FEDAMON: A Forecast-Based, Error-Bounded and Data-Aware Approach to Continuous Distributed Monitoring
Paper i proceeding, 2025
accurate monitoring. Instead of transmitting all observed values to the central coordinator, our event-based monitoring leverages lightweight forecasting models at edge nodes. Both the coordinator and distributed nodes predict the evolution of local values, communicating only when deviations exceed a predefined error threshold. To adapt to dynamically changing trends in data streams, we introduce a data-aware model selection strategy that optimizes
the balance between communication frequency and monitoring accuracy. Our solution is evaluated on diverse datasets and results demonstrate a substantial reduction in communication overhead with minimal impacts on accuracy, outperforming baseline monitoring regarding communication complexity, e.g., sending, on average, only 10% of baseline update events while maintaining less than 2% average error across all monitored streams. Furthermore, we show that our standard parameter solution even surpasses the best calibrated single models, achieving up to a 17% improvement in
communication overhead with identical guarantees on maximum error. Optimizing the control factor in data-aware approach leads to a 13% improvement in performance, reducing error by 1%, without incurring additional communication costs. We believe our approach offers a scalable and efficient solution, enabling fully automatic, real-time monitoring with optimized performance.
network monitor- ing
distributed tracking
continuous monitoring
distributed data streams
data-aware approaches
Författare
Yixing Zhang
Chalmers, Data- och informationsteknik, Dator- och nätverkssystem
Göteborgs universitet
Romaric Duvignau
Chalmers, Data- och informationsteknik, Dator- och nätverkssystem
DEBS 2025 - Proceedings of the 19th ACM International Conference on Distributed and Event-based Systems
39-50
979-8-4007-1332-3 (ISBN)
Gothenburg, Sweden,
READY: Rethinking Monitoring for Large Distributed Systems
Data- och informationsteknik, 2024-03-01 -- 2029-03-01.
Ämneskategorier (SSIF 2025)
Datavetenskap (datalogi)
DOI
10.1145/3701717.3730544
ISBN
9798400713323