关键词： Mobile edge computing  

摘要： With rapid increase of computing demands from users in mobile edge computing (MEC), the traditional mobile edge servers are no longer capable of providing flexible and timely services. Unmanned aerial vehicle (UAV)-assisted mobile edge computing is envisioned as a potential solution to provide high quality services for large-scale user devices. In UAV-assisted mobile edge computing, there are many complex applications such as obstacle detection, image recognition and path planning. These applications are characterized by huge computational resources and extremely low-latency requirements. Most of complex applications in the real-world can be expressed as a set of interdependent tasks, also known as application workflows. Due to the limited battery capacity and computing capabilities of UAVs, and the complex dependencies between workflow tasks, it is challenging to jointly optimize the UAV path planning and the workflow offloading under deadline constraints. To address this challenge, this paper proposes a distributed deep reinforcement learning algorithm based on the graph convolutional network (GCN) and asynchronous-advantage-actor-critic (A3C) learning that minimizes the energy consumption of UAVs while meeting the deadlines of workflows. We conduct simulation experiments to evaluate the performance of the proposed algorithm under complex workflow applications. The results show that our algorithm improves the performance by about 33.6% compared with the other optimization approaches. © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： On-device large language models (LLMs), referring to running LLMs on edge devices, have raised considerable interest owing to their superior privacy, reduced latency, and bandwidth saving. Nonetheless, the capabilities of on-device LLMs are intrinsically constrained by the limited capacity of edge devices compared to the much more powerful cloud centers. To bridge the gap between cloud-based and on-device AI, mobile edge intelligence (MEI) presents a viable solution to this problem by provisioning AI capabilities within the edge of mobile networks with improved privacy and latency relative to cloud computing. MEI sits between on-device AI and cloud-based AI, featuring wireless communications and more powerful computing resources than end devices. This article provides a contemporary survey on harnessing MEI for LLMs. We first cover the preliminaries of LLMs, starting with LLMs and MEI, followed by resource-efficient LLM techniques. We then illustrate several killer applications to demonstrate the need for deploying LLMs at the network edge and present an architectural overview of MEI for LLMs (MEI4LLM). Subsequently, we delve into various aspects of MEI4LLM, extensively covering edge LLM caching and delivery, edge LLM training, and edge LLM inference. Finally, we identify future research opportunities. We aim to inspire researchers in the field to leverage mobile edge computing to facilitate LLM deployment in close proximity to users, thereby unleashing the potential of LLMs across various privacy- and delay-sensitive applications. Copyright © 2024, The Authors. All rights reserved.

关键词： network slicing   zero-touch network   resource slicing   mobile edge computing   service function chaining   network function virtualization   blockchain   SDN  

摘要： Zero-touch networks (ZTNs) can provide autonomous network solutions by integrating software-based solutions for various emerging 5G and 6G applications. The current literature does not provide any suitable end-to-end network management and resource-slicing solutions for service function chaining (SFC) and user intent-based (time and cost preference) 6G/non-6G application execution over ZTNs enabled by mobile edge computing, network function virtualization, and software-defined networking. To tackle these challenges, this work initiates an end-to-end network management and user intent-aware intelligent network resource-slicing scheme for SFC-based 6G/non-6G application execution over ZTNs, taking into account various virtual and physical resources, task workloads, service requirements, and task numbers. The results depicted that at least 25.27% average task implementation delay gain, 6.15% energy gain, and 11.52% service monetary gain are realized in the proposed scheme over the compared schemes.

关键词： Mobile edge computing  

摘要： A novel paradigm of mobile edge generation (MEG)enabled digital twin (DT) is proposed, which enables distributed on-device generation at mobile edge networks for real-time DT applications. First, an MEG-DT architecture is put forward to decentralize generative artificial intelligence (GAI) models onto edge servers (ESs) and user equipments (UEs), which has the advantages of low latency, privacy preservation, and individual-level customization. Then, various single-user and multi-user generation mechanisms are conceived for MEG-DT, which strike trade-offs between generation latency, hardware costs, and device coordination. Furthermore, to perform efficient distributed generation, two operating protocols are explored for transmitting interpretable and latent features between ESs and UEs, namely sketch-based generation and seed-based generation, respectively. Based on the proposed protocols, the convergence between MEG and DT are highlighted. Considering the seed-based image generation scenario, numerical case studies are provided to reveal the superiority of MEG-DT over centralized generation. Finally, promising applications and research opportunities are identified. Code is available at https://***/xiaoxiaxusummer/MEG_ DT. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing   blockchain   resource allocation  

摘要： Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide *** blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the *** is,each edge server runs both users’offloaded tasks and blockchain tasks *** that there is a trade-off between the resource allocation for MEC and blockchain ***,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based *** of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC *** this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing *** the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the *** results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.

关键词： Mobile edge computing  

摘要： The scalability of large language models (LLMs) in handling high-complexity models and large-scale datasets has led to tremendous successes in pivotal domains. While there is an urgent need to acquire more training data for LLMs, a concerning reality is the depletion of high-quality public datasets within a few years. In view of this, the federated learning (FL) LLM fine-tuning paradigm recently has been proposed to facilitate collaborative LLM fine-tuning on distributed private data, where multiple data owners collaboratively fine-tune a shared LLM without sharing raw data. However, the staggering model size of LLMs imposes heavy computing and communication burdens on clients, posing significant barriers to the democratization of the FL LLM fine-tuning paradigm. To address this issue, split learning (SL) has emerged as a promising solution by offloading the primary training workload to a server via model partitioning while exchanging activation/activation’s gradients with smaller data sizes rather than the entire LLM. Unfortunately, research on the SL LLM fine-tuning paradigm is still in its nascent stage. To fill this gap, in this paper, we propose the first SL LLM fine-tuning framework, named SplitLoRA. SplitLoRA is built on the split federated learning (SFL) framework, amalgamating the advantages of parallel training from FL and model splitting from SL and thus greatly enhancing the training efficiency. It is worth noting that SplitLoRA is the inaugural open-source benchmark for SL LLM fine-tuning, providing a foundation for research efforts dedicated to advancing SL LLM fine-tuning. Extensive simulations validate that SplitLoRA achieves target accuracy in significantly less time than state-of-the-art LLM fine-tuning frameworks, demonstrating the superior training performance of SplitLoRA. The project page is available at https://***/splitlora/. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) and terahertz (THz)enabled unmanned aerial vehicle (UAV) communication systems are gaining significant attention for improving user service delays in future mobile networks. This article introduces a novel multi-UAV-aided MEC system operating at THz frequencies to minimize expected user service delays, including communication and computation latency. We address this challenge by jointly optimizing UAV relay selection, power control, positioning, and user-resource association for task offloading and resource allocation. To tackle the problem’s complexities, we decompose it into four subproblems, each solved optimally with our proposed algorithm. An iterative penalty dual decomposition (PDD) algorithm approximates the original problem’s solution. Numerical results demonstrate that our PDD-based approach outperforms baseline algorithms in terms of expected user service delay. © 2024, CC BY.

关键词： Game theory   mobile edge computing   neural networks   offloading   scalable optimization  

摘要： With the unprecedented scalability issues rising in vehicular edge computing (VEC), we argue in this paper that the scalability, along with the remarkable growth of demands for offloading, should be integrated into the modelling for effective offloading decision-making strategies requested by a large number of vehicles. A two-stage game-theory model can depict offloading decision-making strategies by considering both the revenue of network operators and the cost of VEC users. However, heuristic processes of solving such models show significant limitations in terms of high computational complexity and energy consumption due to the changing VEC environment. Therefore, our objective in this study is to solve the game-theory model efficiently and achieve scalable offloading for the changing VEC environment. We first develop a two-stage game-theory model for the offloading decision-making strategy for VEC, by which an operator's revenue, energy consumption and latency are considered. Then a neural network (NN) model is designed to learn the predicted behaviours of the established game-theory model for offloading decisions in a more efficient manner. After that, a feature-based transfer learning algorithm is proposed for scalable offloading optimization under unseen VEC environments. Experimental results show that the proposed NN can significantly improve the efficiency of solving the game theory model, and the developed transfer learning approach can effectively achieve the scalability of offloading decisions in a changing VEC environment. The results demonstrate that the accuracy of the proposed transfer learning approach is 37% higher than that of several state-of-the-art algorithms, and the runtime halves.

关键词： Mobile edge computing   UAV   QoS guarantee   SLA compliance   IoT application  

摘要： Unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) is an emerging network architecture that has been considered as a promising transformative service paradigm. It enhances the coverage of intelligent mobile communication network and promotes the rapid development of a wide range of internet of things (IoT) applications. However, the ever-increasing data transmission demands given rise by these IoT applications based on MEC have posed significant challenges to the service of quality (QoS) guarantee of UAVs-assisted MEC (UAVs-MEC) network. To address the issue, we propose a novel UAVs-MEC QoS guarantee scheme that can enhances the reliability of IoT applications in UAVs-MEC network. Specifically, a dynamic QoS-aware based service level agreement (SLA) compliance verification model for MEC service is proposed in the scheme. It can monitor and detect the consistency of dynamic QoS of IoT applications through UAVs to ensure the continuous SLA compliance of service provided by MEC. Additionally, an objective weight assignment method and an adaptive update method are presented to improve the accuracy and scalability of the scheme. Finally, the simulation experiments conducted using a real-world dataset show that the proposed scheme can efficiently and effectively verify the SLA compliance of MEC service for guaranteeing QoS of IoT applications in UAVs-MEC context while outperforms other existing SLA violation detection methods.

关键词： Computation offloading   deep learning   mobile edge computing   reinforcement learning   resource allocation  

摘要： We consider a multi-user multi-server mobile edge computing (MEC) system, in which users arrive on a network randomly over time and generate computation tasks, which will be computed either locally on their own computing devices or be offloaded to one of the MEC servers. Under such a dynamic network environment, we propose a novel task offloading policy based on hybrid online-offline learning, which can efficiently reduce the overall computation delay and energy consumption only with information available at nearest MEC servers from each user. We provide a practical signaling and learning framework that can train deep neural networks for both online and offline learning and can adjust its offloading policy based on the queuing status of each MEC server and network dynamics. Numerical results demonstrate that the proposed scheme significantly reduces the average computation delay for a broad class of network environments compared to the conventional offloading methods. It is further shown that the proposed hybrid online-offline learning framework can be extended to a general cost function reflecting both delay- and energy-dependent metrics.