Energy‐aware disaster backup among cloud datacenters using multiobjective reinforcement learning in software defined network

2021 ◽  
Author(s):  
Shanwen Yi ◽  
Xiaole Li ◽  
Hua Wang ◽  
Yao Qin ◽  
Jiaxin Yan
Keyword(s):  
Reinforcement Learning ◽  
Software Defined Network ◽  
Energy Aware

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

2021 ◽  
Author(s):  
Guto Leoni Santos ◽  
Theo Lynn ◽  
Judith Kelner ◽  
Patricia Takako Endo
Keyword(s):  
Reinforcement Learning ◽  
Energy Aware

Constructing Energy-Aware Software-Defined Network Virtualization

10.7125/40.3 ◽  
2015 ◽  
Vol 40 (0) ◽  
pp. 14
Author(s):  
Nam Manh Tran ◽  
Thanh Huu Nguyen ◽  
Van Hong Nguyen ◽  
Long Bao Kim ◽  
Lam Duc Nguyen ◽  
...  
Keyword(s):  
Network Virtualization ◽  
Software Defined Network ◽  
Energy Aware

scholarly journals A Deep Reinforcement Learning Based Switch Controller Mapping Strategy in Software Defined Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 221553-221567
Author(s):  
Jia Chen ◽  
Shihua Chen ◽  
Xin Cheng ◽  
Jing Chen
Keyword(s):  
Reinforcement Learning ◽  
Software Defined Network ◽  
Mapping Strategy

scholarly journals Coordinated Multi-Agent Deep Reinforcement Learning for Energy-Aware UAV-Based Big-Data Platforms

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 543
Author(s):  
Soyi Jung ◽  
Won Joon Yun ◽  
Joongheon Kim ◽  
Jae-Hyun Kim
Keyword(s):  
Big Data ◽  
Reinforcement Learning ◽  
Resource Sharing ◽  
Learning Algorithm ◽  
Energy Resource ◽  
Energy Aware ◽  
Aerial Surveillance ◽  
Performance Improvements ◽  
Multi Agent ◽  
Energy Sharing

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.

scholarly journals Energy-Aware Routing in Software-Defined Network using Compression

2018 ◽  
Vol 61 (10) ◽  
pp. 1537-1556 ◽  
Author(s):  
Frédéric Giroire ◽  
Nicolas Huin ◽  
Joanna Moulierac ◽  
Truong Khoa Phan
Keyword(s):  
Software Defined Network ◽  
Energy Aware ◽  
Energy Aware Routing
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