Privacy-Aware Forecasting of Quality of Service in Mobile Edge Computing

卷:16

Article

关键词: QOS PREDICTION;    PLACEMENT;   

DOI  :  10.1109/TSC.2021.3137452

来源: SCIE

【 摘 要 】

We propose a novel privacy-aware Quality of Service (QoS) forecasting approach in the mobile edge environment - Edge-PMAM (Edge QoS forecasting with Public Model and Attention Mechanism). Edge-PMAM can make real-time, accurate and personalized QoS forecasting on the premise of user privacy preservation. Edge-PMAM comprises a public model for privacy-aware QoS forecasting in an edge region and a private model for personalized QoS forecasting for an individual user. An attention mechanism atop Long Short-Term Memory and an automated edge region division solution are devised to enhance the prediction accuracy of the public and private models. We conduct a series of experiments based on public and self-collected data sets. The results demonstrate that our approach can effectively improve forecasting performance and protect user privacy.

【 授权许可】

Free