT applications. Symmetry 12(1), 3 (2019)
Podnar Žarko, I., Antonić, A., Marjanović, M., Pripužić, K., Skorin-Kapov, L.: The OpenIoT approach to sensor mobility with quality-driven data acquisition management. In: Podnar Žarko, I., Pripužić, K., Serrano, M. (eds.) Interoperability and Open-Source Solutions for the Internet of Things, pp. 46–61. Springer International Publishing, Cham (2015). https://doi.org/10.1007/978-3-319-16546-2_5
Salaht, F.A., Desprez, F., Lebre, A.: An overview of service placement problem in fog and edge computing. ACM Comput. Surv. 53(3), 1–35 (2020)
Santos, J., Wauters, T., Volckaert, B., De Turck, F.: Resource provisioning in fog computing: From theory to practice \(\dagger \). Sensors 19(10), 2238 (2019)
University of Zagreb: IMUNES. http://imunes.net/
Zhou, Z., Yang, S., Pu, L., Yu, S.: CEFL: online admission control, data scheduling, and accuracy tuning for cost-efficient federated learning across edge nodes. IEEE Internet Things J. 7(10), 9341–9356 (2020)
Čilić, I., Podnar Žarko, I., Kušek, M.: Towards service orchestration for the cloud-to-thing continuum. In: 2021 6th International Conference on Smart and Sustainable Technologies (SpliTech), pp. 01–07 (2021)
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Čilić, I., Žarko, I.P. (2022). Adaptive Data-Driven Routing for Edge-to-Cloud Continuum: A Content-Based Publish/Subscribe Approach. In: González-Vidal, A., Mohamed Abdelgawad, A., Sabir, E., Ziegler, S., Ladid, L. (eds) Internet of Things. GIoTS 2022. Lecture Notes in Computer Science, vol 13533. Springer, Cham. https://doi.org/10.1007/978-3-031-20936-9_3
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