Dapper, a Large-Scale Distributed Systems Tracing Infrastructure

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

An Open-Source Benchmark Suite for Microservices and Their Hardware-Software Implications for Cloud & Edge Systems (2019, methodology): “1% on end-to-end latency in all cases, which is tolerable for such systems [26, 72, 76].”
Understanding Capacity-Driven Scale-Out Neural Recommendation Inference (2020, background): “Distributed tracing is a proven technique for debugging and performance investigations across inter-dependent services [34], [35].”
Advances and challenges in log analysis (2011, background): “There have been efforts to include message contexts automatically (X-Trace, 4 Dapper 12 ) or to infer them from message contents, 15 but these cannot completely capture the intents and expectations of the developer.”
Seer: Leveraging Big Data to Navigate the Complexity of Performance Debugging in Cloud Microservices (2019, methodology): “Systems like Dapper and GWP , for example, rely on distributed tracing (often at RPC level) and low-level hardware event monitoring respectively to detect performance abnormalities, while the Mystery Machine [24] leverages the large amount of logged data to extract the causal relationships between r”