… As a first step towards understanding the potential of RL for resource management, we
design (§3) and evaluate (§4) DeepRM, a simple multi-resource cluster scheduler. DeepRM …

… As a model-free approach, our RL-based edge computing resource management framework
… proposed RL-based resource management framework as shown in Fig. 1, which consists of …

… traditional resource management. First, resource management includes the coordination
of cloud environment, physical resources, virtual resources, which is a vast project.Besides, …

… have proposed solutions to manage resources in NSL using, … resource management
approaches based on reinforcement … Remarkably, they allow evolving resource management in …

… resource management and interference mitigation in wireless networks using multi-agent
deep reinforcement learning (RL). We equip each transmitter in the network with a deep RL …

… , a multi-dimensional resource management problem is formulated to … deep reinforcement
learning based resource management scheme is proposed to make real-time optimal resource …

… resource management consistent with users’ activities per slice. In that regard, deep reinforcement
… some typical resource management for network slicing scenarios, which include radio …

… in cloud computing community are motivated to revisit cloud resource/workload management
problem by applying the emerging deep reinforcement learning (DRL) method. In this …

… ) and data flow, IIoT experiences spectrum resource scarcity for wireless applications. In this
article, we propose a solution for spectrum resource management for the IIoT network, with …

… a global tier for VM resource allocation to the servers and a local tier for distributed power
management of local servers. The emerging deep reinforcement learning (DRL) technique, …