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Addressing Application Latency Requirements through Edge Scheduling

  • Atakan AralEmail author
  • Ivona Brandic
  • Rafael Brundo Uriarte
  • Rocco De Nicola
  • Vincenzo Scoca
Open Access
Article
  • 41 Downloads

Abstract

Latency-sensitive and data-intensive applications, such as IoT or mobile services, are leveraged by Edge computing, which extends the cloud ecosystem with distributed computational resources in proximity to data providers and consumers. This brings significant benefits in terms of lower latency and higher bandwidth. However, by definition, edge computing has limited resources with respect to cloud counterparts; thus, there exists a trade-off between proximity to users and resource utilization. Moreover, service availability is a significant concern at the edge of the network, where extensive support systems as in cloud data centers are not usually present. To overcome these limitations, we propose a score-based edge service scheduling algorithm that evaluates network, compute, and reliability capabilities of edge nodes. The algorithm outputs the maximum scoring mapping between resources and services with regard to four critical aspects of service quality. Our simulation-based experiments on live video streaming services demonstrate significant improvements in both network delay and service time. Moreover, we compare edge computing with cloud computing and content delivery networks within the context of latency-sensitive and data-intensive applications. The results suggest that our edge-based scheduling algorithm is a viable solution for high service quality and responsiveness in deploying such applications.

Keywords

Edge computing Scheduling Live streaming 

Notes

Acknowledgements

This work has been partially funded by the Rucon project (Runtime Control in Multi Clouds), FWF Y 904 START-Programm 2015, by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838949, and by the Italian National Interuniversity Consortium for Informatics (CINI).

Funding Information

Open access funding provided by Austrian Science Fund (FWF).

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© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Vienna University of TechnologyViennaAustria
  2. 2.IMT School for Advanced Studies LuccaLuccaItaly

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