Abstract
The back-ends of mobile apps usually use services executed on remote (e.g., cloud) machines. The transmission latency may though make the usage of remote machines a less efficient solution for data that need short analysis time. Thus, apps should further use machines located near the network edge, i.e., on the Fog. However, the combination of the Fog and the Cloud introduces the research question of when and how the right binding of the front-end to an edge instance or a remote instance of the back-end can be decided. Such a decision should not be made at the development or the deployment time of apps, because the response time of the instances may not be known ahead of time or cannot be guaranteed. To make such decisions at run-time, we contribute the conceptual model and the algorithmic mechanisms of an autonomic controller as a service. The autonomic controller predicts the response time of edge/remote instances of the back-end and dynamically decides the binding of the front-end to an instance. The evaluation results of our approach on a real-world app for a large number of datasets show that the autonomic controller makes efficient binding-decisions in the majority of the datasets, decreasing significantly the response time of the app.
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Notes
- 1.
The front-end includes the programming clients that interact with the service back-end.
- 2.
The locally stored datasets are synchronized to the remote storage. We do not consider the synchronization time in the current work.
- 3.
We assume at least an edge and a remote instance have been pre-deployed (whose endpoints are registered to the controller). We leave as future work the decision of the number of instances.
- 4.
The programming methods are explicitly defined in a SOAP-based API or they can be determined by parsing the suffix of the URI of the API in a RESTful API [21].
- 5.
- 6.
If the cardinality is not declared in parameter schemas, then our approach considers a large pre-defined value as an artificial cardinality.
- 7.
The relationship between the Dynamic Binding and the Service Proxy is UML composition (depicted by filled diamond) so as to hide the edge/remote instances from the front-end.
- 8.
Due to the algorithmic simplicity, we do not specify the functions UPDATE, ADD and SELECT.
- 9.
- 10.
- 11.
1.9 GHz CPU, 4 GB RAM, Android 8.0.
- 12.
2.70 GHz CPU, Intel Core i5-5257U, 64-bit Windows 10 Home, 8 GB RAM.
- 13.
2.2 GHz 2 vCPU, Intel Xeon E5 v4 (Broadwell) platform, 7.5 GB RAM, Windows server 2016 (the cost of renting a more powerful machine for our experiments was very high).
- 14.
We define the equally-sized intervals of the dataset sizes, (1, 4000], (4000, 8000] and (8000, 12000], which correspond to small-, medium-, and large-sized datasets, respectively.
- 15.
The scale of the y-axis in the first two charts is different from the scale in the next two charts.
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Acknowledgments
The work was partially funded from the Victoria University of Wellington in New Zealand. We further express many thanks to Prof. B. Pernici for her valuable reviews.
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Athanasopoulos, D., McEwen, M., Rainer, A. (2019). Mobile Apps with Dynamic Bindings Between the Fog and the Cloud. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_41
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