Recently, Mobile Edge Computing (MEC) has used lightweight container-based microservices to provide resources for artificial intelligence applications, which will be decomposed into multiple dependent components, forming a Directed Acyclic Graph (DAG). In MEC, users will partition the input of the computation-intensive tasks into multiple sub-tasks for parallel execution acceleration. To satisfy concurrency, app vendors must deploy multiple container replicas for a microservice. Due to the limited capacity of edge servers, containers need to be deployed into geographically distributed and heterogeneous edge servers, resulting in significant inter-edge server traffic. To this end, we propose an adaptive scheme to guide microservice deployment for data partition-based applications in the MEC. We model the multi-replica microservice deployment problem as an integer programming problem to minimize operation costs. We propose a Deterministic Local Search-based Microservice Deployment algorithm (DLSMD), that chooses a superior neighborhood solution iteratively to solve it. We also formulate a more general problem considering both computing and communication time to minimize the total completion time and devise a Heuristic Microservice Deployment (HMD) algorithm to solve it. Extensive simulation results show that DLSMD and HMD outperform other benchmarks, achieving up to $4\times$ and $3.12\times$ speedups in terms of the inter-server traffic and total completion time reduction, respectively.

The steel plate is one of the main products in steel industries, and its surface quality directly affects the final product performance. How to detect surface defects of steel plates in real time during the production process is a challenging problem. The single or fixed model compression method cannot be directly applied to the detection of steel surface defects, because it is difficult to consider the diversity of production tasks, the uncertainty caused by environmental factors, such as communication networks, and the influence of process and working conditions in steel plate production. In this paper, we propose an adaptive model compression method for steel surface defect online detection based on expert knowledge and working conditions. First, we establish an expert system to give lightweight model parameters based on the correlation between defect types and manufacturing processes. Then, lightweight model parameters are adaptively adjusted according to working conditions, which improves detection accuracy while ensuring real-time performance. The experimental results show that compared with the detection method of constant lightweight parameter model, the proposed method makes the total detection time cut down by 23.1%, and the deadline satisfaction ratio increased by 36.5%, while upgrading the accuracy by 4.2% and reducing the false detection rate by 4.3%.