Intelligent service orchestration in edge cloud networks

The surge in data traffic is challenging for network infrastructure owners coping with stringent service requirements (e.g., high bandwidth, ultralow latency) as well as shrinking per-gigabyte revenues. Network softwarization and edge computing are powerful candidates to mitigate these issues. In parallel, there is an increasing demand for network virtualization and container-based services. In this study, we investigate the management of software defined networking (SDN)-based transport network and edge cloud service orchestration. To this end, we use a machine learning (ML)-based design to manage both transport and edge cloud resources of a mobile network effectively. To generate and use real-world data inside our ML platform, we use the Graphical Network Simulator-3 (GNS3) emulator environment. Our emulation results indicate that almost all of the trained ML models can accurately select the correct edge clouds (ECs) (i.e., with high test accuracy) under the considered two scenarios when transport and EC network parameters are considered in comparison to models trained via only transport or cloud-based parameters. At the end of the article, we also provide an evolved architecture where the proposed ML platform can be embedded in an end-to-end mobile network architecture and H2020 5Growth project's baseline management platform.This work has been partially funded by the EU H2020 5Growth Project (grant no. 856709), by MINECO grant TEC2017-88373-R (5G-REFINE), and Generalitat de Catalunya grant 2017 SGR, 1195