As a novel computing technology closer to business ends, edge computing has become an effective solution for delay sensitive business of power Internet of Things (IoT). However, the uneven spatial and temporal distribution of business requests in edge network leads to a significant difference in business busyness between edge nodes. Due to the natural lightweight and portability, container migration has become a critical technology for load balancing, thereby optimizing the resource utilization of edge nodes. To this end, this paper proposes a container migration-based decision-making (CMDM) mechanism in power IoT. First, a load differentiation matrix model between edge nodes is constructed to determine the timing of container migration. Then, a container migration model of load balancing joint migration cost (LBJC) is established to minimize the impact of container migration while balancing the load of edge network. Finally, the migration priority of containers is determined from the perspective of resource correlation and business relevance, and by introducing a pseudo-random ratio rule and combining the local pheromone evaporation with global pheromone update at the same time, a migration algorithm based on modified Ant Colony System (MACS) is designed to utilize the LBJC model and thus guiding the choice of possible migration actions. The simulation results show that compared with genetic algorithm (GA) and Space Aware Best Fit Decreasing (SABFD) algorithm, the comprehensive performance of CMDM in load balancing joint migration cost is improved by 7.3% and 12.5% respectively. INDEX TERMS container migration, load balancing, migration cost, Edge computing, power Internet of things.

With the development and the characteristics of terminal services of the 5G (5th-Generation network) Internet of Vehicles (IoVs), this paper proposes a distributed splitting strategy for multi-type services of 5G V2X (Vehicle to X) networks. Based on a service-oriented adaptive splitting strategy in heterogeneous networks, combined with various service types such as communications between the networks, terminal, and base stations, and the value-added services of 5G IoVs, the proposed strategy jointly considers delay and cost as optimization goals. By analyzing the characteristics of the different services, the proposed traffic flow splitting strategy is modeled as an optimization problem to efficiently split services in 5G V2X networks. The simulation results show that by setting the traffic distribution policy for each service, the distributed traffic flow splitting strategy can significantly improve network transmission efficiency and reduce the service costs in a vehicle V2X network.

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