Considering edge devices have limited resources, it's critical to keep track of their current resource usage and device resource allocation algorithms that send containers to edge and cloud nodes according to their priority. To minimize the strain on edge devices and enable the running of mission-critical applications, edge containers may need to be transferred to a cloud platform. In this paper, we suggested a mechanism for prioritizing container balance between the edge and cloud while attempting to assign delay-sensitive containers to edge nodes. We assess the performance of Docker container management systems on resource-constrained computers and offer ways for reducing administration and migration overhead based on the workload type, to bring load balance to the systems. The proposed algorithm gives flexibility to get the best possible ways to achieve load balancing. The Tensorflow’s object discovery API was used, accessed with Flask, Python's micro-web framework. Docker container management technology was used in the implementation of this application.