We propose Meta-DPMAPPO /metə,dipi'mæpəʊ/, a a metaverse multi-user service migration framework that combines Multi-Agent Proximal Policy Optimization (MAPPO) with Differential Privacy (DP)-enabled dual-domain perturbation. To maintain usability, we incorporate trajectory topology constraints that balance privacy strength with data availability. The framework enables dynamic service migration, i.e., transferring services to follow mobile users, to ensure low-latency access while safeguarding sensitive user data. We design a migration strategy with multiple migration actions (i.e.,reuse,follow, andnomigration) to minimize global delay and improve resource utilization. We conduct a series of experiments using a combination of public, collected, and synthetic datasets. The results demonstrate that our approach significantly reduces global migration delay in multi-user environments while ensuring privacy protection, and adapts well to different metaverse application scenarios.

In mobile edge computing (MEC), mobile users can offload tasks to edge nodes to alleviate local computational loads, leveraging the computing capabilities of edge nodes. However, users' high mobility and temporal variability pose challenges in dynamically allocating mobile users to optimize perceived Quality of Service (QoS). To address this challenge, this paper proposes an adaptive ant colony algorithm for user allocation decisions. This method constructs hidden mobility fitness relationships between users and servers based on user movement trajectories. It utilizes an improved adaptive ant colony algorithm to adjust fitness values automatically and optimize user allocation. The goal is to maximize overall user satisfaction under resource constraints while minimizing user allocation costs. Experimental analysis demonstrates that the proposed method achieves higher user allocation rates and effectively utilizes available resources on edge servers.

The emergence of the metaverse signifies a paradigm shift in Internet technology, offering a comprehensive virtual social platform spanning various domains such as social interaction, gaming, healthcare, and tourism. This new era of the metaverse is facilitated by advancements in next-generation digital technologies including edge computing, artificial intelligence, virtual reality, augmented reality, and blockchain. In the metaverse, the quantity and variety of services requested by users may surpass those in other environments, and existing work cannot be applied to metaverse QoS (Quality of Service) optimization. To address this problem, this paper proposes Meta-PPO, an optimization method for enhancing the QoS of metaverse services using reinforcement learning. Firstly, metaverse services are categorized into virtual scene services and meta-services, providing a comprehensive framework for analysis. Secondly, Meta-PPO, based on the proximal policy optimization algorithm, is introduced to optimize the QoS of metaverse services. This method effectively balances the objectives of minimizing average delay and maximizing resource utilization of mobile devices by making informed offloading decisions for the identified service categories. Simulation results demonstrate the superiority of the proposed method over existing techniques, showcasing its suitability and effectiveness for enhancing the QoS of metaverse service.

Mobile edge computing (MEC) relies on offloading tasks to edge nodes to avoid delays and failures caused by local computing. However, developing efficient offloading decisions is challenging, as it involves addressing the intricacies of tasks and the instability of edge node resources(e.g. available computer resources, memory, and bandwidth). In this paper, we propose a novel approach to tackle the problem of task offloading. Our approach involves dividing tasks into smaller units and considering the correlations between these sub-tasks. To make optimal offloading decisions, we employ a deep reinforcement learning algorithm that takes into account user movement patterns and the availability of resources at edge nodes. Through simulations, we demonstrate that our proposed algorithm outperforms several existing algorithms in terms of offloading decisions. It effectively reduces task execution delays and energy costs. These findings highlight the potential of our approach in improving the performance of task offloading in MEC systems.