Collaborative mobile edge computing (MEC) has emerged as a promising paradigm to enable low-capability edge nodes to cooperatively execute computation-intensive tasks. However, straggling edge nodes (stragglers) significantly degrade the performance of MEC systems by prolonging computation latency. While coded distributed computing (CDC) as an effective technique is widely adopted to mitigate straggler effects, existing CDC schemes exhibit two critical limitations: (i) They cannot successfully decode the final result unless the number of received results reaches a fixed recovery threshold, which seriously restricts their flexibility; (ii) They suffer from inherent poles in their encoding/decoding functions, leading to decoding inaccuracies and numerical instability in the computational results. To address these limitations, this paper proposes an approximated CDC scheme based on barycentric rational interpolation. The proposed CDC scheme offers several outstanding advantages. Firstly, it can decode the final result leveraging any returned results from workers. Secondly, it supports computations over both finite and real fields while ensuring numerical stability. Thirdly, its encoding/decoding functions are free of poles, which not only enhances approximation accuracy but also achieves flexible accuracy tuning. Fourthly, it integrates a novel BRI-based gradient coding algorithm accelerating the training process while providing robustness against stragglers. Finally, experimental results reveal that the proposed scheme is superior to existing CDC schemes in both waiting time and approximate accuracy.

The Artificial Intelligence Generated Content (AIGC) technique has gained significant traction for producing diverse content. However, existing AIGC services typically operate within a centralized framework, resulting in high response times. To address this issue, we integrate collaborative Mobile Edge Computing (MEC) technology to reduce processing delays for AIGC services. Current collaborative MEC methods primarily support single-server offloading or facilitate interactions among fixed Edge Servers (ESs), limiting flexibility and resource utilization across all ESs to meet the varying computing and networking requirements of AIGC services. We propose AMCoEdge, an adaptive multi-server collaborative MEC approach to enhancing AIGC service efficiency. The AMCoEdge fully utilizes the computing and networking resources across all ESs through adaptive multi-ES selection and dynamic workload allocation, thereby minimizing the offloading make-span of AIGC services. Our design features an online distributed algorithm based on deep reinforcement learning, accompanied by theoretical analyses that confirm an approximate linear time complexity. Simulation results show that our method outperforms state-of-the-art baselines, achieving at least an$11.04\%$reduction in task offloading make-span and a$44.86\%$decrease in failure rate. Additionally, we develop a distributed prototype system to implement and evaluate our AMCoEdge method for real AIGC service execution, demonstrating service delays that are$9.23\% - 31.98\%$lower than the three representative methods.

The Industrial Metaverse is driving a new revolution wave for smart manufacturing domain by reproducing the real industrial environment in a virtual space. Real-time synchronization and rendering of all industrial factors result in numerous time-sensitive and computation-intensive tasks, especially matrix multiplication. Distributed edge computing (DEC) can be exploited to handle these tasks due to its low-latency and powerful computing. In this paper, we propose an efficient and reliable coded DEC framework to compute large-scale matrix multiplication tasks. However, an existence of stragglers causes high computation latency that seriously limits the application of DEC in the Industrial Metaverse. To mitigate the impact of stragglers, we design a secure and flexible PolyDot (SFPD) code, which enables information theoretic security (ITS) protection. Several improvements can be achieved with the proposed SFPD. First, it can achieve a smaller recovery threshold than that of the existing codes in almost all settings. And compared with the original PolyDot codes, our SFPD code considers the extra workers required to add ITS protection. It also provides a flexible tradeoff between recovery threshold and communication & computation loads by simply adjusting two given storage parameters$p$and$t$. Furthermore, as an important application scenario, the SFPD code is employed to secure model training in machine learning, which can alleviate the straggler effects and protect ITS of raw data. The experiments demonstrate that the SFPD code can significantly speed up the training process while providing ITS of data. Finally, we provide comprehensive performance analysis which shows the superiority of the SFPD code.

The Metaverse is recognized as the next-generation Internet that provides immersive interaction experiences for users. Convolutional neural networks (CNNs) play a crucial role in providing strong immersive experiences in the Metaverse. However, the Metaverse faces challenges in meeting the escalating demands for computing and storage resources due to the explosive growth of convolution tasks, resulting in severe performance degradation. To tackle these issues, coded distributed computing (CDC) is commonly employed. In this paper, we first propose an efficient and reliable mobile-assisted CDC framework to perform large-scale CNN training tasks for the Metaverse. In this framework, the various mobile devices act as workers contributing their resources to collaborate with each other to complete convolution operation tasks. Furthermore, we design a novel resilient, secure, and private coded convolution (RSPCC) scheme for the proposed framework. The RSPCC scheme achieves several significant performances. First, it substantially reduces computation latency compared to conventional convolution. Second, it efficiently mitigates an adverse impact of straggling workers returning results exceedingly slow. Third, we integrate a verifiable computing approach into the encoding/decoding process to check the correctness of the final computation results. Fourth, the PSPCC scheme considers the existence of colluding workers, providing information-theoretic privacy protection for input data. Finally, experimental results demonstrate that our proposed RSPCC scheme can significantly reduce execution time while ensuring the correctness of computation results within the CDC-based Metaverse framework.

Leveraging mobile edge computing (MEC) for task offloading is an effective strategy to address the computational limitations of mobile devices. However, current offloading strategies largely cater to user-centric objectives, neglecting the motives of service providers, unintentionally diminishing their profits. In this work, we propose a novel allocation strategy to improve resource utilization efficiency based on a parallel edge computing system. Specifically, we jointly consider incentives and cross-server resource allocation in parallel-driven MEC. This approach facilitates the distribution of user workloads across multiple edge servers, enhancing system performance and efficiency. In our approach, we leverage the deep deterministic policy gradient (DDPG) algorithm to support task offloading decisions, maximizing the overall profits of service providers. Simulation results show that our approach, compared to non-parallel edge systems, efficiently boosts resource utilization by an average of 13.37%.