This paper presents a novel probabilistic Quality of Service (QoS) monitoring method named DLSTM-BRPM (Double Long Short Term Memory (DouLSTM-Den) based Bayesian Runtime Proactive Monitoring) to accurately and efficiently monitor QoS in a mobile edge environment. This method consists of a DouLSTM-Den model and a Gaussian Hidden Bayesian classifier. The DouLSTM-Den model aims to predict a user’s future movement trajectory in real time and proactively monitor the spatio-temporal QoS performance of services based on the predicted trajectory. The Gaussian Hidden Bayesian classifier is employed to accurately monitor QoS by constructing parent attributes to reduce the interdependence between QoS attributes. Our experiments based on public synthetic datasets demonstrate the effectiveness of the proposed method over state-of-the-art solutions. We also conducted experiments in a real-world edge environment to validate the feasibility of the proposed method.

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.

The rapid growth of immersive metaverse services demands stringent control over quality of service (QoS) and quality of experience (QoE) in heterogeneous mobile edge computing (MEC) environments. Existing scheduling methods oversimplify task characteristics, failing to address the distinct QoS and QoE needs of scene rendering and avatar interaction tasks. To overcome this, we propose MROA (Meta Reinforcement Learning for Offloading and Allocation), a hierarchical framework that jointly optimizes computation offloading and resource allocation across virtual, physical, management, and edge layers. By leveraging Reptile-based meta-reinforcement learning (Meta-RL), MROA adapts rapidly to dynamic workloads while addressing scene-level and avatar-level heterogeneity. We introduce the jitter-frame-rate ratio (JFR), a unified QoE metric that quantifies rendering stability and user experience impact. Experimental results on 2,500 diverse directed acyclic graph (DAG) workloads demonstrate that MROA reduces delay by 25%, reduces energy consumption by 18%, and improves JFR by 22%, demonstrating notable improvements in these key metrics over existing methods. These findings confirm MROA as a scalable and efficient solution for computation offloading and resource allocation in the metaverse.

The metaverse is a pioneering cyber-physical space that seamlessly blends the physical and virtual worlds, demanding a fully immersive and highly interactive user experience. However, existing communication designs and traditional offloading and allocation approaches fall short of meeting the dynamic network conditions and real-time performance demands of the metaverse. To tackle these challenges, we develop a wireless transmission architecture optimized for metaverse computation offloading and resource allocation. This design leverages the Rayleigh fading and Multiple Input Multiple Output (MIMO) technology to optimize transmission paths, enhancing the reliability and efficiency of signal transmission in high-concurrency connections. We further introduce MetaTMCO (Meta Transformer and MAPPO based Computation Offloading), a dynamic online computation offloading and resource allocation joint optimized method that meets critical QoS requirements for metaverse applications. MetaTMCO uses a metric named Interaction Frequency (IF) to evaluate resource competition between users and resource interaction between users and the environment, combining QoS to maximize utility under resource constraints. By integrating a Transformer-based encoder-decoder within the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm, MetaTMCO mitigates inefficiencies from partial observations, enabling dynamic optimization of real-time fast offloading strategies across multiple agents. Experimental results demonstrate that MetaTMCO significantly outperforms other approaches in metaverse environments, achieving superior strategy optimization and resource efficiency.