Microservice Extraction Using Graph Deep Clustering Based on Dual View Fusion

With the increasing scale of software, traditional monolithic architecture applications are challenging to maintain and scale on cloud platforms. Many companies increasingly adopt microservices architecture as a more flexible choice. However, microservice migration is still challenging due to the lack of higher-quality microservice extraction methods. Traditional microservice extraction methods cannot effectively combine the structural dependency and business functions of monolithic applications; thus, their performance warrants improvement. This paper proposes a graph deep clustering method based on dual view fusion (GDC-DVF) for microservice extraction. GDC-DVF constructs a graph of invocation relationships between classes, which is the structural dependency view, using the runtime trace data of a monolithic application. Then the business function view is constructed by the random walk algorithm and uniform random sampling using the structural dependency view. Next, the fused node feature embedding representations of the two views are learned using a graph encoder based on a graph attention adaptive residual network. Clustering is performed on the fused feature embedding representations to obtain microservice extraction proposals. GDC-DVF is tested on four open-source monolithic applications and achieves better performance compared with comparison methods. Experimental results show that GDC-DVF can extract high-quality microservice collections and validate the effectiveness and scalability of the graph neural network (GNN) for microservice extraction problems.