Optimization of Function Chaining on the Edge for IoT applications

Khaledi, Mohamed-Idriss; Hadji, Makhlouf; Elayoubi, Salah Eddine; Niyato, Dusit

doi:10.1109/wcnc49053.2021.9417362

Cited by 2 publications

(4 citation statements)

References 12 publications

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“…Moreover, the fog computing paradigm introduced above for industrial IoT poses the challenge of placing the processing functions on the continuum from devices to the cloud. Such placement can be optimized for minimizing energy consumption while ensuring performance [14], and the fog function placement may be integrated within the global 6G O-RAN architecture.…”

Section: O-ranmentioning

confidence: 99%

For an Energy Sober Network that Contributes to a Sustainable Society

Elayoubi,

Hardouin

2023

2023 IEEE Future Networks World Forum (FNWF)

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Sustainability and energy sobriety should guide the "why" and the "how" of 6G. It is mandatory that the 6G infrastructure and services have a very low environmental footprint, but also, and more importantly, the 6G technology should be designed on the 5G basis for enabling added-value services for the society and to mitigate the environment footprint. This paper presents a vision for 6G network as an enabler for a more sustainable society, e.g. by enabling the society of maintenance and care and fostering innovation in the industry. On the other hand, some technology enablers for an energy sober 6G network are presented, along with guidelines for network deployments and for the assessment of the global impact of 6G services.

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Section: O-ranmentioning

confidence: 99%

For an Energy Sober Network that Contributes to a Sustainable Society

Elayoubi,

Hardouin

2023

2023 IEEE Future Networks World Forum (FNWF)

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“…In our paper, we propose to investigate machine learning techniques based on Reinforcement Learning to reach near optimal solutions of the virtualized face detection optimization problem in edge infrastructures. Our objective is to train RL models to improve the quality of the solutions compared to the results of the first paper addressing this problem (see reference [8]).…”

Section: Related Workmentioning

confidence: 99%

“…The addressed problem of virtualized face detection optimization and mapping is NP-Complete. More details on the proof can be found in [8].…”

Section: Problem Complexitymentioning

confidence: 99%

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Reinforcement Learning Based Approach for Virtualized Face Detection at the Edge

Khebbache

Hadji

Khaledi

2021

2021 IEEE 22nd International Conference on High Performance Switching and Routing (HPSR)

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Real-time requirements in video streaming and processing are increasing and represent one of the major issues in industry 4.0 domains. In particular, Face Detection (FD) use-case has attracted the interest of industrial and academia researchers for various applications such as cyber-physical security, fault detection, predictive maintenance, etc. To ensure applications with real time performance, Edge Computing is a good approach which consists in bringing resources and intelligence closer to connected devices and hence, it can be used to cope with strong latency and throughput expectations. In this paper, we consider optimal routing, placement and scaling of virtualized face detection services at the edge. We propose an edge networking approach based on Integer Linear formulation to cope with small problem instances. A reinforcement learning solution is proposed to address larger problem sizes and scalability issues. We assess the performance of our proposed approaches through simulations and show advantages of the reinforcement learning approach to converge towards near-optimal solutions in negligible time.

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Optimization of Function Chaining on the Edge for IoT applications

Cited by 2 publications

References 12 publications

For an Energy Sober Network that Contributes to a Sustainable Society

For an Energy Sober Network that Contributes to a Sustainable Society

Reinforcement Learning Based Approach for Virtualized Face Detection at the Edge

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