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。     

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。     

Secured data offloading using reinforcement learning and Markov decision process in mobile edge computing

Mobile Internet services are developing rapidly for several applications based on computational ability such as augmented/virtual reality, vehicular networks, etc. The mobile terminals are enabled using mobile edge computing (MEC) for offloading the task at the edge of the cellular networks, but offloading is still a challenging issue due to the dynamism, and uncertainty of upcoming IoT requests and wireless channel state. Moreover, securing the offloading data enhanced the challenges of computational complexities and required a secure and efficient offloading technique. To tackle the mentioned issues, a reinforcement learning-based Markov decision process offloading model is proposed that optimized energy efficiency, and mobile users' time by considering the constrained computation of IoT devices, moreover guarantees efficient resource sharing among multiple users. An advanced encryption standard is employed in this work to fulfil the requirements of data security. The simulation outputs reveal that the proposed approach surpasses the existing baseline models for offloading overhead and service cost QoS parameters ensuring secure data offloading.     

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.     

无人机辅助MEC系统：架构、关键技术与未来挑战

摘 要：随着5G加速部署和6G研究工作持续推进，无人机（unmanned aerial vehicle，UAV）辅助移动边缘计算（mobile edge computing，MEC）技术在克服复杂地域限制、解决终端设备计算需求和提高系统任务卸载速率等方面具有显著优势，得到了学术界和工业界的广泛关注。首先阐明了无人机辅助MEC系统的概念和技术优势，提出了一种无人机辅助MEC通用架构，并给出了各个功能模块定义；接着总结了典型应用场景，梳理了现有的关键性技术，获得了无人机MEC系统的设计方法；最后对未来的研究方向和存在的挑战进行了展望和阐述。     

Dynamic computation offloading in time-varying environment forultra-dense networks: a stochastic game approach

To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications, combining ultra-dense networks (UDNs) and mobile edge computing (MEC)are considered as important solutions. In the MEC enabled UDNs, one of the most important issues is computation offloading. Although a number of work have been done toward this issue, the problem of dynamic computation offloading in time-varying environment, especially the dynamic computation offloading problem for multi-user, has not been fully considered. Therefore, in order to fill this gap, the dynamic computation offloading problem in time-varying environment for multi-user is considered in this paper. By considering the dynamic changes of channel state and users queue state, the dynamic computation offloading problem for multi-user is formulated as a stochastic game, which aims to optimize the delay and packet loss rate of users. To find the optimal solution of the formulated optimization problem, Nash Q-learning (NQLN) algorithm is proposed which can be quickly converged to a Nash equilibrium solution. Finally, extensive simulation results are presented to demonstrate the superiority of NQLN algorithm. It is shown that NQLN algorithm has better optimization performance than the benchmark schemes.     

基于拉格朗日的计算迁移能耗优化策略

随着移动网络技术的发展和智能终端的普及应用,移动边缘计算已成为云计算的一个重要应用。计算迁移策略已成为移动边缘计算服务的关键问题之一。以移动终端总的计算时间和移动终端能耗最小化为目标,将移动终端的计算迁移资源划分问题建模为一个凸优化问题,运用拉格朗日乘子法进行求解,提出基于阈值的迁移优化策略模型。仿真实验表明,本迁移优化策略模型能有效平衡本地计算和迁移计算之间的关系,为移动边缘计算中执行计算密集型应用提供保障。     

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.     

MEC系统中基于多智能体深度增强学习的协作式任务卸载和资源分配

移动边缘计算(MEC)通过将计算任务卸载到MEC服务器上,在缓解智能移动设备计算负载的同时,可以降低服务时延。然而目前在MEC系统中,关于任务卸载和资源分配仍然存在以下问题：1)边缘节点间缺乏协作;2)计算任务到达与实际环境中动态变化的特征不匹配;3)协作式任务卸载和资源分配动态联合优化问题。为解决上述问题,文章在协作式MEC架构的基础上,提出了一种基于多智能体的深度确定性策略梯度算法(MADDPG)的任务卸载和资源分配算法,最小化系统中所有用户的长期平均成本。仿真结果表明,该算法可以有效降低系统的时延及能耗。     

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...     

Computation energy efficiency maximization based resource allocation scheme in wireless powered mobile edge computing network

For wireless powered mobile edge computing (MEC) network,a system computation energy efficiency (CEE) maximization scheme by considering the limited computation capacity at the MEC server side was proposed.Specifically,a CEE maximization optimization problem was formulated by jointly optimizing the computing frequencies and execution time of the MEC server and the edge user(EU),the transmit power and offloading time of each EU,the energy harvesting time and the transmit power of the power beacon.Since the formulated optimization problem was a non-convex fractional optimization problem and hard to solve,the formulated problem was firstly transformed into a non-convex subtraction problem by means of the generalized fractional programming theory and then transform the subtraction problem into an equivalent convex problem by introducing a series of auxiliary variables.On this basis,an iterative algorithm to obtain the optimal solutions was proposed.Simulation results verify the fast convergence of the proposed algorithm and show that the proposed resource allocation scheme can achieve a higher CEE by comparing with other schemes.     

Aerial edge computing for 6G

In the 6 th generation mobile communication system(6 G) era, a large number of delay-sensitive and computation-intensive applications impose great pressure on resource-constrained Internet of things(IoT) devices. Aerial edge computing is envisioned as a promising and cost-effective solution, especially in hostile environments without terrestrial infrastructures. Therefore, this paper focuses on integrating aerial edge computing into 6 G for providing ubiquitous computing services for IoT devices...     

5G MEC系统安全能力部署方案

多接入边缘计算（multi-access edge computing，MEC）作为5G网络的核心差异能力，是电信运营商为企业客户打造5G专网的关键技术。随着5G MEC节点数量的增多，安全风险和安全防护方案等问题也日益受到关注。首先介绍了5G MEC系统架构，对其潜在安全风险进行了分析。在此基础上，提出了5G MEC系统安全能力部署架构和方案，并介绍部署案例。最后，针对目前边缘计算安全能力部署存在的问题与挑战进行了讨论，为后续研究开发提供了参考。     

5G边缘计算节点部署方法研究

随着5G商用的到来,基于5G三大应用场景的业务需求,现有核心网集中式部署不能满足新的需求,网络随业务流向边缘迁移是产业发展趋势。移动边缘计算靠近用户侧部署,能提供更短时延和保护隐私等功能。本文通过分析移动边缘计算面向的重点行业和重点领域等业务发展需求,协同构建客户的业务、无线和局房资源视图,匹配出移动边缘计算部署机房位置及资源储备。     

电动汽车通信网络的MEC部署策略研究

MEC是5G网络架构中很重要的一个环节.然而,基于时延、带宽、机房条件、安全等方面的考虑,不同业务中MEC服务器的部署位置有着不同要求.文章首先分析了边缘计算的典型业务场景及部署需求,以及在边缘计算的典型业务V2X中需要考虑的问题,并在此基础上提出了适用于电动汽车通信网络的网络建设方案.     

多接入移动边缘计算技术在移动网络的部署实践与思考

多接入移动边缘计算(MEC)技术是当前兴起的一项新技术。通过把计算、存储、带宽和应用等资源放在网络的边缘侧,以减小传输延迟和带宽消耗。MEC可以广泛应用于运营商的4G/5G等移动网络以及Wi-Fi无线网络,并将会成为未来工业自动化和信息化应用的新一代解决方案。本文介绍了多接入移动边缘计算技术如何与4G/5G移动网络结合并在行业场景中的实际尝试和探索,并对主要研究进展和需解决的关键问题进行了介绍。     

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.     

基于5G的MEC网络架构与部署策略

多接入边缘计算(MEC)为5G网络必不可少的网元,实现本地化泄流与云服务提供,解决现有网络垂直封闭烟囱式架构不能满足低时延、高带宽等业务需求问题,降低网络传输投资.基于ETSI与3GPP的5G网络进展,提出MEC网络架构与传统无线接入、传输、承载等网络架构融合,使业务面下沉本地化不同场景部署.结合广州实际情况,对本地化...     

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.     

Dynamic process location management in 5G

5G, as the next generation of wireless networks, promises very high throughput and low latency to mobile users that calls for a substantial innovation in computing management platforms to attend QoS metrics. Thanks to emerging technologies such as software‐defined networking (SDN)/network function virtualization (NFV), many features are available in 5G design to detect and control two types of latency caused by computation and communication. In this paper, taking features of caching mechanisms and SDN into the account, a platform is proposed to minimize latency in 5G via caching big flows intelligently and avoiding bottlenecks that may cause by virtualized computing components. First, the pioneering idea of compromising between the cloud radio access network (CRAN) and mobile edge computing (MEC)/information‐centric network (ICN) via dynamic processing location management platform is investigated. Accordingly, a mathematical optimization problem to minimize the average latency is formulated. Due to the problem complexity, a heuristic algorithm is proposed to treat the latency via dynamic orchestration of processing functionalities. Through numerical results, the performance of the proposed algorithm is analyzed, and the simulations corroborate our analytical results and illustrate the superior performance of the proposed algorithm with acceptable optimality gap.