… However, properly deploying services among resource-constrained edge servers is an …
propose a deep reinforcement learning approach to preferably deploy the services to the edge …

… deployment approach is needed to handle the real-time network variations and various service
… online, deep reinforcement learning approach to automatically deploy SFCs for requests …

… preferable service deployment solutions. Our algorithm leverages reinforcement learning
and neural network to learn a deployment strategy without any human instruction. Therefore, …

… Lastly, we apply deep reinforcement learning algorithm on the process of VNF deployment
and backup, then we propose PA-DRL model to deploy and backup VNFs with reliability …

… service function chain deployment scheme DRL-Deploy based on deep reinforcement
learning… , ie, the physical networks, we use learning agents based on a new type of neural …

… reinforcement learning-based SFCs deployment algorithm. … Approach Multi-agent reinforcement
learning can achieve … In this paper, we introduce a multi-agent reinforcement learning …

… In this paper, we consider the deployment problem as a Markov decision process (MDP) [9], …
reinforcement learning algorithm to formulate the optimal data-intensive application deploy…

… horizontally and vertically using reinforcement learning. In their work, service placement of
… A service Pj has the following requirements for deployment Pj ¼ ½Pjcpu ;Pjmem ;Pjdisk ;Pjk …

… In this work, we formulate the SFC deployment problem … reinforcement learning (RL)-based
algorithm for partitioning the SFC request across the different InPs while considering service …

… the scheduling optimization problem through reinforcement learning methods. Furthermore,
Xue … a deep reinforcement learning approach to prioritize the deployment of services to edge …