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.

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 (MetaTransformer andMAPPO basedComputationOffloading), 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.

We propose a new workflow task execution time prediction approach, DAG-FGL, by integrating Flash attention mechanism with a GraphLSTM model. It addresses the challenge of low task execution time prediction accuracy in the presence of complex dependencies among workflow subtasks. The GraphLSTM model captures and conveys subtask dependencies through the adjacency matrix of sub task relationships modeled as a directed acyclic graph (DAG). The Flash attention mechanism enhances the model by incorporating customized positional encoding of subtask priority. The encoding ensures that the model accurately reflects each subtask's importance and relative order when calculating attention weight. Therefore, DAG- FG L can more accurately predict task execution time in the context of complex dependencies. Experimental results show that DAG-FGL outperforms the best-performing baseline model. It achieves 7.82 % to 44.52 % improvements in prediction accuracies over the best-performing baselines across three cloud workflow datasets of varying lengths.

Mobile edge computing (MEC) enables cooperative vehicle infrastructure systems (CVIS) to provide computational services to vehicles via roadside units. However, the increasing complexity of intelligent vehicles leads to the generation of numerous delay-sensitive tasks, presenting significant challenges for computation offloading. Furthermore, frequent interactions between intelligent vehicles and servers during the offloading process exacerbate privacy risks. In this paper, we propose a novel resource discovery approach, which determines the priority of offloading tasks based on an incentive strategy and constructs a task queue to optimize resource utilization. To facilitate optimal task offloading decisions, we employ a federated reinforcement learning algorithm that integrates dynamic differential privacy, addressing the dynamic nature of privacy protection requirements and enhancing the security of edge nodes. Through simulation, we demonstrate that our proposed algorithm outperforms traditional approaches by effectively reducing task execution latency and preserving vehicular privacy. The experimental findings highlight the potential of our approach to significantly improve task offloading performance in CVIS.

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.

With the rapid development of 5G technologies, the demand of quality of service (QoS) from edge users, including high bandwidth and low latency, has increased dramatically. QoS within a mobile edge network is highly dependent on the allocation of edge users. However, the complexity of user movement greatly challenges edge user allocation, leading to privacy leakage. In addition, updating massive data constantly in a dynamic mobile edge network also crucial to ensure efficiency. To address these challenges, this paper proposes a dynamic QoS optimization strategy (MENIFLD_QoS) in mobile edge networks based on incremental learning and federated learning.MENIFLD_QoSoptimizes service cache in edge regions and allocates edge servers to edge users according to the locations of edge servers accessed by edge users in mobile scenarios. While optimizing regional service quality, the system can effectively protect user privacy. In addition, for dynamic incremental data,MENIFLD_QoStrains updated data based on the strategy of incremental learning hence significantly improves optimization speed. Experimental results on an edge QoS dataset show that the proposed strategy achieves global optimization in both multi-variable and multi-peak user allocation scenarios and notably enhances the training efficiency of the regional invocation model.

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/ (MECQoS forecasting based onRegion recognition andDynamicEchoStateNetwork) 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 adynamic echo state networkcharacterized 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.

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.

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 realtime, 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 based on public and self-collected data sets demonstrate that our approach can effectively improve forecasting performance and protect user privacy.

Quality of Service (QoS) is well acknowledged as a decisive means for ascertaining the performance of third-party Web services. QoS has high uncertainty in complex and dynamic network environments. QoS monitoring is considered as one of the most effective techniques to detect QoS violations at runtime. However, existing QoS monitoring approaches only consider single QoS attribute and do not provide a promising solution for comprehensively monitoring multivariate QoS attributes. To overcome this problem, a novel QoS monitoring approach, named M-BSRM (MultivariateBayeSianRuntimeMonitoring), is proposed. First, M-BSRM adopts the point mutual information theory to initialize the weights of different environmental impact factors and solves the problem of uneven distribution between classes brought by traditional algorithms. Second, each single QoS attribute is integrated with user preference using the information fusion theory. Finally, a Bayesian classifier is used to comprehensively evaluate multivariate QoS attributes at runtime. The experimental results on both the real-world and simulated data sets show that M-BSRM is more effective, practical, and efficient than the other approaches.

Mobile Edge Computing is an emerging technology offering low latency responses by deploying edge servers near mobile devices. We propose a novel privacy-preserving QoS forecasting approach – Edge-Laplace QoS (QoS forecasting with Laplace noise in mobile Edge environments) to address the challenges of user mobility and information leakage encountered by QoS forecasting in mobile edge environments. Edge-Laplace QoS is able to accurately and efficiently forecast Quality of Service (QoS) of various Web Services, while effectively protecting user privacy in mobile edge environments. We employ an improved differential privacy method to add dynamic disguises to the original QoS data in the edge environment to protect user data privacy. A collaborative filtering method is adopted to retrieve similar users’ accessing records based on geographic locations of their accessed servers for QoS forecasting. We conduct a set of experiments using several public network data sets. The results show that the efficiency of Edge-Laplace QoS is superior to traditional forecasting approaches. Edge-Laplace QoS is also validated to be more suitable for edge environments than traditional privacy-preserving approaches.

Mobile edge computing is a new computing paradigm that performs computing on the edge of a network. It provides services to users by deploying edge servers near mobile devices. Services may be unavailable or do not satisfy the needs of users due to changing edge environments. Quality of service (QoS) is commonly employed as a critical means to indicate qualitative status of services. It is particularly important to monitor QoS of services timely and effectively in the mobile edge environment. However, user mobility and dependencies among QoS values often cause the monitoring results to deviate from the real results in the mobile edge environment. Existing QoS monitoring approaches have not taken into account these problems. To address the problems, this article proposes ghBSRM-MEC (GaussianhiddenBayeSianRuntimeMonitoring forMobileEdgeComputing), a novel mobility and dependence-aware QoS monitoring approach for the mobile edge environment. This approach assumes that the QoS attribute values of edge servers obey Gaussian distribution. It constructs a parent property for each property, thus reducing the dependence between properties. During the training stage, a Gaussian Hidden Bayesian classifier is constructed for each edge server. During the monitoring stage, combining with a KNN algorithm, the classifier is changed dynamically based on user mobility to realize QoS monitoring in the mobile edge environment. The experimental results validate the feasibility, effectiveness, and efficiency of ghBSRM-MEC.

We propose aLong-term Quality of Service (QoS) forecasting approach usingAdvertisement andLevenberg-Marquardt improvedRadialBasisFunction (LA-LMRBF)—a novel online QoS forecasting approach. LA-LMRBF aims to accurately predict QoS attributes of Web services in the form of multivariate time series via three stages. First, the phase space reconstruction theory is employed to restore multi-dimensional and nonlinear relations among the multivariate QoS attributes. Second, short-term QoS advertisement data is incorporated to enable long-term QoS forecasting. Finally, an optimized Radial Basis Function (RBF) neural network is constructed to forecast long-term multivariate QoS values, where the Affinity Propagation clustering algorithm is used to determine the number of hidden nodes and the Levenberg-Marquardt (LM) algorithm is utilized to dynamically update some parameters of the RBF neural network. A series of experiments are performed on a mixture of public and self-collected data sets. The results show that LA-LMRBF is superior to the other approaches and more suitable for long-term QoS forecasting.

This paper proposes a novel security-aware QoS (Quality of Service) forecasting approach - Edge QoS Per-PM (Edge QoS forecasting with Personalized training based on Public Models in mobile edge computing) by migrating the principle of integrating cooperative learning and independent learning from federated learning. Edge QoS Per-PM can make fast and accurate forecasting on the premise of ensuring enhanced security. We train private model based on public model for personalized forecasting. The private models are invisible to other users to ensure the absolute security. At regular intervals, a Long Short-Term Memory (LSTM) model is trained based on the latest private data to meet the realtime requirements of the dynamic edge environment and ensure the accuracy of prediction results. A series of experiments is conducted based on public network data sets. The results demonstrate that Edge QoS Per-PM can train appropriate models and achieve faster convergence and higher accuracy.

Mobile edge computing transfers computing and storage from traditional cloud servers to edge servers, presenting new challenges to quality assurance of edge services. Quality of Service (QoS) is considered as a defacto standard to evaluate similar services with different quality. Given the fact that QoS values are highly dynamic in complex edge environments, QoS monitoring is viewed as a promising technique to comprehensively and effectively understand QoS status of edge services. Due to the distributed storage of historical QoS data and the changeable edge environments, traditional QoS monitoring approaches cannot be directly applied into mobile edge computing. To address this problem, this paper proposes a novel multivariate QoS monitoring approach, called Rs-mBSRM (multivariate BayeSian Runtime Monitoring using Rough set), First, the weights of different QoS attributes are quantified and obtained according to the historical samples based on rough set theory. Second, a Bayesian classifier is constructed for each corresponding edge server during the training stage. Finally, during the monitoring stage, considering the distributed data storage, the classifier is dynamically switched and the attribute weights are also updated due to user mobility. Our experimental results on public data sets show that Rs-mBSRM is better than existing QoS monitoring approaches and is more suitable for mobile edge computing.

How to assure Quality of Service (QoS) of the third-party services is very important for the SOA. Effective monitoring technique towards QoS, which is an important measurement for third-party service quality, is necessary to ensure quality of Web service. Current monitoring approaches do not consider the influences of environment factors such as the position of server, user usage, and the load at runtime. Ignoring these influences, which do exist among the monitoring process, may cause existing monitoring approaches producing unpredictable monitoring results. In order to overcome this limitation, this paper proposes a novel Web Service QoS (WS-Qos) monitoring approach sensitive to environmental factors called weighted Bayesian Runtime Monitor (wBSRM) based on weighted naïve Bayesian classifiers and Term Frequency-Inverse Document Frequency (TF-IDF) algorithm. wBSRM constructs weighted naïve Bayesian classifier by learning a part of samples to classify the monitoring results. The results meeting QoS standard are classified as [Formula: see text] and the one that does not meet is classified as [Formula: see text]. Classifier can also output ratio between posterior probability of [Formula: see text] and [Formula: see text], and consequently the analysis can lead to three monitoring results including [Formula: see text], [Formula: see text] or inconclusive. A set of dedicated experiments are conducted to validate wBSRM. The experiments are based on a public dataset and a simulated dataset under the given standard. The experimental results demonstrate that wBSRM is better than previous approaches.