Enhancing the Reliability of Microservice Workflows through Concurrent Artifact Anomaly Detection

Microservice-Based Workflows (MBWs) are used popularly to govern the composition and coordination of individual microservices to realize business processes. With MBWs, designers often aim to maximize concurrency to increase the chances of successful workflow collaboration and enhance business process efficiency. However, operations involving manipulating and accessing artifacts (data objects) within these workflows may introduce anomalies leading to unexpected artifact states. In workflow design phase, seeking bug-free artifact states is vital to help prevent crashes, errors, and unexpected outcomes during execution. Concurrent artifact anomalies are referred to abnormal parallel operations on the same artifact. Few studies have explored the detection of concurrent anomalies in MBWs, and they are inefficient and ineffective as they struggle with high time complexity and are insensitive to the presence of nested AND gateways. This paper focuses on improving microservices workflows reliability by detecting concurrent anomalies in artifacts during the design. We present a series of methods to detect the anomalies based on SP-tree, a tree structure to record workflow paths and artifact information. Our methods outperform existing ones by detecting more anomalies with lower time and space complexity.