关键词： Mobile edge computing   Mobile augmented reality   Deep reinforcement learning   Resource allocation  

摘要： Mobile Edge Computing(MEC)and 5G technology allow clients to access computing resources at the network frontier,which paves the way for applying Mobile Augmented Reality(MAR)*** the MEC paradigm,MAR clients can offload complex tasks to the MEC server and enhance the human perception of the world by merging the received virtual information with the real ***,the resource allocation problem arises as a critical challenge in circumstances where several MAR clients compete for limited resources at the network *** this paper,we aim to design an online resource allocation scheme on the MEC server that takes both high quality of experience and good fairness performance for MAR clients into *** first formulate this problem as a Markov decision process and tackle the challenge of applying the deep reinforcement learning ***,we propose DRAM,a Deep reinforcement learning-based Resource allocation scheme for mobile Augmented reality service in *** also propose a self-adaptive algorithm on the MAR client that is derived based on the analysis of the MAR service to tackle client adaptation *** simulation results demonstrated that DRAM can provide high quality of experience and simultaneously achieve good fairness performance by coordinating with clients’adaptation algorithms.

关键词： mobile edge computing   Internet of Things   dynamic offloading   hybrid energy supply   stochastic optimization  

摘要： Mobile edge computing(MEC),as a new distributed computing model,satisfies the low energy consumption and low latency requirements of computation-intensive *** task offloading of MEC has become an important research hotspot,as it solves the problems of insufficient computing capability and battery capacity of Internet of things(IoT)*** study investigates task offloading scheduling in a dynamic MEC *** integrating energy harvesting technology into IoT devices,we propose a hybrid energy supply *** jointly optimize local computing,offloading duration,and edge computing decisions to minimize system *** the basis of stochastic optimization theory,we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called *** can make task offloading decisions by weighing system cost and queue *** quote dynamic programming theory to obtain the optimal task offloading *** results verify the effectiveness of DTOME,and show that DTOME entails lower system cost than two baseline task offloading strategies.

关键词： Peer-to-peer computing   Wireless communication   Task analysis   Games   Reinforcement learning   Multi-access edge computing   Heuristic algorithms   Mobile edge computing   edge node grouping   game theory   reinforcement learning  

摘要： In mobile edge computing networks, densely deployed access points are empowered with computation and storage capacities. This brings benefits of enlarged edge capacity, ultra-low latency, and reduced backhaul congestion. This paper concerns edge node grouping in mobile edge computing, where multiple edge nodes serve one end user cooperatively to enhance user experience. Most existing studies focus on centralized schemes that have to collect global information and thus induce high overhead. Although some recent studies propose efficient decentralized schemes, most of them did not consider the system uncertainty from both the wireless environment and other users. To tackle the aforementioned problems, we first formulate the edge node grouping problem as a game that is proved to be an exact potential game with a unique Nash equilibrium. Then, we propose a novel decentralized learning-based edge node grouping algorithm, which guides users to make decisions by learning from historical feedback. Furthermore, we investigate two extended scenarios by generalizing our computation model and communication model, respectively. We further prove that our algorithms converge to the Nash equilibrium with upper-bounded learning loss. Simulation results show that our mechanisms can achieve up to 96.99% of the oracle benchmark.

关键词： Mobile edge computing  

摘要： The rapid development of artificial intelligence (AI) over massive applications including Internet-of-things on cellular network raises the concern of technical challenges such as privacy, heterogeneity and resource efficiency. Federated learning is an effective way to enable AI over massive distributed nodes with security. However, conventional works mostly focus on learning a single global model for a unique task across the network, and are generally less competent to handle multitask learning (MTL) scenarios with stragglers at the expense of acceptable computation and communication cost. Meanwhile, it is challenging to ensure the privacy while maintain a coupled multi-task learning across multiple base stations (BSs) and terminals. In this paper, inspired by the natural cloud-BS-terminal hierarchy of cellular works, we provide a viable resource-aware hierarchical federated MTL (RHFedMTL) solution to meet the heterogeneity of tasks, by solving different tasks within the BSs and aggregating the multi-task result in the cloud without compromising the privacy. Specifically, a primal-dual method has been leveraged to effectively transform the coupled MTL into some local optimization sub-problems within BSs. Furthermore, compared with existing methods to reduce resource cost by simply changing the aggregation frequency, we dive into the intricate relationship between resource consumption and learning accuracy, and develop a resource-aware learning strategy for local terminals and BSs to meet the resource budget. Extensive simulation results demonstrate the effectiveness and superiority of RHFedMTL in terms of improving the learning accuracy and boosting the convergence rate. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile edge computing   Deep reinforcement learning   Edge intelligence   Server placement   Internet of things  

摘要： In the era of 5G and beyond, Mobile Edge Computing (MEC) has emerged as a technology that seamlessly integrates wireless networks and the Internet, enabling low-latency and high-reliability computing services for mobile users. A crucial prerequisite for deploying MEC is the strategic selection of edge server locations that can satisfy computing demands and improve resource utilization. In this paper, we study the problem of efficient and intelligent dynamic edge server placement considering time-varying network states and placement costs. We present a long-term dynamic decision-making process that models edge server placement as a Markovian decision process and dynamically adjusts server layout. To achieve intelligent decision-making, we propose two deep reinforcement learning-based algorithms. Namely, the DBPA algorithm based on D3QN and the PBPA algorithm based on PPO, which significantly improve the efficiency and performance of model training. We also propose a novel method for transforming network states into network inputs using heat map and grayscale map to enhance the agent's learning efficiency. Experimental results demonstrate that our proposed algorithms achieve intelligent and dynamic placement of edge servers, and outperform comparison algorithms by 13.20% to 61.84% and 23.09% to 66.32%, respectively.

关键词： Task analysis   Containers   Image edge detection   Scheduling   Feature extraction   Heuristic algorithms   Scheduling algorithms   Mobile edge computing   dependency-aware scheduling   container   reinforcement learning  

摘要： Due to the features of lightweight and easy deployment, the use of containers has emerged as a promising approach for Mobile Edge Computing (MEC). Before running the container, an image composed of several layers must exist locally. However, it has been conspicuously neglected by existing work that task scheduling at the granularity of the layer instead of the image can significantly reduce the task completion time to further meet the real-time requirement and resource efficiency in resource-limited MEC. To bridge the gap, considering the complex dependency between layers and images, a novel layer dependency-aware container scheduling algorithm is proposed to reduce the total task completion time. Specifically: 1) We model the online layer dependency-aware scheduling problem for containers in a heterogeneous MEC, considering the layer download time and task computation time. 2) A policy gradient algorithm is proposed to solve this problem, and the high-dimensional and low-dimensional relations for layer dependencies are extracted with improved action selection. 3) Experiments based on the real-world data trace show that the proposed algorithm outperforms the image-based and layer-based baseline algorithms by 54% and 19% on average, respectively.

关键词： Multi-Access Edge Computing   experimentation   edge computing   mobile edge computing  

摘要： Multi-Access Edge Computing (MEC) attracts much attention from the scientific community due to its scientific, technical, and commercial implications. In particular, the European Telecommunications Standards Institute (ETSI) standard convergence consolidates the discussions around MEC. Still, the existing MEC practical initiatives are incomplete in their majority, hardening or invalidating their effective deployment. To fill this gap, it is essential to understand a series of experimental prototypes, implementations, and deployments. The early implementations can reveal the potential, the limitations, the related technologies, and the development tools for MEC adoption. In this context, this work first brings a discussion on existing MEC initiatives regarding the use cases they target and their vision (i.e., whether they are more network-related or more distributed systems). Second, we survey MEC practical initiatives according to their strategies, including the ETSI MEC standard. Besides, we compare the strategies according to related limitations, impact, and deployment efforts. We also survey the existing tools making MEC systems a reality. Finally, we give hints to issues yet to be addressed in practice. By bringing a better comprehension of MEC initiatives, we believe this survey will help researchers and developers design their own MEC systems or improve and simplify the usability of existing ones.

关键词： internet of vehicles (IoV)   vehicle to everything (V2X)   computing offloading   mobile edge computing   game theory  

摘要： Due to the rocketing development of the Internet of Vehicles (IoV), the growth of computing-intensive and latency-sensitive applications brings a challenge to individual vehicles with limited computing resources. The computation offloading technology provides a feasible solution to this issue. In this paper, a multi-tier symmetric Vehicle-to-Everything (V2X) network framework is proposed, which consists of vehicle nodes (VNs), mobile edge computing (MEC) servers and a cloud server to provide computation offloading services for user vehicles. In this symmetric system, besides local computation, tasks can be offloaded to VNs or MEC servers or cloud servers for processing. The computation offloading problem in this network framework is considered as a game based on game theory. Then, in order to achieve the Nash equilibrium (NE) in this game, a joint optimization of computation offloading and resource allocation (JOCORA) algorithm is proposed. The numerical simulations show that the JOCORA algorithm can improve the success probability of offloading and reduce the total latency. The JOCORA algorithm has a better performance compared to other methods.

关键词： Task analysis   Servers   Manganese   NOMA   Resource management   Optimization   Downlink   Mobile edge computing   non-orthogonal multiple access   latency-minimization  

摘要： In this letter, we consider a multi-server mobile edge computing (MEC) network where a user with limited computation ability aims to complete an intensive computation task in a short time under the help of multiple edge servers. We study the delay-minimization offloading schemes with and without user's energy constraint, which involve the joint optimization of task allocation ratio for local computation, the wireless offloading manner (i.e., non-orthogonal multiple access (NOMA) offloading or single-server offloading mode), and the server pair selection. Specifically, the joint optimal solutions are derived in semi-closed forms and the critical conditions of switching offloading manners are derived in closed form. Numerical results corroborate the superiority of our proposed offloading scheme in terms of overall latency under various settings.

关键词： RIS   Mobile edge computing   Profit  

摘要： This paper focuses on the profit maximization problem in a reconfigurable intelligent surfaces (RIS) aided computing network, where multiple heterogeneous users offload their computational tasks to one computational access point (CAP) for seeking computing acceleration at the cost of profit. In particular, the CAP can also pre-store a part of the computing task to speed up computing, and the system has limited communication and computing resources, where heterogeneous users have different offloading requirements and the CAP can dynamically allocate the system resources to meet the requirements of users to earn profits. To maximize the system profit, we devise the system by proposing a resource allocation scheme which employs a genetic algorithm (GA), based on statistical channel state information (CSI) of wireless links. The proposed algorithm maximizes the long-term profit of the system by optimizing resource allocation among users. Finally, simulation results are provided to verify the proposed scheme. The results show that our proposed resource allocation scheme outperforms the conventional ones.