关键词： Costs   Predictive models   Data models   Quality of service   Optimization   Servers   Digital twins   Mobile edge computing   digital twins   service migration   mobile traffic prediction   deep reinforcement learning  

摘要： In Mobile Edge Computing (MEC) networks, dynamic service migration can support service continuity and reduce user-perceived delay. However, service migration in MEC networks faces significant challenges due to the uncertainty in future traffic demands, the distributed architecture of MEC networks, high operating costs and the dynamism of network resources. Digital Twins (DT), which achieve the mapping of physical entities to virtual digital models in cyberspace, provide new perspectives for intelligent and efficient service provisioning in MEC networks. In this paper, we propose a traffic prediction-assisted federated deep reinforcement learning scheme to efficiently migrate services and improve the cost efficiency of DT-enabled MEC networks. Specifically, to address the coupled spatio-temporal dependencies of mobile traffic and the imbalance in traffic data, a Multi-order Spatio-temporal information integration-based distributed Traffic Prediction (MSTP) scheme is proposed, which achieves high-accuracy mobile traffic prediction at a low cost. Then, we propose a Federated Cooperative cost-efficient Service Migration (FCSM) algorithm that adaptively adjusts service migration strategies in a distributed manner to respond to future traffic demands. Moreover, a theoretical model is developed to analyze the convergence of FCSM and derive the upper bound of the time-average squared gradient norm. Finally, extensive simulations demonstrate that the proposed schemes achieve excellent traffic prediction performance, enhance users' Quality of Service (QoS), and significantly reduce the system cost of MEC networks.

关键词： Computation offloading   Deep reinforcement learning   Mobile edge computing   Resource allocation  

摘要： Mobile edge computing (MEC) can enhance the computation capabilities of smart mobile devices for computation-intensive mobile applications via supporting computation offloading efficiently. However, the limitation of wireless resources and computational resources of edge servers often becomes the bottlenecks to realizing the developments of MEC. In order to address the computation offloading problem in the time-varying wireless networks, the offloading decisions and the allocation of radio and computation resources need to be jointly managed. Traditional optimization methods are challenging to deal with the combinatorial optimization problem in complex real-time dynamic network environments. Therefore, we propose a deep reinforcement learning (DRL)-based optimization approach, named DRJOA, which jointly optimizes offloading decisions, computation, and wireless resources allocation. The optimization algorithm based on DRL has the advantages of fast solving speed and strong generalization ability, which makes it possible to solve combinatorial optimization problems online. Simulation results show that our proposed DRJOA in this study dramatically outperforms the benchmark methods for offloading decisions and system utility.

关键词： Delay   Energy   Mobile edge computing   Task offloading  

摘要： Many tasks generated by mobile user devices are computation-intensive and latency-sensitive, such as autonomous driving and video analysis. However, due to limited energy and computing capacity, a user device may not be able to complete its task within a given time, leading to a poor user experience. Mobile edge computing (MEC) can address this challenge by offloading tasks to edge servers with stronger computing capacity and more resources for execution, which can save energy of user devices and reduce the task computation time. Different offloading strategies will impact the number of tasks completed, latency, energy overhead and so on. This paper investigates the problem of maximizing the number of completed tasks while minimizing the average completion time, energy overhead and cost in MEC under both time and energy constraints. To solve the problem, we develop the mayfly genetic algorithm (MGA), which jointly optimizes task offloading locations and ratios, central processor unit (CPU) frequencies of user devices and computing capacities allocated to user devices by edge servers. Simulation experiments indicate that MGA outperforms state-of-the-art algorithms in terms of the number of completed tasks.

关键词： Mobile edge computing   vehicle edge computing   digital twin network   deep reinforcement learning   task offloading  

摘要： Vehicle Edge Computing (VEC) is a promising paradigm that exposes Mobile Edge Computing (MEC) to road scenarios. In VEC, task offloading can enable vehicles to offload the computing tasks to nearby Roadside Units (RSUs) that deploy computing capabilities. However, the highly dynamic network topology, strict low-delay constraints, and massive data of tasks of VEC pose significant challenges for implementing efficient offloading. Digital Twin-based VEC is emerging as a promising solution that enables real-time monitoring of the state of the VEC network through mapping and interaction between the physical and virtual worlds, thus assisting in making sound offload decisions in the physical world. Thus, this paper proposes an intelligent partial offloading scheme, namely, Digital Twin-Assisted Intelligent Partial Offloading (IGNITE). First, to find the optimal offloading space in advance, we combine the improved clustering algorithm with the Digital Twin (DT) technique, in which unreasonable decisions can be avoided by reducing the size of the decision space. Second, to reduce the overall cost of the system, Deep Reinforcement Learning (DRL) algorithm is employed to train the offloading strategy, allowing for automatic optimization of computational delay and vehicle service price. To improve the efficiency of cooperation between digital and physical spaces, a feedback mechanism is established. It can adjust the parameters of the clustering algorithm based on the final offloading results in this clustering. To the best of our knowledge, this is the first study on DT-assisted vehicle offloading that proposes a feedback mechanism, forming a complete closed loop as prediction-offloading-feedback. Extensive experiments demonstrate that IGNITE has significant advantages in terms of total system computational cost, total computational delay, and offloading success rate compared with its counterparts.

关键词： Mobile edge computing   computing offloading   deep reinforcement learning   fairness guarantee  

摘要： Edge computing can reduce service latency and save backhaul bandwidth by completing services at network edges, providing support for diverse computation-intensive and delay-sensitive services. However, it is not practical to support all services at edge nodes due to the limited network resources. The decision that which services can be provided locally and which services should been offloaded to cloud significantly impacts the user experience. Cloud-edge computing offloading becomes an important issue in edge computing. In this paper, we take the fairness into the optimization objective of computing offloading problem, and consider both computing capacity and storage space as problem constraints. The problem is formulated as a long-term average optimization problem to maximize the alpha-fair utility function of saved time, and further translated as a Markov decision process. As the optimization problem with fairness guarantee and huge action space, we cannot solve it with traditional methods. Therefore, an innovative multi-update deep reinforcement learning algorithm is proposed which can optimize the objective with alpha -fair utility function and reduce dramatically the size of action space. We also prove the convergence of our algorithm theoretically. To our best knowledge, the long-term average optimization of computing offloading with fairness guarantee is rarely seen in literature. Extensive simulation experiments show that our algorithm can converge quickly and has better performance in terms of service delay and fairness.

关键词： Mobile edge computing   Cloudlet deployment   Graph cut   Approximate kernel optimization  

摘要： Mobile edge computing provides a low-latency, high-bandwidth cloud computing environment for resource-constrained mobile devices by allowing mobile devices to offload tasks, but user task migration causes greater transmission delays. Cloudlets, a new component of mobile edge computing, can perform tasks offloaded by mobile users nearby to reduce the access latency and meet users' requirements for system response time. However, deploying cloudlets in large-scale wireless metropolitan area networks (WMANs) to improve the service quality of mobile applications is currently still difficult. To resolve this issue, we design a cloudlet deployment model based on approximate graph cut, which abstracts the wireless communication network into an undirected weighted graph, divides the graph according to the access point location attributes, and minimizes the user access delay of subgraphs to obtain optimal network area segmentation and cloudlet deployment locations. We also develop an efficient kernel method to optimize the objective function of graph cuts. The simulation experimental results demonstrate that our model has low time and space complexity;thus, it is suitable for large-scale cloudlet deployment and has valuable application prospects.

关键词： Smart factory   Mobile edge computing   Cloud-edge-end collaboration   Emergency task offloading  

摘要： The realization of smart factories is becoming increasingly possible due to the rapid development of edge computing technology and 5G communication technology. Smart devices in smart factories can perform tasks such as environmental monitoring, production scheduling, and autonomous mobility. These tasks cannot be completed on time depending only on the computing power of smart devices. Therefore, some tasks need to be performed on edge servers and cloud servers. However, events such as natural disasters, network attacks, and hardware failures will cause server computing resource failures. These threats bring hidden dangers to the normal operation of smart factories. This paper proposes an emergency offloading strategy based on cloud-edge-end collaboration for smart factories. In this strategy, the total task execution delay and the key task execution delay constitute an objective function, which is solved by a Fast Chemical Reaction Optimization (Fast-CRO) algorithm. The algorithm follows the principle of prioritizing the offloading of key tasks in emergency situations, and it quickly makes emergency offloading decisions for the system. Simulation results show that this strategy outperforms the other five baseline algorithms and existing priority based CRO algorithms.

关键词： Ant Colony Optimization   Autonomous vehicles   Dynamic Source Dynamic Destination   Meta-heuristic   Mobile Edge Computing   Optimization   Shortest path estimation   Swarm intelligence  

摘要： In the present literature, various algorithms are available for computing the shortest path between two objects. The maximum number of these algorithms compute the shortest path either between two static objects or one static object and one dynamic object. This article presents an insight to integrated Mobile Edge Computing (MEC) based smart devices for tracking mobile rogue objects based on dynamic source and dynamic destination optimal cost estimation. This device considers any two mobile objects to estimate the shortest path between them. The proposed Ant Colony Optimization (ACO) based algorithm considers the property of dead-end removal and nth path exploration with efficient self-loop removal strategy. To review the performance of the proposed algorithm, experimentations are carried out and compared with several well-established shortest cost estimation techniques available in the literature - Floyd Warshall, Bellman Ford, Dijkstra, A* algorithms and the only dynamic shortest path algorithm. The detailed algorithmic comparisons clearly indicate the superiority of the proposed one over the existing dynamic and present state-of-the-art shortest path estimation methodologies.

关键词： Mobile edge computing  

摘要： Large language models (LLMs), which have shown remarkable capabilities, are revolutionizing AI development and potentially shaping our future. However, given their multimodality, the status quo cloud-based deployment faces some critical challenges: 1) long response time;2) high bandwidth costs;and 3) the violation of data privacy. 6G mobile edge computing (MEC) systems may resolve these pressing issues. In this article, we explore the potential of deploying LLMs at the 6G edge. We start by introducing killer applications powered by multimodal LLMs, including robotics and healthcare, to highlight the need for deploying LLMs in the vicinity of end users. Then, we identify the critical challenges for LLM deployment at the edge and envision the 6G MEC architecture for LLMs. Furthermore, we delve into two design aspects, i.e., edge training and edge inference for LLMs. In both aspects, considering the inherent resource limitations at the edge, we discuss various cutting-edge techniques, including split learning/inference, parameter-efficient fine-tuning, quantization, and parameter-sharing inference, to facilitate the efficient deployment of LLMs. This article serves as a position paper for thoroughly identifying the motivation, challenges, and pathway for enabling LLMs at the 6G edge. Copyright © 2023, The Authors. All rights reserved.

关键词： adaptive proximate computing   mobile edge computing   mobile resource augmentation architecture   neuro-fuzzy hybrid framework   task offloading   web service-based IoT framework  

摘要： Mobile devices play a vital role in people's day-to-day activities and are essential for communication, accessing Internet resources, entertainment, and so forth. In most cases, the performance of mobile applications is restricted due to the inadequate resources of mobile specifications such as controlled battery power, predetermined storage, and limited processing competence. Thus to improve the processing efficiency of resource-constrained mobile devices, this research proposes a model developed on offloading technique based on the recent literature observations which transfer the resource-consuming tasks from mobile devices to proximate computing entities. This generic architecture aiming for resource augmentation is formulated with five fundamental components including a mobile device, offloading engine, resource augmentation engine, scheduler, and synchronizer. The offloading engine identifies the resource-intensive tasks that need to be executed on external entities using soft computing techniques such as neural network training algorithms, fuzzy logic, and neuro-fuzzy logic processing. The augmentation engine chooses feasible edge computing entities such as Arduino, Raspberry PI controller, and fog devices. The scheduler prioritizes the resource-consuming tasks for offloading based on their importance. The synchronizer coordinates all the central components by updating the execution status of tasks at regular intervals of time. Further extension of this research work, based on the proposed architecture three resource augmentation frameworks are developed namely (a) an adaptive proximate computing framework (b) a web service-based IoT framework, and (c) a neuro-fuzzy hybrid framework. Moreover, these frameworks are realized through the development of resource-efficient intelligent edge computing IoT mobile apps namely (a) an inventory monitoring app (b) a lecture recording app, and (c) an intelligent transport app. The performance of these three resource augme