gEdge:基于容器技术的云边协同的异构计算框架

由于按需灵活配置、高可用性、高资源利用率等优点,云计算技术成为过去十年的主流计算范式.随着万物互联时代的到来,单独依赖云计算技术已经无法满足数以亿计的IoT设备及其数据流量的需求.边缘计算可以被看作是云计算的进化,它因5G网络和物联网的崛起而诞生.随着云游戏、VR技术以及人工智能技术在日常生活中的广泛运用,对计算资源的需求也在日渐增长.然而,受体积与功耗限制,处于边缘的节点设备算力较弱.本文提出了gEdge:一种基于容器技术的云边协同的异构计算框架.该框架通过GPU虚拟化技术,将云端的物理GPU资源分为多块虚拟GPU资源,按需为边缘节点提供GPU算力资源,并且对用户容器无感知.实验表明,使用gEdge框架使边缘节点使用的容器镜像体积降低了48.8%,容器启动时间降低了35.5%,平均相对运行速度提高了213%.

Edge computing oriented virtual optical network mapping scheme based on fragmentation prediction

As edge computing services soar,the problem of resource fragmentation situation is greatly worsened in elastic optical networks(EON).Aimed to solve this problem,this article proposes the fragmentation prediction model that makes full use of the gate recurrent unit(GRU)algorithm.Based on the fragmentation prediction model,one virtual optical network mapping scheme is presented for edge computing driven EON.With the minimum of fragmentation degree all over the whole EON,the virtual network mapping can be successively conducted.Test results show that the proposed approach can reduce blocking rate,and the supporting ability for virtual optical network services is greatly improved.

SFC placement and dynamic resource allocation based on VNF performance-resource function and service requirement in cloud-edge environment

With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the network has risen sharply.Due to the high cost of edge computing resources,coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge.In this paper,we focus on optimiz-ing the placement of network services in cloud-edge environ-ments to maximize the efficiency.It is first proved that,in cloud-edge environments,placing one service function chain(SFC)integrally in the cloud or at the edge can improve the utilization efficiency of edge resources.Then a virtual network function(VNF)performance-resource(P-R)function is proposed to repre-sent the relationship between the VNF instance computing per-formance and the allocated computing resource.To select the SFCs that are most suitable to deploy at the edge,a VNF place-ment and resource allocation model is built to configure each VNF with its particular P-R function.Moreover,a heuristic recur-sive algorithm is designed called the recursive algorithm for max edge throughput(RMET)to solve the model.Through simula-tions on two scenarios,it is verified that RMET can improve the utilization efficiency of edge computing resources.

在Solid Edge中实现虚拟装配

探讨在敏捷制造环境下，虚拟公司的各个合作伙伴如何在设计阶段就能够进行异地协调。以盟主企业为核心，各合作伙伴分别进行零件、组件、工艺、装配设计，通过虚拟装配，使得各合作伙伴能够及时了解相互之间的设计情况，达到并行工作的目的。文中探讨了在Solid Edge软件中，实现虚拟装配的方法。

基于Solid Edge的齿轮泵虚拟装配系统

利用SolidEdge提供的Part模块进行三维实体设计 ,用Draft模块生成零件图 ,采用VB的ActiveXAutomation技术调用工程库文件对零件实现尺寸驱动。利用VB作为平台将整个工作过程进行整合 。

基于Solid Edge的不锈钢圆筒形件拉深模具设计的研究

分析了不锈钢在拉深中存在的工艺问题和如何选用模具材料以延长模具的寿命。并基于SolidEdge平台,采用参数设计技术和VisualBasic工具,开发了一个圆筒形零件拉深模具系统,该系统能够全参数化进行零件设计并生成零件图、模具虚拟安装并生成模具装配图。

基于SOLID EDGE的虚拟装配技术的应用

以三维CAD软件SOLIDEDGE(V8.0)为软件平台,以具体实例介绍了虚拟装配技术的原理和方法。

基于Solid Edge软件的虚拟装配及运动仿真

介绍了Solid Edge软件装配模块的主要功能,以油泵主要部件为对象进行虚拟装配设计,并进行了干涉检查,实现了运动仿真。

基于Solid Edge环境下圆柱齿轮减速器的虚拟装配与运动仿真

介绍了在Solid Edge环境下,在齿轮减速器设计过程中应用虚拟装配技术和三维环境下模拟机构运动的实现方法,为减速器的研制提供了一种新的设计方法与实施途径。

用VB开发Solid Edge柱塞泵虚拟装配系统的研究

利用SolidEdge提供的Part模块进行三维实体设计 ,并以Draft模块生成零件图 ,通过VB的ActiveXAutomation技术调用工程库文件对零件进行尺寸驱动。在VB平台上将整个工作过程整合 ,实现了柱塞泵的虚拟装配。

A Review in the Core Technologies of 5G: Device-to-Device Communication, Multi-Access Edge Computing and Network Function Virtualization

5G is a new generation of mobile networking that aims to achieve unparalleled speed and performance. To accomplish this, three technologies, Device-to-Device communication (D2D), multi-access edge computing (MEC) and network function virtualization (NFV) with ClickOS, have been a significant part of 5G, and this paper mainly discusses them. D2D enables direct communication between devices without the relay of base station. In 5G, a two-tier cellular network composed of traditional cellular network system and D2D is an efficient method for realizing high-speed communication. MEC unloads work from end devices and clouds platforms to widespread nodes, and connects the nodes together with outside devices and third-party providers, in order to diminish the overloading effect on any device caused by enormous applications and improve users’ quality of experience (QoE). There is also a NFV method in order to fulfill the 5G requirements. In this part, an optimized virtual machine for middle-boxes named ClickOS is introduced, and it is evaluated in several aspects. Some middle boxes are being implemented in the ClickOS and proved to have outstanding performances.

A Heterogeneous Virtual Machines Resource Allocation Scheme in Slices Architecture of 5G Edge Datacenter

In the paper,we investigate the heterogeneous resource allocation scheme for virtual machines with slicing technology in the 5G/B5G edge computing environment.In general,the different slices for different task scenarios exist in the same edge layer synchronously.A lot of researches reveal that the virtual machines of different slices indicate strong heterogeneity with different reserved resource granularity.In the condition,the allocation process is a NP hard problem and difficult for the actual demand of the tasks in the strongly heterogeneous environment.Based on the slicing and container concept,we propose the resource allocation scheme named Two-Dimension allocation and correlation placement Scheme(TDACP).The scheme divides the resource allocation and management work into three stages in this paper:In the first stage,it designs reasonably strategy to allocate resources to different task slices according to demand.In the second stage,it establishes an equivalent relationship between the virtual machine reserved resource capacity and the Service-Level Agreement(SLA)of the virtual machine in different slices.In the third stage,it designs a placement optimization strategy to schedule the equivalent virtual machines in the physical servers.Thus,it is able to establish a virtual machine placement strategy with high resource utilization efficiency and low time cost.The simulation results indicate that the proposed scheme is able to suppress the problem of uneven resource allocation which is caused by the pure preemptive scheduling strategy.It adjusts the number of equivalent virtual machines based on the SLA range of system parameter,and reduces the SLA probability of physical servers effectively based on resource utilization time sampling series linear.The scheme is able to guarantee resource allocation and management work orderly and efficiently in the edge datacenter slices.

Constrained Edge Dynamic Deleting in CD-TIN Based on Influence Domain Retriangulation of Virtual Point

Constrained Delaunay triangulated irregular network is one kind of dynamic data structures used in geosciences. The research on point and edges insertion in CD-TIN is the basis of its application. Comparing with the algorithms of points and constrained edge insertion, there are very a few researches on constrained edge deletion in CD-TIN. Based on the analysis of the polymorphism of constrained edge, virtual points are used to describe the intersection of constrained edges. A new algorithm is presented, called as influence domain retriangulating for virtual point （IDRVP）, to delete constrained edges with virtual points. The algorithm is complete in topology. Finally, the algorithm is tested by some applications cases.

Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes

Abstract:Fog computing provides quality of service for cloud infrastructure.As the data computation intensifies,edge computing becomes difficult.Therefore,mobile fog computing is used for reducing traffic and the time for data computation in the network.In previous studies,software-defined networking(SDN)and network functions virtualization(NFV)were used separately in edge computing.Current industrial and academic research is tackling to integrate SDN and NFV in different environments to address the challenges in performance,reliability,and scalability.SDN/NFV is still in development.The traditional Internet of things(IoT)data analysis system is only based on a linear and time-variant system that needs an IoT data system with a high-precision model.This paper proposes a combined architecture of SDN and NFV on an edge node server for IoT devices to reduce the computational complexity in cloud-based fog computing.SDN provides a generalization structure of the forwarding plane,which is separated from the control plane.Meanwhile,NFV concentrates on virtualization by combining the forwarding model with virtual network functions(VNFs)as a single or chain of VNFs,which leads to interoperability and consistency.The orchestrator layer in the proposed software-defined NFV is responsible for handling real-time tasks by using an edge node server through the SDN controller via four actions:task creation,modification,operation,and completion.Our proposed architecture is simulated on the EstiNet simulator,and total time delay,reliability,and satisfaction are used as evaluation parameters.The simulation results are compared with the results of existing architectures,such as software-defined unified virtual monitoring function and ASTP,to analyze the performance of the proposed architecture.The analysis results indicate that our proposed architecture achieves better performance in terms of total time delay(1800 s for 200 IoT devices),reliability(90%),and satisfaction(90%).

Teaching mechanism empowered by virtual simulation: Edge computing–driven approach

With the rapid development of network and communication techniques,the teaching forms have become diversified.To enhance the education experience and improve the teaching environment,an increasing number of educational institutions have adopted virtual simulation technology.A typical teaching mechanism is to exploit Virtual Reality(VR)technology,which affords participants an immersive experience.Unquestionably,such a VRbased mode is highly approved.However,the performance of this technology requires further optimization.On one hand,for VR 360video,the current intraframe decision cannot adapt to rapid response demands.On the other hand,the generated data size is considerably large and fast computation may not be realized,depending on the local VR device.Therefore,this study proposes an improved teaching mechanism empowered by edge computing–driven VR,called VE4T,that involves two parts.First,an intraframe decision algorithm for VR 360videos is devised to realize the rapid responses.Second,an edge computing framework is proposed to offload some tasks to an edge server for computation,where a task scheduling strategy is developed to check whether a task needs to be offloaded.Finally,experiments are performed using a practical teaching scenario with some VR devices.The obtained results demonstrate that VE4T is more efficient than existing mechanisms.

Soft Computing Based Metaheuristic Algorithms for Resource Management in Edge Computing Environment

In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices.Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge.One of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved performance.With this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing environment.The SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource allocation.The goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy consumption.The problem is formulated based on the availability of VMs,task characteristics,and queue dynamics.The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data center.For experimental validation,a comprehensive set of simulations were performed using the CloudSim tool.The experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.

Edge robotics:are we ready?an experimental evaluation of current vision and future directions

Cloud-based robotics systems leverage a wide range of Information Technologies(IT)to offer tangible benefits like cost reduction,powerful computational capabilities,data offloading,etc.However,the centralized nature of cloud computing is not well-suited for a multitude of Operational Technologies(OT)nowadays used in robotics systems that require strict real-time guarantees and security.Edge computing and fog computing are complementary approaches that aim at mitigating some of these challenges by providing computing capabilities closer to the users.The goal of this work is hence threefold:i)to analyze the current edge computing and fog computing landscape in the context of robotics systems,ii)to experimentally evaluate an end-to-end robotics system based on solutions proposed in the literature,and iii)to experimentally identify current benefits and open challenges of edge computing and fog computing.Results show that,in the case of an exemplary delivery application comprising two mobile robots,the robot coordination and range can be improved by consuming real-time radio information available at the edge.However,our evaluation highlights that the existing software,wireless and virtualization technologies still require substantial evolution to fully support edge-based robotics systems.

Fast Motion Estimation Algorithm with Edge Alignment for H.264 Encoder

Image sequences processing and video encoding are extremely time consuming problems. The time complexity of them depends on image contents. This paper presents an estimation of a block motion method for video coding with edge alignment. This method uses blocks of size 4 × 4 and its basic idea is to find motion vector using the edge position in each video coding block. The method finds the motion vectors more accurately and faster than any known classical method that calculates all the possibilities. Our presented algorithm is compared with known classical algorithms using the evaluation function of the peak signal-to-noise ratio. For comparison of the methods we are using parameters such as time, CPU usage, and size of compressed data. The comparison is made on benchmark data in color format YUV. Results of our proposed method are comparable and in some cases better than results of standard classical algorithms.

元宇宙:概念、技术及应用研究综述

网络技术、人机交互和人工智能等技术的飞速发展催生了元宇宙,并进一步促进人们物质生活各个方面的数字化转型.2021年是元宇宙元年,元宇宙作为一个新兴的概念受到产业界、学术界、媒体界及公众的广泛关注.本文尝试从技术维度和应用角度深度剖析元宇宙.首先,从定义、起源与发展、特征和关键技术(网络及运算技术、物联网技术、人机交互技术、电子游戏技术、区块链技术、数字孪生技术和其他技术)等多方面对元宇宙的概念及内涵进行论述;然后,讨论了当下布局元宇宙的企业和应用实例;最后,剖析了目前元宇宙发展存在的问题和机遇,并对未来研究与应用进行了展望.通过对元宇宙当前的发展状况、研究趋势进行归纳分析以及科学地评估元宇宙的落地应用,为元宇宙研究人员提供有益的参考和借鉴.

基于改进深度Q网络的虚拟电厂实时优化调度

深度强化学习算法以数据为驱动,且不依赖具体模型,能有效应对虚拟电厂运营中的复杂性问题。然而,现有算法难以严格执行操作约束,在实际系统中的应用受到限制。为了克服这一问题,提出了一种基于深度强化学习的改进深度Q网络(improved deep Q-network,MDQN)算法。该算法将深度神经网络表达为混合整数规划公式,以确保在动作空间内严格执行所有操作约束,从而保证了所制定的调度在实际运行中的可行性。此外,还进行了敏感性分析,以灵活地调整超参数,为算法的优化提供了更大的灵活性。最后,通过对比实验验证了MDQN算法的优越性能。该算法为应对虚拟电厂运营中的复杂性问题提供了一种有效的解决方案。