An Edge and Fog Computing Platform for Effective Deployment of 360 Video Applications

This paper has been presented at: Seventh International Workshop on Cloud Technologies and Energy Efficiency in Mobile Communication Networks (CLEEN 2019). How cloudy and green will mobile network and services be? 15 April 2019 - Marrakech, MoroccoIn press / En prensaImmersive video applications based on 360 video streaming require high-bandwidth, high-reliability and lowlatency 5G connectivity but also flexible, low-latency and costeffective computing deployment. This paper proposes a novel solution for decomposing and distributing the end-to-end 360 video streaming service across three computing tiers, namely cloud, edge and constrained fog, in order of proximity to the end user client. The streaming service is aided with an adaptive viewport technique. The proposed solution is based on the H2020 5G-CORAL system architecture using micro-services-based design and a unified orchestration and control across all three tiers based on Fog05. Performance evaluation of the proposed solution shows noticeable reduction in bandwidth consumption, energy consumption, and deployment costs, as compared to a solution where the streaming service is all delivered out of one computing location such as the Cloud.This work has been partially funded by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant num. 761586)

Public and non-public network integration for 5Growth Industry 4.0 use cases

5G is playing a paramount role in the digital transformation of the industrial sector, offering high-bandwidth, reliable, and low-latency wireless connectivity to meet the stringent and critical performance requirements of manufacturing processes. This work analyzes the applicability of 5G technologies as key enablers to support, enhance, and even enable novel advances in Industry 4.0. It proposes a complete 5G solution for two real-world Industry 4.0 use cases related to metrology and quality control. This solution uses 5Growth to ease and automate the management of vertical services over a soft-ware-defined network and network function virtualization based 5G mobile transport and computing infrastructure, and to aid the integration of the verticals' private 5G network with the public network. Finally, a validation campaign assesses the applicability of the proposed solution to support the performance requirements (especially latency and user data rate) of the selected use cases, and evaluates its efficiency regarding vertical service setup time across different domains in less than three minutes.This work has been partially supported by the EC H2020 5GPPP 5Growth project (Grant 856709) and the H2020 5G-EVE project (Grant 815074)

Towards Very Low-Power Mobile Terminals through Optimized Computational Offloading

This paper has been presented at 2020 IEEE International Conference on Communications WorkshopsEnergy consumption is a major issue for modern embedded mobile computing platforms, and with new technological developments, such as IoT and Edge/Fog computing, the number of connected embedded mobile computing systems is rapidly increasing. Heterogeneous multi-core CPUs seek to improve the performance of these platforms, with a particular focus on energy efficiency. By using different techniques like DVFS, core mapping, and multi-threading, a substantial improvement in the achievable CPU energy efficiency level for Multi-processor system-on-chip (MPSoC) can be observed. However, controlling only the CPU power dissipation has a limited effect on the overall platform energy consumption. Other components of the platform, including memory, disk, and other peripherals, play an important role in the energy efficiency of the platform and need to be taken into account. The availability of different sleep strategies at various levels of the platform makes the energy efficiency issue even more complex. In this paper, we set the view of energy efficiency at the entire platform level and discuss computation offloading as a mechanism to help in reaching the optimal platform energy-efficient state. As an application, we consider object detection performed on several types of images to define when offloading is beneficial to the platform energy efficiency. We survey the energy efficiency of different neural network algorithms in an embedded environment, with the possibility to perform computation offloading, and discuss the obtained results concerning the level of object recognition accuracy provided by different neural networks.This work has been partially funded by the H2020 EU/TW joint action 5G-DIVE (Grant no. 859881)