Abstract
Renting very high bandwidth or special connection links is neither affordable nor economical for service providers. As a consequence, ensuring data streaming systems to be able to guarantee desired service quality experienced by the users has been a challenging issue due to real-time changes in the network performance of the Internet communications. This paper presents a network monitoring approach that is broadly applicable in the adaptation of real-time services running on network edge computing platforms. The approach identifies runtime variations in the network quality of links between application servers and end-users. It is shown that by identifying critical conditions, it is possible to continuously adapt the deployed service for optimal performance. Adaptation possibilities include reconfiguration by dynamically changing paths between clients and servers, vertical scaling such as re-allocation of bandwidth to specific links, horizontal scaling of application servers, and even live-migration of application components from one edge server to another to improve the application performance.
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WebRTC, https://webrtc.org/.
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CipSoft, http://www.cipsoft.com/.
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Medooze, http://www.medooze.com/.
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Kubernetes, http://kubernetes.io/.
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Acknowledgements
This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 643963 (SWITCH project: Software Workbench for Interactive, Time Critical and Highly self-adaptive cloud applications).
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Taherizadeh, S., Taylor, I., Jones, A., Zhao, Z., Stankovski, V. (2017). A Network Edge Monitoring Approach for Real-Time Data Streaming Applications. In: Bañares, J., Tserpes, K., Altmann, J. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2016. Lecture Notes in Computer Science(), vol 10382. Springer, Cham. https://doi.org/10.1007/978-3-319-61920-0_21
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DOI: https://doi.org/10.1007/978-3-319-61920-0_21
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