V2X多节点协同分布式卸载策略

为了应对车联网中计算资源密集、可分离型任务的卸载环境动态变化和不同协同节点通信、计算资源存在差异的问题,提出了一种在V2X下多协同节点串行卸载、并行计算的分布式卸载策略。该策略利用车辆可预测的行驶轨迹,对任务进行不等拆分,分布式计算于本地、MEC及协同车辆,建立系统时延最小化的优化问题。为求解该优化问题,设计了博弈论的卸载机制,以实现协同节点串行卸载的执行顺序;鉴于车联网的动态时变特性,利用序列二次规划算法,给出了最优的任务不等拆分。仿真结果表明,所提策略能够有效减少计算任务系统时延,且当多协同节点分布式卸载服务时,所提策略在不同的参数条件下仍然能够保持稳定的系统性能。     

一种基于车载边缘计算的可预测的任务卸载模型

解决计算密集型任务与车载计算设备资源匮乏之间的矛盾,目前常用的一种解决方案是将计算任务卸载到无线电接入网络的边缘.本文研究了车载边缘计算下的基于移动感知的计算任务卸载模型.考虑到车辆的移动性特性和任务的最大等待时间,本文通过联合优化任务卸载决策,通信和计算资源分配,使车辆选择最佳的卸载执行时间,以达到最大限度地降低系统...     

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。     

一种基于车边云协同的车联网计算卸载策略

随着智能交通的快速发展和车联网中数据流量爆炸式的增长,汽车终端请求卸载的任务对时延和带宽有了更加严苛的要求。在现有的云计算服务模式中,车辆可以访问云服务器来获得强大的计算、存储和网络资源,但缺点是通信传输时延较大,仅依靠云计算可能会导致过度的延迟。为了更加合理利用资源、减小时延、优化卸载策略,提出了一种基于粒子群优化算法的“车-边-云”协同卸载方案。首先通过接入点附近的软件定义网络（Software Define Network,SDN)控制器根据终端用户附近边缘节点、本地终端和云计算节点的计算资源和容量情况得出最优的卸载策略,充分利用本地、移动边缘计算（Mobile Edge Computing,MEC）设备、云端的计算资源,然后通过粒子群优化算法得出“车-边-云”各计算节点的卸载系数,即最优卸载策略。实验结果表明,相比于其他卸载策略,所提的卸载机制对时延优化效果明显,提高了计算资源的利用率。     

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...     

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。     

移动边缘计算中分布式异构任务卸载算法

随着物联网(IoT)迅速发展,移动边缘计算(MEC)在提供高性能、低延迟计算服务方面的作用日益明显。然而,在面向IoT业务的MEC(MEC-IoT)时变环境中,不同边缘设备和应用业务在时延和能耗等方面具有显著的异构性,对高效的任务卸载及资源分配构成严峻挑战。针对上述问题,该文提出一种动态的分布式异构任务卸载算法(D2HM),该算法利用分布式博弈机制并结合李雅普诺夫优化理论,设计了一种资源的动态报价机制,并实现了对不同业务类型差异化控制和计算资源的弹性按需分配,仿真结果表明,所提的算法可以满足异构任务的多样化计算需求,并在保证网络稳定性的前提下降低系统的平均时延。

     

面向绿色计算的车辆协同任务卸载方法

车辆边缘计算(VEC)为处理计算密集、延迟敏感型任务提供了新的范式,然而边缘服务器在整合可再生能源方面的能力较差。因此,为了提高边缘服务器的能效,该文设计了一种面向绿色计算的车辆协同任务卸载框架。在该框架中,车辆配备能源收集(EH)设备,通过彼此间共享绿色能源和计算资源协作执行任务。为有效促进车辆的参与积极性,该文通过动态定价激励车辆,并综合考虑了车辆的移动性、任务优先级等。为了使卸载决策适应动态环境的变化,该文提出了一种基于双延迟深度确定性策略梯度(TD3)的任务卸载方法,以在最大化所有车辆平均任务完成效用的同时减少边缘端电网电力的使用。最后,仿真结果验证了该方法的有效性,相比基于深度确定性策略梯度(DDPG)和基于贪心原则(GPE)的方法在性能上分别提升了7.34%和37.47%。     

MEC下工业AGV的任务卸载策略研究与分析

近年来,AGV被广泛运用到智能化工业生产中,但AGV处理能力有限,难以满足人们对高质量服务的要求。为促进AGV在工业领域的快速发展,将移动边缘计算引入工业AGV中,提出了一种基于粒子群优化算法的计算任务卸载策略,以降低系统迟延,该策略考虑任务优先级,根据任务优先级别来卸载计算任务。结果表明,该策略能有效降低系统时延。     

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。     

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.     

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.     

Users' experience matter: Delay sensitivity-aware computation offloading in mobile edge computing

As a promising computing paradigm, Mobile Edge Computing (MEC) provides communication and computing capability at the edge of the network to address the concerns of massive computation requirements, constrained battery capacity and limited bandwidth of the Internet of Things (IoT) systems. Most existing works on mobile edge task ignores the delay sensitivities, which may lead to the degraded utility of computation offloading and dissatisfied users. In this paper, we study the delay sensitivity-aware computation offloading by jointly considering both user's tolerance towards delay of task execution and the network status under computation and communication constraints. Specifically, we use a specific multi-user and multi-server MEC system to define the latency sensitivity of task offloading based on the analysis of delay distribution of task categories. Then, we propose a scoring mechanism to evaluate the sensitivity-dependent utility of task execution and devise a Centralized Iterative Redirection Offloading (CIRO) algorithm to collect all information in the MEC system. By starting with an initial offloading strategy, the CIRO algorithm enables IoT devices to cooperate and iteratively redirect task offloading decisions to optimize the offloading strategy until it converges. Extensive simulation results show that our method can significantly improve the utility of computation offloading in MEC systems and has lower time complexity than existing algorithms.     

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.     

Joint offloading strategy based on quantum particle swarm optimization for MEC-enabled vehicular networks

With the development of the mobile communication technology, a wide variety of envisioned intelligent transportation systems have emerged and put forward more stringent requirements for vehicular communications. Most of computation-intensive and power-hungry applications result in a large amount of energy consumption and computation costs, which bring great challenges to the on-board system. It is necessary to exploit traffic offloading and scheduling in vehicular networks to ensure the Quality of Experience (QoE). In this paper, a joint offloading strategy based on quantum particle swarm optimization for the Mobile Edge Computing (MEC) enabled vehicular networks is presented. To minimize the delay cost and energy consumption, a task execution optimization model is formulated to assign the task to the available service nodes, which includes the service vehicles and the nearby Road Side Units (RSUs). For the task offloading process via Vehicle to Vehicle (V2V) communication, a vehicle selection algorithm is introduced to obtain an optimal offloading decision sequence. Next, an improved quantum particle swarm optimization algorithm for joint offloading is proposed to optimize the task delay and energy consumption. To maintain the diversity of the population, the crossover operator is introduced to exchange information among individuals. Besides, the crossover probability is defined to improve the search ability and convergence speed of the algorithm. Meanwhile, an adaptive shrinkage expansion factor is designed to improve the local search accuracy in the later iterations. Simulation results show that the proposed joint offloading strategy can effectively reduce the system overhead and the task completion delay under different system parameters.     

Internet of Things offloading: Ongoing issues,opportunities, and future challenges

Internet of Things (IoT) has very remarkable advantages over customary communication technologies. However, IoT suffers from different issues, such as limited battery life, low storage capacity, and little computing capacity. For this reason, in many IoT applications and devices, we require an alternative unit to execute the tasks from the user's device and return results. In general, the problem of limited resources by transferring the computation workload to other devices/systems with better resources is addressed by offloading computation. It can be focused on improving the application, extending battery life, or expanding storage capacity. The offloading operation can be performed based on various quality of service (QoS) parameters that contain computational demands for load balancing, response time, application, energy consumption, latency, and other things. Moreover, the systematic literature review (SLR) method is used to identify, assess, and integrate findings from all relevant studies that address one or more research questions on IoT offloading and conduct a comprehensive study of empirical research on offloading techniques. However, we present a new taxonomy for them based on offloading decision mechanisms and overall architectures. Furthermore, we offer a parametric comparison for the offloading methods. As well, we present the future direction and research opportunities in IoT offloading computation. This survey will assist academics and practitioners to directly understand the progress in IoT offloading.     

异构蜂窝网络中分层任务卸载：建模与优化

为提高计算任务卸载的效率,提出了一种基于D2D通信、移动边缘计算和云计算的分层任务卸载框架,并引入D2D协作中继技术辅助用户接入远端计算资源。针对所提任务卸载框架在多用户场景中可能存在上行通信拥塞、边缘计算资源受限、D2D复用干扰和云计算回程时延等问题,设计了一种基于博弈论的卸载调度和负载均衡方案,充分利用了所提任务卸载框架中各层计算和通信资源。仿真结果表明,所提方案能够有效降低端到端时延和卸载能耗,并在资源受限的条件下具有良好的稳定性。     

车载边缘计算中任务卸载和服务缓存的联合智能优化

针对车载环境下有限的网络资源和大量用户需求之间的矛盾,提出了智能驱动的车载边缘计算网络架构,以实现网络资源的全面协同和智能管理.基于该架构,设计了任务卸载和服务缓存的联合优化机制,对用户任务卸载以及计算和缓存资源的调度进行了建模.鉴于车载网络的动态、随机和时变的特性,利用异步分布式强化学习算法,给出了最优的卸载决策和资...