correlate timestamps across systems

In distributed systems, clock asynchrony impedes fair and deterministic total ordering of events. Method: This paper proposes a probabilistic event serialization framework that replaces the deterministic “happened-before” relation with a novel “likely-happened-before” probabilistic ordering. It models per-node clock skews as time-varying probability distributions and performs Bayesian comparison of noisy timestamps to infer the most probable causal order. Crucially, it requires no strong clock synchronization assumptions and operates online. Contribution/Results: The approach enables fine-grained, statistically fair total order generation without global consensus or physical clock alignment. Experiments demonstrate accurate estimation of relative occurrence probabilities between events. The method provides a provably sound, scalable foundation—both theoretically and empirically—for achieving system-wide fairness in asynchronous environments.

Network congestion induces delay jitter in time synchronization packets, significantly degrading the clock synchronization accuracy of protocols such as NTP and PTP. This work proposes a lightweight congestion marking mechanism that leverages existing unused fields in IP, PTP, or NTP headers to tag synchronization packets experiencing queuing delays on programmable switches (Tofino platform), without requiring deep packet inspection or protocol modifications, thereby preserving backward compatibility. At the receiver, statistical filtering strategies—combining minimum RTT and median delay estimates—effectively discard congestion-affected packets. Experimental results demonstrate that the proposed approach improves synchronization accuracy by over 80% in single-hop scenarios and reduces clock offset estimation error by 30%–80% in multi-hop environments, achieving up to a 90% performance gain over conventional filtering methods.

High-precision online estimation algorithms for robotics are highly sensitive to sensor timestamp accuracy; however, existing synchronization solutions struggle to simultaneously achieve real-time operation, low cost, and high temporal precision. To address this, we propose a real-time, trigger-based time synchronization system built on commodity hardware. Our approach employs a hardware-triggered mechanism to jointly schedule heterogeneous sensors operating at different frequencies, and integrates an enhanced clock synchronization protocol with nanosecond-resolution timestamping to ensure precise coordination between sensors and the onboard computer. Crucially, the system eliminates reliance on expensive dedicated timing hardware, thereby substantially mitigating the impact of timing errors on online estimation. Experimental evaluation on a physical robot platform demonstrates sub-microsecond synchronization accuracy, along with significant improvements in both estimation robustness and real-time performance.

The lack of systematic integration between Kafka design patterns and benchmarking methodologies hinders reproducible, evidence-based architectural decision-making for event-streaming systems. Method: This study systematically analyzes 42 academic and industrial publications (2015–2025) using bibliometric analysis and pattern induction, complemented by standardized (TPCx-Kafka, Yahoo Streaming Benchmark) and customized workload evaluations. Contribution/Results: We introduce the first unified Kafka architectural pattern taxonomy—comprising nine high-frequency patterns—and establish a pattern-to-benchmark mapping matrix. Additionally, we propose heuristic guidelines to support architecture-level decisions. Our work fills a critical gap in reproducible design methodology for event-streaming systems, significantly improving design quality and practical consistency across performance, fault tolerance, and cross-study comparability dimensions.

This work addresses the challenge of fair event ordering in distributed systems, where clock synchronization errors complicate deterministic sequencing. The authors propose Tommy, a novel sequencer that, for the first time, integrates social choice theory into event ordering. By modeling the statistical characteristics of clock synchronization errors, Tommy probabilistically compares noisy timestamps and reformulates the ordering task as a social choice problem, yielding a partially ordered sequence. Rather than attempting to eliminate clock inaccuracies, Tommy explicitly tolerates them, effectively handling the non-transitivity inherent in probabilistic comparisons. This approach ensures fairness in a probabilistic sense while significantly outperforming baseline methods based on Spanner’s TrueTime.

This work addresses the inconsistency in synchronization strategies arising from heterogeneous sensor sampling rates in asynchronous data stream monitoring by introducing a novel tempo type system for the RTLola language. By formally capturing the semantics of tempo annotations and integrating type-theoretic principles with machine-checked proofs in Coq, the system enables, for the first time, static verification of synchronization consistency across asynchronous streams. We develop this type system over a core fragment of RTLola and provide a machine-verified proof of its soundness, guaranteeing that users cannot express unrealizable synchronization logic. This ensures the temporal correctness of generated monitors, thereby enhancing the reliability of runtime verification in asynchronous settings.

This study demonstrates that even in functionally and performance-wise correct distributed AI inference systems, microsecond-level clock skew among nodes can induce observable causality violations. By injecting controlled clock offsets into a multi-node inference pipeline built on Kafka and ZeroMQ, the authors reveal—for the first time—the high sensitivity of such causal anomalies to temporal synchronization: as little as 5 ms of offset suffices to produce noticeable violations, with their manifestation dynamically evolving alongside relative clock drift. Crucially, while system throughput and output correctness remain unaffected, observability degrades significantly, underscoring the necessity of treating time as a first-class concern in the design and operation of distributed AI systems.

This work addresses the inefficiency of traditional Dynamic Time Warping (DTW) in aligning long sequences due to its inherently serial computation, which hinders effective GPU parallelization. The study presents the first systematic exploration of GPU-oriented parallel alternatives to DTW, introducing four novel algorithms. The first three employ rectangular block-based approximations to accelerate computation, while the fourth, termed ParDTW, achieves exact alignment through diagonal-wise parallelization. ParDTW integrates block matrix processing with a diagonal scheduling strategy, preserving full alignment accuracy while delivering 15–100× speedup over existing methods on long sequences. This breakthrough substantially overcomes the performance limitations of conventional DTW, establishing ParDTW as an efficient and practical solution for large-scale sequence alignment tasks.