基于移动边缘计算的任务调度算法设计

移动边缘计算(Mobile Edge Computing, MEC)作为一种新兴的计算模式可以将智能设备上的任务调度到MEC服务器中执行以解决智能设备资源受限问题。多用户场景下以时延和任务依赖性为约束的任务调度问题是移动边缘计算中的研究热点之一。针对该问题建立了任务调度模型,然后依据场景特性将任务调度问题转换为最小化能量消耗问题。针对任务调度问题的实时性和持续性进一步将优化问题缩放至较小规模的优化问题,并依据优化问题的解设计了一个实时调度算法。最后使用遗传算法作为对比算法进行仿真实验。实验结果表明实时调度算法比遗传算法更有效地降低了智能设备整体能量消耗,并在高并发、高时延要求等情况下仍保持良好的性能。     

5G MEC边缘智能架构研究

作为边缘计算与人工智能融合驱动的新模式,边缘智能已然渗透到各个行业。5G MEC作为运营商新型网络边缘的锚点,需要借助边缘智能来充分释放网络边缘价值。文章初步探讨网络边缘智能化需求,提出一种基于5G MEC的边缘智能优化架构,扩展了面向异构计算的弹性AI加速服务和自适应云边智能协同调度能力,从而实现了MEC平台运营智能化和AI能力服务化。     

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。     

移动边缘计算网络安全防护关键技术研究

随着物联网和5G网络的快速发展,单纯依靠云计算的集中式数据处理方式将无法满足以物联网感知为背景的大规模数据处理需求。移动边缘计算(MEC)作为新兴计算范式,是云计算的有力补充。但MEC的开放特性加剧了其面临的非授权访问、敏感数据泄露、网络攻击等安全风险。文章基于MEC在5G网络环境中面临的安全风险,结合MEC网络基础支撑统一化、能力服务化、流程编排化等特点,提出移动边缘计算网络安全防护方案,突破安全功能高效虚拟化、安全云服务联动、安全服务动态编排、安全功能自适应部署与协同调度、态势感知和高级威胁检测等技术瓶颈,形成移动边缘计算网络安全纵深防御体系,对于推进移动通信网络信息安全建设具有重要意义。     

基于5G边缘计算的智能电网高性价比任务调度

针对智能电网环境中电力数据量庞大且对处理时效性要求高的问题,将5G边缘计算引入智能电网系统.研究了基于5G边缘计算的智能电网任务调度问题,在满足电网任务完成需求的同时,最大限度地降低成本.基于此提出了一种基于贪心策略的启发式任务调度算法,通过与两种算法在包括输入任务数、传输数据大小和延迟要求等参数下的比较,验证了所提算...     

基于雾计算的智能工厂实时任务调度架构

针对传统的基于云的任务调度架构中没有充分利用智能工厂的资源,以及远距离传输导致高传输时延的问题,提出了一种基于雾计算的实时任务调度架构。设计了一种基于雾计算的智能工厂网络架构;考虑到工厂任务的时延敏感性和优先级特性,提出了一种基于动态优先级的任务调度模型,该模型被雾节点用来调度和执行等待队列中的任务;基于提出的网络架构和任务调度模型,提出了一种任务卸载策略,该策略可以被用于解决智能工厂中的资源利用问题。仿真结果证明了提出的实时任务调度架构在智能工厂中应用的可行性和有效性。     

面向5G的MEC系统关键技术

移动边缘计算(MEC)是未来5G移动通信系统提升服务应用能力的重要技术手段之一。通过在无线接入网络的边缘节点处部署具备计算、存储和通信能力的服务应用平台,MEC能够有效处理终端用户的高时效性业务需求,大幅度缩短端到端时延,并解决核心网络的数据流量瓶颈等相关问题。     

云计算及LLF算法的光纤数据差异化调度策略

现有光纤数据差异化调度策略忽视数据节点的排序,导致构建的调度模型效率较低,影响数据调度速度,为提高光纤数据差异化调度能力,提出基于云计算及LLF算法制定光纤数据差异化调度策略。排列LLF算法下松弛度队列顺序,确保松弛度较大任务能够率先完成,基于LLF算法设计数据调度模型,求出光纤数据调度范围,制定云计算环境下数据差异化调度策略,提升队列排序的处理能力,提高光纤数据调度效率。实验结果可知,该调度策略的数据平均计算时间约为263s,数据平均调度时间为186s,验证了所提方法能够有效提升数据计算及调度效率。     

5G专网在智慧矿山的应用

5G专网具备大带宽、海量连接、低时延、高可靠性、高安全性等能力,在网络覆盖性、技术演进、业务质量、移动业务的连接性、综合成本等方面具有综合性优势。当前矿山行业面临信息化、智能化、数字化转型升级挑战,文章梳理智慧矿山的各场景业务需求,采用5 G网络+UPF (用户平面功能)+MEC(多接入边缘计算)平台的总体解决方案,实现智慧矿山应用;提供5G+远控、5G+高清视频、5G+数据采集等能力;对工业数据的全面深度感知、实时传输交换、快速计算处理和建模分析,实现智能化生产和数字化升级。     

5G边缘计算安全研究与应用

本论文聚焦在5G边缘计算安全研究与应用,包括5G边缘计算安全风险、5G边缘安计算安全防护要求以及5G边缘计算安全应用。首先从网络服务、硬件环境、虚拟化、边缘计算平台、能力开放、应用、管理、数据方面明确5G边缘计算安全风险,然后针对安全风险提出对应的安全防护要求,并以智能电网为例介绍了5G边缘计算安全应用。论文为5G边缘计算安全的风险以及防护要求分析等研究提供支持,为5G边缘计算安全应用提供发展思路。     

EA-DFPSO: An intelligent energy-efficient scheduling algorithm for mobile edge networks

Cloud data centers have become overwhelmed with data-intensive applications due to the limited computational capabilities of mobile terminals. Mobile edge computing is emerging as a potential paradigm to host application execution at the edge of networks to reduce transmission delays. Compute nodes are usually distributed in edge environments, enabling crucially efficient task scheduling among those nodes to achieve reduced processing time. Moreover, it is imperative to conserve edge server energy, enhancing their lifetimes. To this end, this paper proposes a novel task scheduling algorithm named Energy-aware Double-fitness Particle Swarm Optimization (EA-DFPSO) that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution time. The proposed EA-DFPSO algorithm applies a dual fitness function to search for an optimal tasks-scheduling scheme for saving edge server energy while maintaining service quality for tasks. Extensive experimentation demonstrates that our proposed EA-DFPSO algorithm outperforms the existing traditional scheduling algorithms to achieve reduced task completion time and conserve energy in an edge computing environment.     

基于5G的警务集群系统

通过分析集群通信系统沿专网与公网方向发展演进的技术趋势,结合公安调度需求研究了基于5G切片的警务集群系统体系结构,包括应用层、服务层、传输层、终端层、标准及管理体系和安全保障体系。在网络组网架构方面,通过超高可靠低时延通信(Ultra-reliable and Low Latency Communications,uRLLC)切片传输控制信号,增强型移动宽带(Enhanced Mobile Broadband,eMBB)切片传输业务内容,并提出集群业务软件中通信调度业务逻辑、综合业务适配和维护管理软件的模块组成,对其应用的协同算法、时延保证、安全可靠性和可扩展性等关键技术问题给出建议。基于多智能体控制模型提出多接入边缘计算(Multiple Access Edge Computing,MEC)服务器之间状态同步协调算法,为警务集群系统在5G技术体制下的进一步发展提供了基础。     

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。     

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。     

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.     

MEC enabled satellite-terrestrial network:architecture,key technique and challenge

The introduction of mobile edge computing (MEC) technology in satellite-terrestrial networks can effectively improve the quality of user experience and reduce network redundant traffic,but also brings some challenges.Firstly,the basic architecture of satellite-terrestrial networks and MEC technology was introduced.Moreover,the motivation of introducing MEC into satellite-terrestrial networks and the deployment of MEC were discussed.Then,the architecture of MEC enabled satellite-terrestrial networks was proposed,and the key techniques and typical applications were summarized and analyzed.Finally,the key challenges such as task scheduling and mobility management and some open research issues in integrated networks were summarized.It hopes to provide new ideas for future research in this field.     

An Optimized Collaborative Scheduling Algorithm for Prioritized Tasks with Shared Resources in Mobile-Edge and Cloud Computing Systems

Mobile edge computing (MEC) is a promising technology that has the potential to meet the latency requirements of next-generation mobile networks. Since MEC servers have limited resources, an orchestrator utilizes a scheduling algorithm to decide where and when each task should execute so that the quality of service (QoS) of each task is achieved. The scheduling algorithm should use the least possible resources required to meet the service demands. In this paper, we develop a two-level cooperative scheduling algorithm with a centralized orchestrator layer. The first scheduling level is used to schedule tasks locally on MEC servers. In contrast, the second level resides at the orchestrator and assigns tasks to a neighboring base station or the cloud. The tasks serve in accordance with their priority, which is determined by the latency and required throughput. We also present a resource optimization algorithm for determining resource distribution in the system in order to ensure satisfactory service availability at the minimum cost. The resource optimization algorithm contains two variations that can be employed depending on the traffic model. One variant is used when the traffic is uniformly distributed, and the other is used when the traffic load is unbalanced among base stations. Numerical results show that the cooperative model of task scheduling outperforms the non-cooperative model. Furthermore, the results show that the suggested scheduling algorithm performs better than other well-known scheduling algorithms, such as shortest job first scheduling and earliest deadline first scheduling.