Joint caching and computing service placement for edge-enabled IoT based on deep reinforcement learning
Y Chen, Y Sun, B Yang, T Taleb - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
… IoT facilities, edge computing can satisfy various … joint caching and computing service
placement (JCCSP) problem for sensingdata-driven IoT applications. Then, deep reinforcement …
placement (JCCSP) problem for sensingdata-driven IoT applications. Then, deep reinforcement …
Joint optimization of caching, computing, and radio resources for fog-enabled IoT using natural actor–critic deep reinforcement learning
… Natural policy-gradientbased deep RL algorithm to learn the … a joint optimization solution for
content caching, computing … of joint solution for content caching and computation offloading. …
content caching, computing … of joint solution for content caching and computation offloading. …
Energy efficient joint computation offloading and service caching for mobile edge computing: A deep reinforcement learning approach
… process the relevant computation tasks. … computing and caching resources without
considering the collaboration among ESs. This paper considers a joint optimization of computation …
considering the collaboration among ESs. This paper considers a joint optimization of computation …
Joint computing and caching in 5G-envisioned Internet of vehicles: A deep reinforcement learning-based traffic control system
… -based traffic control system by investigating Deep Reinforcement Learning (DRL) for …
computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly …
computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly …
Joint computation offloading and resource allocation for edge-cloud collaboration in internet of vehicles via deep reinforcement learning
… and computation-intensive tasks in IoV. We study the joint optimization of computation …
dynamic optimization problem, and apply a deep reinforcement learning (DRL) technique to …
dynamic optimization problem, and apply a deep reinforcement learning (DRL) technique to …
[HTML][HTML] Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing
… to relieve the heavy computational burdens and realize an efficient computation offloading.
… resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q …
… resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q …
Joint offloading and resource allocation using deep reinforcement learning in mobile edge computing
… energy sustainability jointly. On the other hand, for a real-time mobile edge computing (MEC) …
In this paper, we propose an energy-efficient algorithm based on deep reinforcement …
In this paper, we propose an energy-efficient algorithm based on deep reinforcement …
Joint service caching and computation offloading scheme based on deep reinforcement learning in vehicular edge computing systems
… To fill this gap, we aim at joint computation offloading and service caching, taking full
advantage of the limited resources of vehicles and edge nodes as well as edge cooperative …
advantage of the limited resources of vehicles and edge nodes as well as edge cooperative …
Joint secure offloading and resource allocation for vehicular edge computing network: A multi-agent deep reinforcement learning approach
… 1) We propose a DRL-based joint SORA scheme in the multi-user offloading scenario using
… on jointly optimizing the transmit power, the spectrum access selection and the computation …
… on jointly optimizing the transmit power, the spectrum access selection and the computation …
Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks
… is the joint optimization of individual computing mode (ie, … a deep reinforcement learning-based
online offloading (DROO) framework to maximize the weighted sum of the computation …
online offloading (DROO) framework to maximize the weighted sum of the computation …
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