Managing microservice architectures in distributed systems is complex and resource-intensive due to the high frequency and dynamic nature of inter-service interactions. Accurate prediction of these future interactions can enhance adaptive monitoring, enabling proactive maintenance and resolution of potential performance issues before they escalate. This study introduces a Graph Neural Network (GNN)-based approach, specifically using a Graph Attention Network (GAT), for link prediction in microservice Call Graphs. Unlike social networks, where interactions tend to occur sporadically and are often less frequent, microservice Call Graphs involve highly frequent and time-sensitive interactions that are essential to operational performance.

As software evolves, regression testing techniques are typically used to ensure the new changes are not adversely affecting the existing features. Despite recent advances, regression testing for distributed systems remains challenging and extremely costly. Existing techniques often require running a failing system several time before detecting a regression. As a result, conventional approaches that use re-execution without considering the inherent non-determinism of distributed systems, and providing no (or low) control over execution are inadequate in many ways. In this paper, we present MRegTest, a replay-based regression testing framework in the context of model-driven development to facilitate deterministic replay of traces for detecting regressions while offering sufficient control for the purpose of testing over the execution of the changed system. The experimental results show that compared to the traditional approaches that annotate traces with timestamps and variable values MRegTest detects almost all regressions while reducing the size of the trace significantly and incurring similar runtime overhead.

Ordering and replaying of execution traces of distributed systems is a challenging problem. State-of-the-art approaches annotate the traces with logical or physical timestamps. However, both kinds of timestamps have their drawbacks, including increased trace size. We examine the problem of determining consistent orderings of execution traces in the context of model-driven development of reactive distributed systems, that is, systems whose code has been generated from communicating state machine models. By leveraging key concepts of state machines and existing model analysis and transformation techniques, we propose an approach to collecting and reordering execution traces that does not rely on timestamps. We describe a prototype implementation of our approach and an evaluation. The experimental results show that compared to reordering based on logical timestamps using vector time (clocks), our approach reduces the size of the trace information collected by more than half while incurring similar runtime overhead.