移动边缘计算中的联合优化迁移决策和资源分配

移动边缘迁移计算中,边缘服务器之间的协作能为用户提供更高效的服务。本文对正交频分复用上行无线通信系统,基于移动边缘计算技术的任务迁移的子载波选择、用户发射功率和迁移量的联合优化问题进行了研究。在公平性原则下,本文考虑最小化迁移计算的最大时延问题,并提出了一种非凸问题的拉格朗日对偶法解决方案。首先将min-max问题转化为最小化问题,再用泰勒级数将其近似为一个凸问题,最后用拉格朗日对偶法求解。本文还给出特殊情况下的简便算法,适用于低信噪比的通信环境下。仿真结果证实了本算法的收敛性和实用性。      

基于多接入边缘计算的任务卸载和资源分配策略研究

随着移动通信技术和工业互联网的飞速发展,移动设备端日渐庞大的数量和复杂的应用对大量计算密集和低时延提出了要求,也因此引出了基于多接入边缘计算的任务卸载概念。这种任务卸载方式能够有效地利用边缘云服务器资源,将复杂的计算任务卸载至邻近的低消耗边缘服务器,提高任务计算效率和更高的服务质量。提出了一种基于拓扑结构的任务卸载策略和边缘资源分配策略,旨在解决边缘计算场景中,任务卸载效率低、资源利用率不足等问题。     

基于移动边缘计算的资源分配策略

为进一步探究移动互联网的资源分配问题,文中基于无线互联网通信工况恶劣的特殊情形构建了仿真模型,并结合实际情况,以支持移动边缘计算的服务器为基础,引入基于深度域不变性残差计算的长短期记忆网络（DR-LSTM）,从互联网设备任务卸载的角度着手设计了资源分配策略和主要算法流程。通过仿真实验结果可知,基于移动边缘计算的资源分配策略在性能上存在一定的优势,具有潜在的应用价值。     

移动边缘网络中基于用户移动的服务功能链迁移策略

移动边缘计算(Mobile Edge Computing,MEC)通过在网络边缘部署服务器,提供计算和存储资源,可为用户提供超低时延和高带宽业务。网络功能虚拟化(Network Function Virtualization,NFV)与MEC技术相结合,可在MEC服务器上提供服务功能链(Service Function Chain,SFC),提升用户的业务体验。为了保证移动用户的服务质量,需要在用户跨基站移动时将SFC迁移到合适的边缘服务器上。主要以最小化用户服务的端到端时延和运行成本为目标,提出了MEC网络中具有资源容量约束的SFC迁移策略,以实现移动用户业务的无缝迁移。仿真结果表明,与现有方案相比,该策略具有更好的有效性和高效性。     

基于边缘计算的无人机移动辅助卸载策略研究

文中研究了基于边缘计算的无人机移动辅助卸载技术,对促进无人机发展、提升其经济价值具有重要的意义。值得注意的是,当前我国需要实现无人机辅助边缘计算系统的能耗最小化、用户时延最小化、系统吞吐量最大化、无人机能耗最小化等,以促进无人机技术高质量发展。基于此,文中提出了利用合理的计算卸载机制,使无人机在卸载过程中的位置适宜化,实现了用户任务的分配高效、无人机运动建模、无人机轨迹优化,并设计了用户终端任务分配方案、优化算法,从而设计出一种快速收敛的算法,推动无人机技术的快速发展。     

基于5G移动网络中边缘计算与计算迁移模型的研究

随着通信技术和移动互联网的高速发展,移动通信已进入了5G时代。但数据的蓬勃发展也让网络面临大带宽、低时延、广连接、高可靠度、高安全性等挑战。面对这些挑战,移动边缘计算(mobile edge computing,MEC)孕育而生了,MEC架构提供了分流、计算、业务感知、计算迁移的能力,并将相应能力下沉至网络边缘。文章首先介绍了边缘计算在5G网络中的基本架构和最新的研究成果。其次,基于MEC平台下的任务迁移是未来必然的发展趋势,分析了MEC环境下任务迁移的过程、算法、优势等。最后提出了目前边缘计算发展所面临的问题及挑战。     

低轨卫星协作边缘计算任务迁移和资源分配算法

研究了基于星间链路的低轨卫星协作边缘计算任务迁移和资源分配问题,为偏远地区用户提供边缘计算服务.采用部分任务迁移机制,以地面用户加权总能耗最小化为目标建立优化问题,提出了一种低轨卫星协作边缘计算的任务迁移和资源分配算法,基于优化问题的非凸性,将其分解为任务迁移子问题和资源分配子问题,分别采用标准凸优化方法和拉格朗日对偶...     

减少核心网拥塞的边缘计算资源分配和卸载决策

随着互联网的发展,智能终端在实践中得到了应用,大量时间敏感的计算机应用在人们的生活中也被广泛使用,如p/虚拟现实、智能家居和汽车互联网等。网络流量的增加将逐渐增大核心网络的压力,管理延迟网络变得越来越困难。目前,云协作计算解决方案是拟议的模型边界,文中提出了一种新的算法来管理边缘云之间基本网络流量的分布和解密,以共享时间和分配来改善边缘处理流程的算法资源,遗传算法用于寻找最佳分解分辨率。实验结果表明,与基线相比,拟议的算法可以提高资源效率并减少云流量边缘,从而减少核心网络拥堵现象。     

超密集网络中基于移动边缘计算的任务卸载和资源优化

移动边缘计算(MEC)通过在无线网络边缘为用户提供计算能力,来提高用户的体验质量。然而,MEC的计算卸载仍面临着许多问题。该文针对超密集组网(UDN)的MEC场景下的计算卸载,考虑系统总能耗,提出卸载决策和资源分配的联合优化问题。首先采用坐标下降法制定了卸载决定的优化方案。同时,在满足用户时延约束下采用基于改进的匈牙利算法和贪婪算法来进行子信道分配。然后,将能耗最小化问题转化为功率最小化问题,并将其转化为一个凸优化问题得到用户最优的发送功率。仿真结果表明,所提出的卸载方案可以在满足用户不同时延的要求下最小化系统能耗,有效地提升了系统性能。

     

一种多边缘协作的任务卸载策略

当前,多数任务卸载策略只考虑单边缘或者“物-边-云”的卸载方式,而没有对异地边缘服务器的资源进行充分利用。针对上述问题,提出了一种多边缘协作的网络架构,该架构中的任务可以选择在本地执行、本地服务器执行、异地服务器执行或者在云端执行。分别对4种执行方法的时延和能耗的加权求和建立数学模型。在传统的任务属性中引入新变量——终端所能承受的最大合作成本,以便吸引更多的异地边缘服务器积极协作完成终端任务的计算。针对传统的粒子群算法容易早熟和陷入局部最优的缺点,采用免疫粒子群优化算法(Immune Particle Optimization,IPSO)来对优化目标进行求解。仿真结果表明,与本地卸载策略、免疫算法(Immune Algorithm,IA)和粒子群(Particle Swarm Optimization,PSO)算法相比,所提任务卸载策略的总代价分别减少了66.7%,54%和45.5%,可以提高任务的执行效率,有效地减少系统的总代价。     

Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing

The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the edge of the cellular networks, has been considered as an efficient approach to relieve the heavy computational burdens and realize an efficient computation offloading. Driven by the consequent requirement for proper resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC. Specifically, we consider a MEC system in which every mobile terminal has multiple tasks offloaded to the edge server and design a joint task offloading decision and bandwidth allocation optimization to minimize the overall offloading cost in terms of energy cost, computation cost, and delay cost. Although the proposed optimization problem is a mixed integer nonlinear programming in nature, we exploit an emerging DQN technique to solve it. Extensive numerical results show that our proposed DQN-based approach can achieve the near-optimal performance.     

Research on intelligent computing offloading model based on reputation value in mobile edge computing

Aiming at the problem of high-latency,high-energy-consumption,and low-reliability mobile caused by computing-intensive and delay-sensitive emerging mobile applications in the explosive growth of IoT smart mobile terminals in the mobile edge computing environment,an offload decision-making model where delay and energy consumption were comprehensively included,and a computing resource game allocation model based on reputation that took into account was proposed,then improved particle swarm algorithm and the method of Lagrange multipliers were used respectively to solve models.Simulation results show that the proposed method can meet the service requirements of emerging intelligent applications for low latency,low energy consumption and high reliability,and effectively implement the overall optimized allocation of computing offload resources.     

Q-learning-based task offloading strategy for satellite edge computing

In this paper, we study the task offloading optimization problem in satellite edge computing environments to reduce the whole communication latency and energy consumption so as to enhance the offloading success rate. A three-tier machine learning framework consisting of collaborative edge devices, edge data centers, and cloud data centers has been proposed to ensure an efficient task execution. To accomplish this goal, we also propose a Q-learning-based reinforcement learning offloading strategy in which both the time-sensitive constraints and data requirements of the computation-intensive tasks are taken into account. It enables various types of tasks to select the most suitable satellite nodes for the computing deployment. Simulation results show that our algorithm outperforms other baseline algorithms in terms of latency, energy consumption, and successful execution efficiency.     

移动边缘计算卸载技术综述

移动边缘计算(Mobile Edge Computing,MEC)将云服务器的计算资源扩展到更靠近用户一侧的网络边缘,使得用户可以将任务卸载到边缘服务器,从而克服原先云计算中将任务卸载到云服务器所带来的高时延问题。首先介绍了移动边缘计算的基本概念、基本框架和应用场景,然后围绕卸载决策、联合资源分配的卸载决策分别从单MEC服务器和多MEC服务器两种场景总结了任务卸载技术的研究现状,最后结合当前MEC卸载技术中存在的不足展望了未来MEC卸载技术的研究。     

车联网场景下移动边缘计算协作式资源分配策略

为了有效利用边缘云的计算资源,尽可能降低任务卸载时的平均等待时延,提出了一种满足边缘计算服务器容限阈值和任务卸载成功率约束条件下的多个边缘计算服务器相互协作的资源分配方案,通过单位时间总代价指标优化边缘计算服务器个数.将此方案建模为一个整数优化问题,之后设计了一种最小代价算法求解此优化问题,得到约束条件下的单位时间总代...     

Survey on computation offloading in mobile edge computing

Computation offloading in mobile edge computing would transfer the resource intensive computational tasks to the edge network.It can not only solve the shortage of mobile user equipment in resource storage,computation performance and energy efficiency,but also deal with the problem of resource occupation,high latency and network load compared to cloud computing.Firstly the architecture of MEC was introduce and a comparative analysis was made according to various deployment schemes.Then the key technologies of computation offloading was studied from three aspects of decision on computation offloading,allocation of computing resource within MEC and system implement of MEC.Based on the analysis of MEC deployment scheme in 5G,two optimization schemes on computation offloading was proposed in 5G MEC.Finally,the current challenges in the mobility management was summarized,interference management and security of computation offloading in MEC.     

Energy-efficient task offloading strategy in mobile edge computing for resource-intensive mobile applications

Mobile Edge Computing (MEC) has been considered a promising solution that can address capacity and performance challenges in legacy systems such as Mobile Cloud Computing (MCC). In particular, such challenges include intolerable delay, congestion in the core network, insufficient Quality of Experience (QoE), high cost of resource utility, such as energy and bandwidth. The aforementioned challenges originate from limited resources in mobile devices, the multi-hop connection between end-users and the cloud, high pressure from computation-intensive and delay-critical applications. Considering the limited resource setting at the MEC, improving the efficiency of task offloading in terms of both energy and delay in MEC applications is an important and urgent problem to be solved. In this paper, the key objective is to propose a task offloading scheme that minimizes the overall energy consumption along with satisfying capacity and delay requirements. Thus, we propose a MEC-assisted energy-efficient task offloading scheme that leverages the cooperative MEC framework. To achieve energy efficiency, we propose a novel hybrid approach established based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) to solve the optimization problem. The proposed approach considers efficient resource allocation such as sub-carriers, power, and bandwidth for offloading to guarantee minimum energy consumption. The simulation results demonstrate that the proposed strategy is computational-efficient compared to benchmark methods. Moreover, it improves energy utilization, energy gain, response delay, and offloading utility.     

边缘计算多约束可信协同任务迁移策略

为保障边缘计算的服务质量,提出一种在多约束条件下边缘计算可信协同任务迁移策略。该策略基于任务需求,由边缘计算协同服务盟主节点组织调度协同服务盟员,基于用户任务迁移的K维权重指标,确定协同盟员调度优先级,以盟员负载均衡性为适应函数,通过贪心算法执行盟员任务分配与调度,基于路由捎带选择备用节点,通过迁移优先级评估,实现协同服务异常时的调度和迁移,由此提高边缘计算任务迁移的服务质量,保障任务迁移的可靠性。仿真实验表明,该机制能有效完成协同任务分发与迁移调度,提高边缘计算协同效率,保障网络服务质量。     

An intelligent task offloading algorithm (iTOA) for UAV edge computing network

Unmanned Aerial Vehicle (UAV) has emerged as a promising technology for the support of human activities, such as target tracking, disaster rescue, and surveillance. However, these tasks require a large computation load of image or video processing, which imposes enormous pressure on the UAV computation platform. To solve this issue, in this work, we propose an intelligent Task Offloading Algorithm (iTOA) for UAV edge computing network. Compared with existing methods, iTOA is able to perceive the network’s environment intelligently to decide the offloading action based on deep Monte Calor Tree Search (MCTS), the core algorithm of Alpha Go. MCTS will simulate the offloading decision trajectories to acquire the best decision by maximizing the reward, such as lowest latency or power consumption. To accelerate the search convergence of MCTS, we also proposed a splitting Deep Neural Network (sDNN) to supply the prior probability for MCTS. The sDNN is trained by a self-supervised learning manager. Here, the training data set is obtained from iTOA itself as its own teacher. Compared with game theory and greedy search-based methods, the proposed iTOA improves service latency performance by 33% and 60%, respectively.     

Resource scheduling in mobile edge computing using improved ant colony algorithm for space information network

With the development of space information network (SIN), new network applications are emerging. Satellites are not only used for storage and transmission but also gradually used for calculation and analysis, so the demand for resources is increasing. But satellite resources are still limited. Mobile edge computing (MEC) is considered an effective technique to reduce the pressure on satellite resources. To solve the problem of task execution delay caused by limited satellite resources, we designed Space Mobile Edge Computing Network (SMECN) architecture. According to this architecture, we propose a resource scheduling method. First, we decompose the user tasks in SMECN, so that the tasks can be assigned to different servers. An improved ant colony resource scheduling algorithm for SMECN is proposed. The heuristic factors and pheromones of the ant colony algorithm are improved through time and resource constraints, and the roulette algorithm is applied to route selection to avoid falling into the local optimum. We propose a dynamic scheduling algorithm to improve the contract network protocol to cope with the dynamic changes of the SIN and dynamically adjust the task execution to improve the service capability of the SIN. The simulation results show that when the number of tasks reaches 200, the algorithm proposed in this paper takes 17.52% less execution time than the Min-Min algorithm, uses 9.58% less resources than the PSO algorithm, and achieves a resource allocation rate of 91.65%. Finally, introducing dynamic scheduling algorithms can effectively reduce task execution time and improve task availability.