Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, th...

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Veröffentlicht in:	IEEE internet of things journal 2019-06, Vol.6 (3), p.4005-4018
Hauptverfasser:	Chen, Xianfu, Zhang, Honggang, Wu, Celimuge, Mao, Shiwen, Ji, Yusheng, Bennis, Medhi
Format:	Artikel
Sprache:	eng
Schlagworte:	<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Q -function decomposition Algorithms Computation offloading Deep reinforcement learning Edge computing Electronic devices Knowledge management Machine learning Markov analysis Markov chains Markov decision process (MDP) Markov processes Mobile communication systems Mobile computing mobile-edge computing (MEC) Network slicing network virtualization Policies Queues Radio access networks radio access networks (RANs) Radio equipment Reinforcement learning Wireless communication
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