Causal Latency Modelling for Cloud Microservices

The use of microservices-based architectures is becoming more prominent due to their advantageous characteristics, such as manageability, scalability, and flexibility. However, their management can be complex, and their performance can be affected by high latencies, which can alter the Service Level Objective (SLO). In order to identify the causes of high latencies, we present a causal modelling framework which is capable of analysing and reconstructing latencies within a microservice-based architectures. To this end, we employ causal discovery to identify the causes of latencies. Our model integrates domain knowledge to impose constraints on the causal graph, ensuring the accuracy of the discovered relationships as well as accelerating the causal discovery. To validate our approach, we reconstruct latency metrics using machine learning techniques and demonstrate the effectiveness of our approach by accurately capturing the interrelationships between microservice resources. Our framework provides a better understanding of the causes of latencies that lead to SLO violations, and paves the way for sophisticated mechanisms that enable proactive management of cloud resources.