Runtime application observability is crucial not only for system provenance but also for the orchestration of deployed microservices in dynamic sandboxed distributed computing environments. Also, log extraction and aggregation in highly distributed and sandboxed environments pose significant challenges, especially when preserving the causal order of the events triggered by different asynchronous microservices running over multiple hosts. However, ensuring causally consistent logging of application events is challenging, although it is vital for continuously tracing and profiling the underlying platform. This paper proposesXPLOG, a scalable, pluggable, easily deployable, and dynamic runtime observability framework for distributed sandboxed computing platforms that leverages the capability of extended Berkeley Packet Filters (eBPF) to intercept system-level events within the host while capturing and amalgamating relevant application and system logs to produce globally causally-consistent log streams. Through qualitative and quantitative analysis, we observe thatXPLOGsignificantly improves log richness with minimum system overhead while preserving the causality of log-generating events across multiple microservices.

The shift from monolithic services to microservices brings modularity and elasticity, but detecting faults and anomalies is challenging due to diverse data and evolving technology. The heterogeneous nature of this data complicates the analysis of anomaly signatures across various dimensions. Given the continuous evolution of this technology, exhaustively learning from historical data poses difficulties. To address these challenges, we present URCD, a solution designed to identify and localize faults or anomalies at the application and service level. Remarkably, URCD achieves this without explicit training on faulty data. Our approach integrates heterogeneous microservice data into a bidirectional weighted graph, leveraging a sophisticated Hyper Graph Attention Network (HGAN) model to process heterogeneous data logs generated by microservices. Our evaluation shows the optimal performance of URCD while detecting root cause of anomalies.