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： 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： 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  

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： 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： Digital twins   Heuristic algorithms   Approximation algorithms   Delays   Edge computing   Containers   Computers   Digital twin   serverless computing   mobile edge computing   user mobility   approximation algorithm   online algorithm   user service satisfaction   delay-sensitive service   utility maximization   resource allocation and optimization  

摘要： Driven by data and models, the digital twin technique presents a new concept of optimizing system design, process monitoring, decision-making and more, through performing comprehensive virtual-reality interaction and continuous mapping. By introducing serverless computing to Mobile Edge Computing (MEC) environments, the emerging serverless edge computing paradigm facilitates the communication-efficient digital twin services, and promises agile, fine-grained and cost-efficient provisioning of limited edge resources, where serverless functions are implemented by containers in cloudlets (edge servers). However, the nonnegligible cold start delay of containers deteriorates the responsiveness of digital twin services dramatically and the perceived user service experience. In this paper, we investigate delay-sensitive query service provisioning in digital twin-empowered serverless edge computing by considering user mobility. With digital twins of users deployed in the remote cloud, referred to as primary digital twins, we deploy their digital twin replicas based on serverless functions in cloudlets to mitigate the query service delay while enhancing user service satisfaction that is expressed as a utility function. We study two optimization problems with the aim of maximizing the accumulative utility gain: the digital twin replica placement problem per time slot, and the dynamic digital twin replica placement problem over a finite time horizon. We first formulate an Integer Linear Program (ILP) solution for the digital twin replica placement problem when the problem size is small;otherwise, we propose an approximation algorithm for the problem with a provable approximation ratio. We then design an online algorithm for the dynamic digital twin replica placement problem, and a performance-guaranteed online algorithm for a special case of the problem by assuming each user issues a query at each time slot. Finally, we evaluate the performance of the proposed algorithms for pl

关键词： mobile edge computing   unmanned aerial vehicle   deployment optimization   task offloading   evolutionary algorithm  

摘要： With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms.

关键词： blockchain   mobile edge computing   task offloading   multi-agent reinforcement learning  

摘要： In recent years, many mobile edge computing network solutions have enhanced data privacy and security and built a trusted network mechanism by introducing blockchain technology. However, this also complicates the task-offloading problem of blockchain-enabled mobile edge computing, and traditional evolutionary learning and single-agent reinforcement learning algorithms are difficult to solve effectively. In this paper, we propose a blockchain-enabled mobile edge computing task-offloading strategy based on multi-agent reinforcement learning. First, we innovatively propose a blockchain-enabled mobile edge computing task-offloading model by comprehensively considering optimization objectives such as task execution energy consumption, processing delay, user privacy metrics, and blockchain incentive rewards. Then, we propose a deep reinforcement learning algorithm based on multiple agents sharing a global memory pool using the actor-critic architecture, which enables each agent to acquire the experience of another agent during the training process to enhance the collaborative capability among agents and overall performance. In addition, we adopt attenuatable Gaussian noise into the action space selection process in the actor network to avoid falling into the local optimum. Finally, experiments show that this scheme's comprehensive cost calculation performance is enhanced by more than 10% compared with other multi-agent reinforcement learning algorithms. In addition, Gaussian random noise-based action space selection and a global memory pool improve the performance by 38.36% and 43.59%, respectively.

关键词： Task analysis   Servers   Wireless communication   Resource management   Fault tolerant systems   Fault tolerance   Wireless sensor networks   Mobile edge computing   wireless networks   resource allocation   fault-tolerance  

摘要： This letter studies fault tolerance and resource allocation for offloaded tasks in Mobile Edge Computing (MEC). The limitations of computational resources and wireless transmission power, coupled with server failures in MEC, present challenges for optimizing resource allocation and ensuring uninterrupted task execution. Existing literature primarily addresses resource allocation and fault tolerance separately, resulting in resource wastage and an increased count of faulty tasks. To address these issues, we first propose a novel method for jointly allocating computational resources and wireless transmission power for offload task. We then introduce the Dynamic Fault Tolerance Task Packing (DFTTP) algorithm, an online task packing algorithm. DFTTP, based on a new variant of the primary-standby replica mechanism, can tolerate multiple server failures simultaneously. Theoretical analysis and simulation results demonstrate that DFTTP, together with the proposed resource allocation method, can reduce the number of faulty tasks and enable higher resource utilization compared with existing algorithms.

关键词： online   offline   service caching   inference   mobile edge computing   deep reinforcement learning  

摘要： Edge servers frequently manage their own offline digital twin (DT) services, in addition to caching online digital twin services. However, current research often overlooks the impact of offline caching services on memory and computation resources, which can hinder the efficiency of online service task processing on edge servers. In this study, we concentrated on service caching and task offloading within a collaborative edge computing system by emphasizing the integrated quality of service (QoS) for both online and offline edge services. We considered the resource usage of both online and offline services, along with incoming online requests. To maximize the overall QoS utility, we established an optimization objective that rewards the throughput of online services while penalizing offline services that miss their soft deadlines. We formulated this as a utility maximization problem, which was proven to be NP-hard. To tackle this complexity, we reframed the optimization problem as a Markov decision process (MDP) and introduced a joint optimization algorithm for service caching and task offloading by leveraging the deep Q-network (DQN). Comprehensive experiments revealed that our algorithm enhanced the utility by at least 14.01% compared with the baseline algorithms.