SCIENCE@home

BSTL: Bayesian STL for Predictive Edge Service Monitoring With Probabilistic Guarantee

Deng Zhao,Zhangbing Zhou,Xiaoyan Meng,Xiao Xue,Ruixi Pan,Walid Gaaloul

Published 2026 in IEEE Transactions on Services Computing

ABSTRACT

Edge service monitoring is essential for ensuring the robustness and efficiency of service executions, where predictive monitoring enables proactive detection of potential service violations. Current approaches for predictive monitoring, which mostly adopt <italic>S</italic>ignal <italic>T</italic>emporal <italic>L</italic>ogic (<italic>STL</italic>) specifications for requirements representation and evaluation, primarily focus on deterministic signals, and thus, may lack probabilistic guarantees for uncertainty interpretation. To address these challenges, this paper proposes <italic>B</italic>ayesian <italic>STL</italic> (<italic>BSTL</italic>), an extension of <italic>STL</italic> that enables probabilistic reasoning over stochastic signals. Specifically, <italic>B</italic>ayesian <italic>N</italic>eural <italic>N</italic>etworks (<italic>BNNs</italic>) are employed to generate sequences of posterior probability distributions, offering more comprehensive predictive insights compared to traditional point- or interval-based methods with deterministic sequential predictions. Uncertainty interpretation over these distribution predictions is achieved by a novel expected robustness metric that jointly quantifies both the degree and probability of service satisfaction. Thereafter, a <italic>BSTL</italic>-based predictive monitoring framework is developed, where a service constraint is formally specified by a <italic>BSTL</italic> formula and interpreted with both qualitative and quantitative semantics. Besides, confidence levels and constraint thresholds ensuring robust satisfaction of a <italic>BSTL</italic> formula are rigorously estimated. Extensive experiments on publicly available datasets demonstrate that <italic>BSTL</italic> outperforms baseline techniques in terms of expressiveness, robustness, and applicability.

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-21 of 21 references · Page 1 of 1

CITED BY

  • No citing papers are available for this paper.

Showing 0-0 of 0 citing papers · Page 1 of 1