Monitoring and debugging modern cloud-based applications is challenging since even a single API call can involve many interdependent distributed microservices. To provide observability for such complex systems, distributed tracing frameworks track request flow across the microservice call tree. However, such solutions require instrumenting every component of the distributed application to add and propagate tracing headers, which has slowed adoption. This paper explores whether we can trace requests without any application instrumentation, which we refer to as request trace reconstruction. To that end, we develop TraceWeaver, a system that incorporates readily available information from production settings (e.g., timestamps) and test environments (e.g., call graphs) to reconstruct request traces with usefully high accuracy. At the heart of TraceWeaver is a reconstruction algorithm that uses request-response timestamps to effectively prune the search space for mapping requests and applies statistical timing analysis techniques to reconstruct traces. Evaluation with (1) benchmark microservice applications and (2) a production microservice dataset demonstrates that TraceWeaver can achieve a high accuracy of ~90% and can be meaningfully applied towards multiple use cases (e.g., finding slow services and A/B testing).

Modern cloud-based applications have complex inter-dependencies on both distributed application components as well as network infrastructure, making it difficult to reason about their performance. As a result, a rich body of work seeks to automate performance diagnosis of enterprise networks and such cloud applications. However, existing methods either ignore inter-dependencies which results in poor accuracy, or require causal acyclic dependencies which cannot model common enterprise environments.

As modern cloud services have scaled out, applications have moved from relatively monolithic designs to highly modularized fleets of microservices that communicate among each other to perform application-level tasks. These microservices effectively form a network at the application layer, and service mesh frameworks have recently emerged to factor out microservices' common communication functionality.

We present a vision of data-informed network simulation to address significant shortcomings in the state of the art. We substantiate our position with proof points based on iBox, which leverages networking domain knowledge and machine learning (ML) models, coupled with plentiful data, to provide a pathway to perpetual renewal of network simulators.