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.

Service Function Chain Migration in LEO Satellite Networks

With the advancements of software defined network(SDN)and network function virtualization(NFV),service function chain(SFC)placement becomes a crucial enabler for flexible resource scheduling in low earth orbit(LEO)satellite networks.While due to the scarcity of bandwidth resources and dynamic topology of LEO satellites,the static SFC placement schemes may cause performance degradation,resource waste and even service failure.In this paper,we consider migration and establish an online migration model,especially considering the dynamic topology.Given the scarcity of bandwidth resources,the model aims to maximize the total number of accepted SFCs while incurring as little bandwidth cost of SFC transmission and migration as possible.Due to its NP-hardness,we propose a heuristic minimized dynamic SFC migration(MDSM)algorithm that only triggers the migration procedure when new SFCs are rejected.Simulation results demonstrate that MDSM achieves a performance close to the upper bound with lower complexity.

Fuzzy Adaptive Control of Stochastic Nonlinear Systems with Unknown Virtual Control Gain Function

The problem of track control is studied for a class of strict-feedback stochastic nonlinear systems in which unknown virtual control gain function is the main feature. First, the so-called stochastic LaSalle theory is extended to some extent, and accordingly, the results of global ultimate boundedness for stochastic nonlinear systems are developed. Next, a new design scheme of fuzzy adaptive control is proposed. The advantage of it is that it does not require priori knowledge of virtual control gain function sign, which is usually demanded in many designs. At the same time, the track performance of closed-loop systems is improved by adaptive modifying the estimated error upper bound. By theoretical analysis, the signals of closed-loop systems are globally ultimately bounded in probability and the track error converges to a small residual set around the origin in 4th-power expectation.

Mechanism of Kinect-based virtual reality training for motor functional recovery of upper limbs after subacute stroke

The Kinect-based virtual reality system for the Xbox 360 enables users to control and interact with the game console without the need to touch a game controller, and provides rehabilitation training for stroke patients with lower limb dysfunctions. However, the underlying mechanism remains un- clear. In this study, 18 healthy subjects and five patients after subacute stroke were included. The five patients were scanned using functional MRI prior to training, 3 weeks after training and at a 12-week follow-up, and then compared with healthy subjects. The FugI-Meyer Assessment and Wolf Motor Function Test scores of the hemiplegic upper limbs of stroke patients were significantly increased 3 weeks after training and at the 12-week follow-up. Functional MRI results showed that contralateral primary sensorimotor cortex was activated after Kinect-based virtual reality training in the stroke patients compared with the healthy subjects. Contralateral primary sensorimotor cortex, the bilateral supplementary motor area and the ipsilateral cerebellum were also activated during hand-clenching in all 18 healthy subjects. Our findings indicate that Kinect-based virtual reality training could promote the recovery of upper limb motor function in subacute stroke patients, and brain reorganization by Kinect-based virtual reality training may be linked to the contralateral sen- sorimotor cortex.

Virtualizing Network and Service Functions: Impact on ICT Transformation and Standardization

Virtualization of network/service functions means time sharing network/service（and affiliated）resources in a hyper speed manner.The concept of time sharing was popularized in the 1970s with mainframe computing.The same concept has recently resurfaced under the guise of cloud computing and virtualized computing.Although cloud computing was originally used in IT for server virtualization,the ICT industry is taking a new look at virtualization.This paradigm shift is shaking up the computing,storage,networking,and ser vice industries.The hope is that virtualizing and automating configuration and service management/orchestration will save both capes and opex for network transformation.A complimentary trend is the separation（over an open interface）of control and transmission.This is commonly referred to as software defined networking（SDN）.This paper reviews trends in network/service functions,efforts to standardize these functions,and required management and orchestration.

Using the Cooperative Game for Service Placement of Virtual Network Functions

To address the issues that middleboxes as a fundamental part of today's networks are facing, Network Function Virtualization(NFV)has been recently proposed, which in essence asserts to migrate hardware-based middleboxes into software-based virtualized function entities.Due to the demands of virtual services placement in NFV network environment, this paper models the service amount placement problem involving with the resources allocation as a cooperative game and proposes the placement policy by Nash Bargaining Solution(NBS). Specifically,we first introduce the system overview and apply the rigorous cooperative game-theoretic guide to build the mathematical model, which can give consideration to both the responding efficiency of service requirements and the allocation fairness.Then a distributed algorithm corresponding to NBS is designed to achieve predictable network performance for virtual instances placement.Finally, with simulations under various scenarios,the results show that our placement approach can achieve high utilization of network through the analysis of evaluation metrics namely the satisfaction degree and fairness index. With the suitable demand amount of services, the average values of two metrics can reach above 90%. And by tuning the base placement, our solution can enable operators to flexibly balance the tradeoff between satisfaction and fairness of resourcessharing in service platforms.

Functional Virtual Prototyping in Vehicle Chassis Reform Analysis and Improvement Design

The contribution of functional virtual prototyping to vehicle chassis development is presented. The different topics that we took into consideration were reform analysis and improvement design during the vehicle chassis development. A frame of coordinates based on the digital-model was established, the main CAE analy- sis methods, multi-body system dynamics and finite element analysis were applied to the digital-model build by CAD/CAM software. The method was applied in the vehicle chassis reform analysis and improvement design, all the analysis and design projects were implemented in the uniform digital-model, and the development was carried through effectively.

Application of the Somatosensory Interaction Technology Combined with Virtual Reality Technology on Upper Limbs Function in Cerebrovascular Disease Patients

Objective: To explore the effects of the somatosensory interaction technology combined with virtual reality technology on upper limbs function and activities of daily living (ADL) in cerebrovascular disease patients. Methods: Form January, 2019 to December, 2019, 80 cerebrovascular disease patients were recruited, and had been divided into control group (n = 40) and observation group (n = 40), randomly. The control groups received conventional rehabilitation treatment, for 40 minutes per day, while observation group received conventional rehabilitation treatment, for 20 minutes per day, and virtual reality technology treatment, 20 minutes per day, 5 days a week for 4 weeks. Wolf Motor Function Test (WMFT), Fugl-Meyer Assessment-Upper Extremities (FMA-UE) and modified Barthel index (MBI) were used to assess the motor function of the upper limbs and ADL before and after treatment. Results: Before treatment, the scores of WMFT, FMA-UE and MBI were no significant difference between two groups (P > 0.05). The scores improved in both groups after treatment (P < 0.01), and were higher in the observation group than in the control group (P < 0.05). Conclusion: The somatosensory interaction technology combined with virtual reality technology could facilitate to improve the upper limbs function and ADL in cerebrovascular disease patients.

Network Function Virtualization Technology:Progress and Standardization

Network innovation and business transformation are both necessary for telecom operators to adapt to new situations, but operators face challenges in terms of network bearer complexity, business centralization, and IT/CT integration. Network function virtualization （NFV） may inspire new development ideas, but many doubts still exist within industry, especially about how to introduce NFV into an operator＇ s network. This article describes the latest progress in NFV standardization, NFV requirements and hot technology issues, and typical NFV applications in an operator networks.

Optimize the Deployment and Integration for Multicast-Oriented Virtual Network Function Tree

Due to the development of network technology,the number of users is increasing rapidly,and the demand for emerging multicast services is becoming more and more abundant,traffic data is increasing day by day,network nodes are becoming denser,network topology is becoming more complex,and operators’equipment operation and maintenance costs are increasing.Network functions virtualization multicast issues include building a traffic forwarding topology,deploying the required functions,and directing traffic.Combining the two is still a problem to be studied in depth at present,and this paper proposes a two-stage solution where the decisions of these two stages are interdependent.Specifically,this paper decouples multicast traffic forwarding and function delivery.The minimum spanning tree of traffic forwarding is constructed by Steiner tree,and the traffic forwarding is realized by Viterbi-algorithm.Use a general topology network to examine network cost and service performance.Simulation results show that this method can reduce overhead and delay and optimize user experience.

Enhanced Reliability in Network Function Virtualization by Hybrid Hexagon-Cost Efficient Algorithm

In this,communication world, the Network Function Virtualization concept is utilized for many businesses, small services to virtualize the network nodefunction and to build a block that may connect the chain, communication services.Mainly, Virtualized Network Function Forwarding Graph (VNF-FG) has beenused to define the connection between the VNF and to give the best end-to-endservices. In the existing method, VNF mapping and backup VNF were proposedbut there was no profit and reliability improvement of the backup and mapping ofthe primary VNF. As a consequence, this paper offers a Hybrid Hexagon-CostEfficient algorithm for determining the best VNF among multiple VNF and backing up the best VNF, lowering backup costs while increasing dependability. TheVNF is chosen based on the highest cost-aware important measure (CIM) rate,which is used to assess the relevance of the VNF forwarding graph.To achieveoptimal cost-efficiency, VNF with the maximum CIM is selected. After the selection process, updating is processed by three steps which include one backup VNFfrom one SFC, two backup VNF from one Service Function Chain (SFC),and twobackup VNF from different SFC. Finally, this proposed method is compared withCERA, MinCost, MaxRbyInr based on backup cost, number of used PN nodes,SFC request utility, and latency. The simulation result shows that the proposedmethod cuts down the backup cost and computation time by 57% and 45% compared with the CER scheme and improves the cost-efficiency. As a result, this proposed system achieves less backup cost, high reliability, and low timeconsumption which can improve the Virtualized Network Function operation.

Leap Motion-based virtual reality training for improving motor functional recovery of upper limbs and neural reorganization in subacute stroke patients

Virtual reality is nowadays used to facilitate motor recovery in stroke patients. Most virtual reality studies have involved chronic stroke patients; however, brain plasticity remains good in acute and subacute patients. Most virtual reality systems are only applicable to the proximal upper limbs （arms） because of the limitations of their capture systems. Nevertheless, the functional recovery of an affected hand is most difficult in the case of hemiparesis rehabilitation after a stroke. The recently developed Leap Motion controller can track the fine movements of both hands and fingers. Therefore, the present study explored the effects of a Leap Motion-based virtual reality system on subacute stroke. Twenty-six subacute stroke patients were assigned to an experimental group that received virtual reality training along with conventional occupational rehabilitation, and a control group that only received conventional rehabilitation. The Wolf motor func- tion test （WMFT） was used to assess the motor function of the affected upper limb; functional magnetic resonance imaging was used to measure the cortical activation. After four weeks of treatment, the motor functions of the affected upper limbs were significantly improved in all the patients, with the improvement in the experimental group being significantly better than in the control group. The action perfor- mance time in the WMFT significantly decreased in the experimental group. Furthermore, the activation intensity and the laterality index of the contralateral primary sensorimotor cortex increased in both the experimental and control groups. These results confirmed that Leap Motion-based virtual reality training was a promising and feasible supplementary rehabilitation intervention, could facilitate the recovery of motor functions in subacute stroke patients. The study has been registered in the Chinese Clinical Trial Registry （registration number： ChiCTR-OCH- 12002238）.

Adaptive Partial Task Offloading and Virtual Resource Placement in SDN/NFV-Based Network Softwarization

Edge intelligence brings the deployment of applied deep learning(DL)models in edge computing systems to alleviate the core backbone network congestions.The setup of programmable software-defined networking(SDN)control and elastic virtual computing resources within network functions virtualization(NFV)are cooperative for enhancing the applicability of intelligent edge softwarization.To offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization,this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows,link delays,and allocatable bandwidth capacities.Adaptive partial task offloading policy considered the DL-based recommendation to modify efficient virtual resource placement for minimizing the completion time and termination drop ratio.The optimization problem of resource placement is tackled by a deep reinforcement learning(DRL)-based policy following the Markov decision process(MDP).The agent observes the state spaces and applies value-maximized action of available computation resources and adjustable resource allocation steps.The reward formulation primarily considers taskrequired computing resources and action-applied allocation properties.With defined policies of resource determination,the orchestration procedure is configured within each virtual network function(VNF)descriptor using topology and orchestration specification for cloud applications(TOSCA)by specifying the allocated properties.The simulation for the control rule installation is conducted using Mininet and Ryu SDN controller.Average delay and task delivery/drop ratios are used as the key performance metrics.

改进人工势场算法的路径规划

人工势场法是一种适用于局部路径规划的算法,针对人工势场法存在的目标不可达、局部极小值等固有缺陷,提出了一种改进后的人工势场法。首先针对目标不可达问题,将引力影响因子添加到斥力场的生成中,改进斥力场函数,从而避免引力与斥力合力为零的情况。针对局部极小值问题,通过设立虚拟目标点来引入额外外力,打破机器人的平衡问题。通过与其他算法的对比实验,仿真结果显示,改进人工势场法规划的路径长度和消耗时间都更短,稳定性更强。

Virtual reality training improves balance function

Virtual reality is a new technology that simulates a three-dimensional virtual world on a com- puter and enables the generation of visual, audio, and haptic feedback for the full immersion of users. Users can interact with and observe objects in three-dimensional visual space without limitation. At present, virtual reality training has been widely used in rehabilitation therapy for balance dysfunction. This paper summarizes related articles and other articles suggesting that virtual reality training can improve balance dysfunction in patients after neurological diseases. When patients perform virtual reality training, the prefrontal, parietal cortical areas and other motor cortical networks are activated. These activations may be involved in the reconstruction of neurons in the cerebral cortex. Growing evidence from clinical studies reveals that virtual reality training improves the neurological function of patients with spinal cord injury, cerebral palsy and other neurological impairments. These findings suggest that virtual reality training can acti- vate the cerebral cortex and improve the spatial orientation capacity of patients, thus facilitating the cortex to control balance and increase motion function.

基于虚拟仿真技术的函数传参实验教学系统开发与应用

由于计算机程序运行过程具有不可见性,教师运用绘图讲解的方法很难清晰地描述其参数的动态变化过程,学生也较难理解各传参方式的工作机制,致使函数传参问题成为教师教和学生学的痛点。一般来说传参方式共有四种,但目前还没有能同时支持这四种方式的编程语言。为了满足不同编程语言学习者的需求,利用虚拟仿真技术构建了通用的函数传参线上实验教学系统。其中,虚拟仿真模块可根据录入的参数值和传参方式,模拟展示函数伪代码运行时计算机内存的动态变化,让传参过程变得直观可见,便于学生理解函数传参的工作机制;基础实验训练、拓展实验提高和测试拔高等模块对教学目标的达成均起相应的辅助作用。教学实践结果显示,该方法能有效调动学生学习的积极性和主动性,在辅助学生掌握函数传参知识方面有明显成效。

基于资源感知的多域服务功能链编排成本优化

网络功能虚拟化技术的兴起使得实例化为服务功能链(SFC)的网络服务能够共享基底网络,缓解了传统网络体系结构僵化的问题.然而,网络中大量服务请求给多域SFC编排带来了新的挑战.首先由于域内网络资源信息及内部策略的保密性,使得多域SFC的编排更为复杂.其次多域SFC编排要确定最佳候选编排域集,先前的研究较少考虑域间负载的均衡性,对服务接受率造成了消极影响.此外跨网络域编排服务请求对服务的成本和响应时间提出了更严格的要求.为解决上述挑战,在本文中,我们首先针对多域网络隐私性需求,提出了域级图的构造方法;然后基于域间负载均衡提出了域权重的计算方法进行SFC编排域的选择;最后,针对多域网络成本和响应时间需求,提出编排算法.实验结果表明,提出的算法有效地权衡了平均服务成本和接受率,并且在服务平均响应时间方面也得到了优化.

功能主义视角下NFT虚拟财产保护

NFT因其存于网络数字空间,具虚拟性、可支配性、排他性等属性,应被认定为一种新形态的网络虚拟财产。基于学理视角下的主观权利论与现实环境下的租值耗散因素等考虑,理应适用虚拟财产理论中的权利范式,但由于现阶段虚拟财产规制并无明确法律依据,且权利范式存在客体依赖等困境,故对于NFT虚拟财产保护的研究应放弃路径依赖,以功能主义范式弥合形式主义范式带来的固有困境。通过目的分析与价值衡量等手段,可将NFT财产权利转化为法定财产权利边界的解释问题,以回应对NFT虚拟财产权利保护的需要。

DTN中基于虚拟货币交易的高效率路由算法

由于容迟网络的间歇性连接等特点,以及节点自身缓存、能量等资源受限,DTN中的节点往往会表现出一定的自私性。自私节点的存在可能会提高网络的开销,降低消息的成功投递率。为了促进自私节点参与合作,提出了一种基于虚拟货币交易的高效率路由算法PVCT(Efficient Routing Algorithm Based on Virtual Currency Transaction in DTN),并结合容迟网络的小世界特性,提高了路由算法的效率。该算法利用虚拟货币交易的方式,并根据节点的基本属性、位置属性、社会属性等进行定价,节点根据设计的价格函数给出对应的报价,并利用价格函数合理地分配消息副本数。在PVCT策略中,节点根据判断情况分为正常节点和自私节点,当消息的跳数小于或等于两跳时,按照概率路由的策略进行转发;反之,当消息的跳数大于两跳时,若遇到的为自私节点,则执行虚拟货币交易的路由算法。携带消息节点的出价若高于转发节点的价格,则进行交易,更新各自的收益状态;否则,进入二次价格调整阶段以协调节点双方之前的虚拟报价。仿真实验表明,PVCT路由算法在DTN中能更好地促进消息的转发,从而提升网络的整体性能。

可再生能源高渗透下时滞孤岛微电网的负荷频率控制

针对孤岛微电网系统中大规模接入可再生能源导致的低惯性和开放通信网络中通信延迟对系统稳定运行造成的不可忽略的影响,该文研究变时滞影响下低惯性孤岛微电网系统的负荷频率控制问题。首先,提出一种引入虚拟惯性控制的时滞微电网分层频率控制模型;然后构造一个新的Lyapunov-Krasovskii泛函,它包括时滞乘积项和增广的s-dependent积分项。进而,选取复合松弛积分不等式、二次函数不等式和凸组合等方法与所构造泛函有效配合,得到保守性更低的稳定性判据。最后,通过不同场景的仿真分析验证所提方法在提高孤岛微电网时滞稳定裕度和系统动态性能方面具有更好的效果,并分析了时滞稳定裕度与控制器参数、非线性负载扰动之间的关系。