Availability-aware and energy-aware dynamic SFC placement using reinforcement learning

Author(s):  
Guto Leoni Santos ◽  
Theo Lynn ◽  
Judith Kelner ◽  
Patricia Takako Endo
Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 543
Author(s):  
Soyi Jung ◽  
Won Joon Yun ◽  
Joongheon Kim ◽  
Jae-Hyun Kim

This paper proposes a novel coordinated multi-agent deep reinforcement learning (MADRL) algorithm for energy sharing among multiple unmanned aerial vehicles (UAVs) in order to conduct big-data processing in a distributed manner. For realizing UAV-assisted aerial surveillance or flexible mobile cellular services, robust wireless charging mechanisms are essential for delivering energy sources from charging towers (i.e., charging infrastructure) to their associated UAVs for seamless operations of autonomous UAVs in the sky. In order to actively and intelligently manage the energy resources in charging towers, a MADRL-based coordinated energy management system is desired and proposed for energy resource sharing among charging towers. When the required energy for charging UAVs is not enough in charging towers, the energy purchase from utility company (i.e., energy source provider in local energy market) is desired, which takes high costs. Therefore, the main objective of our proposed coordinated MADRL-based energy sharing learning algorithm is minimizing energy purchase from external utility companies to minimize system-operational costs. Finally, our performance evaluation results verify that the proposed coordinated MADRL-based algorithm achieves desired performance improvements.


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 209750-209761 ◽  
Author(s):  
Anh Vu Le ◽  
Rizuwana Parween ◽  
Phone Thiha Kyaw ◽  
Rajesh Elara Mohan ◽  
Tran Hoang Quang Minh ◽  
...  

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