With the advent of a new round of the Industrial Revolution, the Industrial Internet will carry the convergence of heterogeneous network and the dynamic reconfiguration of industrial equipment. In order to further provide higher performance of network capabilities, the Industrial Internet has experienced unprecedented growth while facing enormous challenges from the actual needs of industrial networks. The typical scenarios in industrial applications, combined with the technical advantages of mobile edge computing, are described in view of the low latency, high bandwidth and high reliability demanded by the Industrial Internet in the new era. The key technologies of mobile edge computing for the Industrial Internet have been outlined in this treatise, whose feasibility and importance are demonstrated by typical industrial applications that have been deployed. As combined with the development trend of the Industrial Internet, this paper summarizes the existing work and discusses the future research direction of key technologies of mobile edge computing for the Industrial Internet.

Aiming at the local overload of multi-controller deployment in software-defined networks, a load balancing mechanism of SDN controller based on reinforcement learning is designed. The initial paired migrate-out domain and migratein domain are obtained by calculating the load ratio deviation between the controllers, a preliminary migration triplet, contains migration domain mentioned above and a group of switches which are subordinated to the migrate-out domain, makes the migration efficiency reach the local optimum. Under the constraint of the best efficiency of migration in the whole and without migration conflict, selecting multiple sets of triples based on reinforcement learning, as the final migration of this round to attain the global optimal controller load balancing with minimum cost. The experimental results illustrate that the mechanism can make full use of the controllers' resources, quickly balance the load between controllers, reduce unnecessary migration overhead and get a faster response rate of the packet-in request.

In view of the inability of traditional interdomain routing schemes to meet the sudden network changes and adapt the routing policy accordingly, many optimization schemes such as modifying Border Gateway Protocol (BGP) parameters and using software-defined network (SDN) to optimize interdomain routing decisions have been proposed. However, with the change and increase of the demand for network data transmission, the high latency and flexibility of these mechanisms have become increasingly prominent. Recent researches have addressed these challenges through multiagent reinforcement learning (MARL), which can be capable of dynamically meeting interdomain requirements, and the multiagent Markov Decision Process (MDP) is introduced to construct this routing optimization problem. Thus, in this paper, an interdomain collaborative routing scheme is proposed in interdomain collaborative architecture. The proposed Feudal Multiagent Actor-Critic (FMAAC) algorithm is designed based on multiagent actor-critic and feudal reinforcement learning to solve this competition-cooperative problem. Our multiagent learns about the optimal interdomain routing decisions, focused on different optimization objectives such as end-to-end delay, throughput, and average delivery rate. Experiments were carried out in the interdomain testbed to verify the convergence and effectiveness of the FMAAC algorithm. Experimental results show that our approach can significantly improve various Quality of Service (QoS) indicators, containing reduced end-to-end delay, increased throughput, and guaranteed over 90% average delivery rate.