An optimal edge server placement approach for cost reduction and load balancing in intelligent manufacturing

With the development of the intelligent manufacturing, a large amount of data will be generated at the edge of the network. Industrial data have unprecedented requirements for transmission speed and quality. Edge computing makes full use of the computing power of the terminal, which realizes real-time processing of data. As the first step of the edge computing, the deployment of edge servers is the foundation and key. However, unreasonable edge server deployment strategies cannot meet the requirements of data processing in intelligent manufacturing. In this paper, we establish an edge server deployment optimization model to optimize deployment cost and load balance. Reliability is an important point in intelligent manufacturing, so we propose a fault-tolerant server deployment scheme. When the edge server fails, the fault-tolerant server can replace the fault server in time to ensure the normal workflow. To solve the above model, we propose a binary-based gray wolf genetic strategy algorithm, which improves the global optimal solution of the algorithm. Simulations reveal that up to 10.97 $$\%$$ of the total load can be saved by using the gray wolf genetic algorithm and an average of 16.15 $$\%$$ time savings can be achieved using our proposed method during hours.