Reinforcement Learning Enabled Energy-Efficient Vbbu Pre-Migration in Cloud-Fog Based Elastic Optical Networks
Luyao Guan,Danshi Wang,Min Zhang,Chunyu Zhang,Zhendong Zhang +4 more
TL;DR: A reinforcement learning-based approach is proposed to optimize energy efficiency in cloud-fog elastic optical networks by pre-migrating vBBUs, achieving an 8.52% reduction in energy consumption while maintaining quality of service.
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Abstract: We propose a reinforcement learning enabled energy-efficient vBBU pre-migration in cloud-fog based elastic optical network. The results show scheme can efficiently reduce energy consumption by 8.52% under the premise of ensuring quality of service.
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References
Low-latency and energy-efficient BBU placement and VPON formation in virtualized cloud-fog RAN
Rodrigo Izidoro Tinini,Daniel Macedo Batista,Gustavo B. Figueiredo,Massimo Tornatore,Biswanath Mukherjee +4 more
TL;DR: A hybrid cloud-fog RAN (CF-RAN) architecture that resorts to fog computing and to network function virtualization to replicate the processing capacity of a CRAN in local fog nodes closer to the RRHs that can be activated on demand to process surplus fronthaul/cloud traffic.
Area-Aware Routing and Spectrum Allocation for the Tidal Traffic Pattern in Metro Optical Networks
TL;DR: A tidal traffic model to formulate a kind of tidal traffic phenomenon is proposed, the area-aware routing and spectrum allocation algorithm that focuses on the traffic adjustment in specific functional areas is proposed and two benchmark algorithms named min-hop k-shortest path routing algorithm and occupied-slots-as-weight k- shortest path routed algorithm are introduced.
Demonstration of AI-Assisted Energy-Efficient Traffic Aggregation in 5G Optical Access Network
Luyao Guan,Min Zhang,Danshi Wang +2 more
- 08 Mar 2020
TL;DR: An AI-assisted energy-efficient traffic aggregation scheme, which is demonstrated in software-defined optical network testbed, can efficiently reduce energy consumption by traffic aggregation according to traffic prediction.
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