一种面向移动边缘计算的多用户细粒度任务卸载调度方法

在移动边缘计算中(Mobile Edge Computing,MEC),任务卸载可以有效地解决移动设备资源受限的问题,但是将全部任务都卸载到边缘服务器并非最优.本文提出一种面向移动边缘计算的多用户细粒度任务卸载调度新方法,把计算任务看作一个有向无环图(Directed Acyclic Graph,DAG),对节点的执行位置和调度顺序进行了优化决策.考虑系统的延迟把计算卸载看作一个约束多目标优化问题(Constrained Multi-object Optimization Problem,CMOP),提出了一个改进的NSGA-Ⅱ算法来解决CMOP.所提出的算法能够实现本地和边缘的并行处理从而减少延迟.实验结果表明,算法能够在实际应用程序中做出最优决策.     

车辆网络多平台卸载智能资源分配算法

为了降低计算任务的时延和系统的成本,移动边缘计算(MEC)被用于车辆网络,以进一步改善车辆服务。该文在考虑计算资源的情况下对车辆网络时延问题进行研究,提出一种多平台卸载智能资源分配算法,对计算资源进行分配,以提高下一代车辆网络的性能。该算法首先使用K临近(KNN)算法对计算任务的卸载平台(云计算、移动边缘计算、本地计算)进行选择,然后在考虑非本地计算资源分配和系统复杂性的情况下,使用强化学习方法,以有效解决使用移动边缘计算的车辆网络中的资源分配问题。仿真结果表明,与任务全部卸载到本地或MEC服务器等基准算法相比,提出的多平台卸载智能资源分配算法实现了时延成本的显著降低,平均可节省系统总成本达80%。     

基于UCB算法的信道选择方案

物联网在设备中的应用导致了更多的网络交通堵塞,本地服务器无法满足大数据传输的需要。很难做到在大数据下的中央处理模式云计算。边缘计算的出现,将数据卸载到多个边缘服务器进行处理。卸载到服务器的数据需要通过信道,以前的信道选择方法是基站的统一分配。如果终端设备可以通过自己的学习选择信道,可以提高效率、减轻基站的负担。文章对此开展分析。     

基于车辆成组的V2V协同计算卸载策略研究

在车联网中引入V2V计算卸载技术可以缓解当前车载计算卸载热点地区路边单元(RSU)计算资源不足的问题.然而,在计算卸载过程中,服务车辆可能因故障离组或自主选择离开车组.如何返回任务结果并高效地分配计算任务是需要进一步研究的关键问题.提出了一个车组内计算任务分配算法,考虑了可能导致车辆离开车组的因素影响,以及组中每辆车能...     

基于匹配理论的电力物联网边缘服务器选择机制

移动边缘计算利用部署在用户附近基站或具有空闲资源的路侧单元、车辆和MEC服务器作为网络的边缘,为设备提供所需的服务以及云端计算能力,以减少网络操作和服务交付的时延。文章将移动设备和MEC服务器的任务分配问题描述为一对一的匹配博弈,解决了移动边缘计算中的任务卸载问题。文章提出的算法具有良好的扩展性,并且能够降低总体能耗,使任务卸载时延最小化。     

基于深度强化学习的多用户计算卸载优化模型和算法

在移动边缘计算(MEC)密集部署场景中,边缘服务器负载的不确定性容易造成边缘服务器过载,从而导致计算卸载过程中时延和能耗显著增加。针对该问题,该文提出一种多用户计算卸载优化模型和基于深度确定性策略梯度(DDPG)的计算卸载算法。首先,考虑时延和能耗的均衡优化建立效用函数,以最大化系统效用作为优化目标,将计算卸载问题转化为混合整数非线性规划问题。然后,针对该问题状态空间大、动作空间中离散和连续型变量共存,对DDPG深度强化学习算法进行离散化改进,基于此提出一种多用户计算卸载优化方法。最后,使用该方法求解非线性规划问题。仿真实验结果表明,与已有算法相比,所提方法能有效降低边缘服务器过载概率,并具有很好的稳定性。     

面向海洋观监测传感网的计算卸载方法研究

针对海洋网络节点间计算能力与通信资源的差异性,提出了一种基于海洋网络连通概率的边缘计算节点选取方法.根据海洋近岸与远岸的网络节点密度不同,分别建立2种卸载模型.在近岸场景下,提出多节点协同的卸载方法,利用基于海洋多节点协同卸载遗传算法求解;在远岸场景下,提出可容错的卸载方法,利用基于分组交叉学习粒子群算法求解.仿真结果...     

NOMA-MEC系统中基于改进遗传算法的协作式计算卸载与资源管理

为平衡网络负载与充分利用网络资源,针对超密集异构的多用户和多任务边缘计算网络,在用户时延约束下,该文构造了协作式计算任务卸载与无线资源管理的联合优化问题以最小化系统能耗。问题建模时,为应对基站超密集部署导致的严重干扰问题,该文采用了频带划分机制,并引入了非正交多址技术(NOMA)以提升上行频谱利用率。鉴于该目标优化问题具备非线性混合整数的形式,根据多样性引导变异的自适应遗传算法(AGADGM),设计出了协作式计算卸载与资源分配算法。仿真结果表明,在严格满足时延约束条件下,该算法能获取较其他算法更低的系统能耗。     

一种超密集网络基站分簇管理算法

为了降低超密集网络中基站管理算法的计算复杂度并提升基站的能源使用效率,根据用户密度、网络负载量等信息,提出了一种基于分簇的动态管理基站算法.该算法首先根据用户测量报告计算出理论最小需求基站数,然后对基站进行合理的网络分簇,最终通过粒子群优化算法确定基站休眠组合.仿真结果表明,与未进行分簇的基站管理算法相比,该算法可以降低约60%的计算复杂度,并能有效降低基站能源消耗.     

基于安全保障的边缘计算卸载方案

提出了基于安全管理的边缘计算卸载方案,并基于量子进化算法(QEA)设计了卸载决策方案。该方案保证了用户在边缘计算网络中进行计算卸载的安全性。仿真结果表明,与常规计算卸载方案对比,本方案能在保证计算卸载安全的情况下有效降低整个系统的开销。     

Multiuser computation offloading for edge-cloud collaboration using submodular optimization

A computation offloading scheme based on edge-cloud computing was proposed to improve the system utility of multiuser computation offloading.This scheme improved the system utility while considering the optimization of edge-cloud resources.In order to tackle the problems of computation offloading mode selection and edge-cloud resource allocation,a greedy algorithm based on submodular theory was developed by fully exploiting the computing and communication resources of cloud and edge.The simulation results demonstrate that the proposed scheme effectively reduces the delay and energy consumption of computing tasks.Additionally,when computing tasks are offloaded to edge and cloud from devices,the proposed scheme still maintains stable system utilities under ultra-limited resources.     

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。     

V2X offloading and resource allocation under SDN and MEC architecture

To address the serious problem of delay and energy consumption increase and service quality degradation caused by complex network status and huge amounts of computing data in the scenario of vehicle-to-everything (V2X),a vehicular network architecture combining mobile edge computing (MEC) and software defined network (SDN) was constructed.MEC sinks cloud serviced to the edge of the wireless network to compensate for the delay fluctuation caused by remote cloud computing.The SDN controller could sense network information from a global perspective,flexibly schedule resources,and control offload traffic.To further reduce the system overhead,a joint task offloading and resource allocation scheme was proposed.By modeling the MEC-based V2X offloading and resource allocation,the optimal offloading decision,communication and computing resource allocation scheme were derived.Considering the NP-hard attribute of the problem,Agglomerative Clustering was used to select the initial offloading node,and Q-learning was used for resource allocation.The offloading decision was modeled as an exact potential game,and the existence of Nash equilibrium was proved by the potential function structure.The simulation results show that,as compared to other mechanisms,the proposed mechanism can effectively reduce the system overhead.     

基于NOMA的超密集MEC网络任务卸载和资源分配方案

为了提高移动边缘计算(mobile edge computing,MEC)网络的频谱效率,满足大量用户的服务需求,建立了基于非正交多址接入(non-orthogonal multiple access,NOMA)的超密集MEC系统模型。为了解决多个用户同时卸载带来的严重通信干扰等问题,以高效利用边缘服务器资源,提出了一种联合任务卸载和资源分配的优化方案,在满足用户服务质量的前提下最小化系统总能耗。该方案联合考虑了卸载决策、功率控制、计算资源和子信道资源分配。仿真结果表明,与其他卸载方案相比,所提方案可以在满足用户服务质量的前提下有效降低系统能耗。     

DDPG-based intelligent rechargeable fog computation offloading for IoT

In view of the existing computation offloading research on fog computing network scenarios, most scenarios focus on reducing energy consumption and delay and lack the joint consideration of smart device rechargeability. This paper proposes a deep deterministic policy gradient-based intelligent rechargeable fog computation offloading mechanism that is combined with simultaneous wireless information and power transfer technology. Specifically, an optimization problem that minimizes the total energy consumption for completing all tasks in a multiuser scenario is formulated, and the joint optimization of the task offloading ratio, uplink channel bandwidth, power split ratio and computing resource allocation is fully considered. Based on the above nonconvex optimization problem with a continuous action space, a communication, computation and energy harvesting co-aware intelligent computation offloading algorithm is developed. It can achieve the optimal energy consumption and delay, and similar to a double deep Q-network, an inverting gradient updating-based dual actor-critic neural network design can improve the convergence and stability of the training process. Finally, the simulation results validate that the proposed mechanism can converge quickly and can effectively reduce the energy consumption with the lowest task delay.

     

基于深度强化学习的云边协同计算迁移研究

基于单一边缘节点计算、存储资源的有限性及大数据场景对高效计算服务的需求,本文提出了一种基于深度强化学习的云边协同计算迁移机制.具体地,基于计算资源、带宽和迁移决策的综合性考量,构建了一个最小化所有用户任务执行延迟与能耗权重和的优化问题.基于该优化问题提出了一个异步云边协同的深度强化学习算法,该算法充分利用了云边双方的计...     

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.     

Joint partial computation offloading and resource allocation inMEC-enable networks

The sudden surge of various applications poses great challenges to the computation capability of mobile devices. To address this issue, computation offloading to multi-access edge computing(MEC) was proposed as a promising paradigm. This paper studies partial computation offloading scenario by considering time delay and energy consumption, where the task can be splitted into several blocks and computed both in local devices and MEC, respectively. Since the formulated problem is a nonconvex probl...