Swift and Accurate Mobility-Aware QoS Forecasting for Mobile Edge Environments

Abstract

We propose an innovative approach named MEC-RDESN /mek”r:dI’saIn/ ( MEC QoS forecasting based on R egion recognition and D ynamic E cho S tate N etwork) enabling mobility-aware and swift QoS forecasting in the mobile edge computing environment. MEC-RDESN offers efficient QoS forecasting while maintaining high accuracy. We can identify the edge region to which a user belongs in real time while moving by leveraging mobile sensing technology. We employ a dynamic echo state network characterized by multi-service adaptability to retain information about services invoked by users to ensure real-time training and forecasting accuracy. Our approach is validated through a series of experiments using both public and collected datasets. The experiments demonstrate that MEC-RDESN achieves the goal of fast forecasting while ensuring its forecasting accuracy in diverse application scenarios.