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- W4313141168 abstract "This article presents a novel probabilistic QoS (Quality of Service) monitoring approach called LSTM-BSPM (DonLSTM-Den based BayeSian Runtime Proactive Monitoring), which is based on the DouLSTM-Den model and Gaussian Hidden Bayesian Classifier for mobile edge environments. A DouLSTM-Den model is designed to predict a user’s trajectory in mobile edge environments. The predicted trajectory is leveraged to obtain the mobility-aware QoS and capture its spatio-temporal dependency. Next, a parent attribute is constructed for each QoS attribute to reduce the influence of dependence between QoS attributes on monitoring accuracy. A Gaussian hidden Bayes classifier is trained for each edge server to proactively monitor the user’s mobility-aware QoS. We conduct a set of experiments respectively upon a public data set and a real-world data set demonstrate the feasibility and effectiveness of the proposed approach." @default.
- W4313141168 created "2023-01-06" @default.
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- W4313141168 date "2022-01-01" @default.
- W4313141168 modified "2023-10-16" @default.
- W4313141168 title "Mobility-Aware Proactive QoS Monitoring for Mobile Edge Computing" @default.
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- W4313141168 doi "https://doi.org/10.1007/978-3-031-20984-0_9" @default.
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