基于SDN的卫星网络端到端QoS类映射研究

针对在多个卫星网络域环境下域间协作困难,以及如何在各个卫星网络域内选择合适的QoS类实现端到端QoS类映射的问题,基于软件定义卫星网络架构,首先,提出一种SDN卫星网络域间通信方案,以及面向多QoS类的端到端服务交付数据包处理方法。其次,提出一种基于多准则决策的控制模块。模块利用ELECTRE方法,在ELECTRE方法中通过综合权重法计算出各个卫星网络域内QoS指标相应的权重系数,并且利用ELECTRE方法对每个QoS类的净支配指数进行排序,从而为端到端路径的每个卫星网络域选择合适的QoS类。最后,与现有端到端QoS类映射方案进行比较,仿真结果表明,上述方法在多卫星网络域环境下保证端到端的时延、时延抖动、丢包率和代价。

一种可扩展的Web Service QoS管理框架

Web Service是目前研究界和产业界广泛关注的技术之一.随着Web Service的广泛应用,研究者们普遍认识到,服务的非功能属性,即服务质量(Quality of Service,QoS)是面向服务的应用能否成功的关键因素之一.因此,研究者们尝试从多个角度对QoS相关问题展开了研究.然而,现有工作普遍关注基于QoS的动态服务选择和组装等上层应用技术,而对于如何获取、存储、度量QoS等基础支持技术研究较少,而这些基础性工作对QoS相关的研究工作具有显著的重要性.此外,不同应用领域对Web Service QoS的需求不尽相同,因此,需要有一套灵活的机制支持在QoS模型定义、QoS度量方法、QoS信息采集等方面体现出的领域特性.针对这个问题,文中提出了一个可扩展的Web Service QoS信息管理框架,详细分析了该框架涉及到的重要方法与核心技术,并给出了该框架在北京大学软件构件库系统中的设计决策和方案.最后,介绍了文中框架在一个863计划项目中的应用实例,该实例展示了用户根据其应用的领域需求对本框架进行扩展并进行Web Service QoS管理的方法,从而验证了本管理框架的可扩展性及实用性.

基于时间序列分析的Web Service QoS预测方法

通过网络提供服务的Web Service的服务质量会随着网络环境、服务器负载等因素的变化而变化,如何更好地帮助用户选择在未来一段时间内符合服务质量需求的Web Service,是目前服务计算领域中需要解决的关键问题之一。针对上述问题,提出了一种基于时间序列分析的Web Service QoS预测方法,并实现了相应的Web Service QoS自动预测工具。该工具能够根据Web Service的历史QoS数据,有效地预测未来短期内的QoS信息。以17832个Web Service的历史数据为基础,设计了相关实验,并验证了方法的有效性。

QoS驱动的Web services动态计价机制研究

结合Web services的服务质量(QoS)因素和用户经验,对Web services的计价机制进行研究,提出一种QoS驱动的Web services动态计价模型,描述其计价过程,并给出相应的实现算法。通过对模型的简单实现,经测试这一计价模型在准确性和合理性上具有一定优势。

悬停波动对毫米波无人机通信系统QoS的影响研究

毫米波无人机通信是未来通信中一种很有前途的候选者。然而,悬停无人机的随机波动会影响该通信系统的服务质量,并给毫米波无人机通信的应用带来挑战。该文从无人机位置波动对无人机-用户链路质量的影响的角度出发,研究无人机位置波动对空对地毫米波无人机通信系统QoS的影响。以可靠服务概率及覆盖概率作为评估系统QoS的指标,基于随机几何获得QoS评估指标的表达式并通过蒙特卡洛仿真进行验证。结果显示,QoS随无人机波动强度的增加而降低且存在使QoS最大的最优无人机高度,为毫米波无人机通信网的设计与研究提供参考依据。

数字电视信号在IP网络传输中的QoS保障技术应用

数字电视信号对传输质量有着十分严苛的要求,传统网际互连协议(Internet Protocol,IP)网络在提供端到端服务质量(Quality of Service,QoS)保障方面仍面临诸多挑战。对此,分析数字电视信号的业务特点和传输需求,梳理IP网络QoS领域的发展脉络,提出一种端到端QoS保障解决方案的总体架构,从接入网、骨干网、综合管理平台三个层面给出优化措施。

Toward Cloud Computing QoS Architecture:Analysis of Cloud Systems and Cloud Services

Cloud can be defined as a new computing paradigm that provides scalable, on-demand, and virtualized resources for users. In this style of computing, users can access a shared pool of computing resources which are provisioned with minimal management efforts of users. Yet there are some obstacles and concerns about the use of clouds. Guaranteeing quality of service(Qo S) by service providers can be regarded as one of the main concerns for companies tending to use it. Service provisioning in clouds is based on service level agreements representing a contract negotiated between users and providers. According to this contract, if a provider cannot satisfy its agreed application requirements, it should pay penalties as compensation. In this paper, we intend to carry out a comprehensive survey on the models proposed in literature with respect to the implementation principles to address the QoS guarantee issue.

A QoS-aware Method for Web Services Discovery

The non-functional QoS (quality of service) information helps us to select a proper Web-service from the web applications, by using component services such as UDDI[1](Universal Description, Discovery, and Integration) and MDS(Monitoring and Discovery System). MDS is a suite of web services to monitor and discover resources and service on Grids, but MDS only based on function aspects. This paper studies on an approach to provide the QoS information and a discovery model by using MDS and gives a system deployment and implementation plan. The simulation results show that the method is effective in service discovery.

Service allocation based on QoS evaluation in military organization cloud cooperation

The problem of cloud cooperation of military service providers（MSPs） is addressed for allocating limited resources to military service users（MSUs） that are geographically distributed. The MSPs, also called military organization clouds, are virtualized and encapsulated by the services they can offer and each of them contains different kinds of resources that MSU needs. The MSPs are also geographically dispersed. They are required to allocate their resources to the MSU complying with the corresponding quality of service（QoS）, so that each MSU gathers the services it needs to guarantee its task to be implemented. The outline of military organization cloud cooperation is discussed and the method of service optimal selection is proposed based on QoS evaluation. The QoS evaluation method based on exponential approximation is put forward to include the users＇ will. Simulation results verify the effectiveness of the proposed algorithm.

Interactive Web service choice-making based on extended QoS model

Quality of Service (QoS) is a key factor in Web service advertising, choosing and runtime monitoring. Web service QoS is multi-faceted, fuzzy and dynamic. Current researches focus on implementation level performance assurance, ignoring domain specific or application level metrics which are also very important to service users. Industry Web service standards lack QoS expression. The support for QoS based service choice-making is very limited. We proposed an extended Web service QoS model based on configurable fuzzy synthetic evaluation system. Web service QoS is evaluated dynamically according to the service context. A QoS requirement description model is also given for service QoS requirement definition. An interactive Web service choice-making process is described, which takes QoS as a key factor when choosing from functionally equivalent services.

An approach for QoS measurement and web service selection sureness

Web services have gained popularity in recent years and provide a new model of web,which facilitates interaction of scientific and business applications through the Internet.More often,several services with similar functionality are available from a large and changing number of service providers.Quality of Service(QoS)is the dominant factor in service selection and is of great importance to users.In this paper,we propose a model for QoS measurement and web services selection.The model consists of QoS model,QoS monitoring,QoS comparison and service selection with a QoS feedback mechanism.The most suitable service is to take into account the agreed QoS,monitoring is done during invocation phase and if any deviation is recorded,next suitable service is selected.Finally the model is proved to be feasible and effective by simulation experiments.

Constraint-based QoS-aware Web Service Composition in Cross-organizational Collaboration

To improve the Quality of Service (QoS)-aware Web service compositions considering constraints between cross-organizational business,this paper analyzes the types of constraints,and accordingly proposes a Chaos Genetic Algorithm (CGA). The algorithm creates an initial population of service compositions based on the chaos theory,and then processes individuals which violate constraints in the initial population using repair strategies. Next,a new fitness function is designed to gradually eliminate the infeasible compositions generated in evolution. Finally,the algorithm makes minor chaotic disturbances on the evolved group to accelerate convergence and avoid local optimum. Experimental results demonstrate the effectiveness of the proposed algorithm.

基于服务负载的时序QoS预测

网络技术的发展和多接入边缘计算的兴起使得计算和网络资源的部署逐渐靠近终端.随着服务数量的增多,为了向用户更好地推荐服务,如何在复杂、动态的边缘计算环境中实时、准确地预测服务质量(quality of service,QoS)成为一项挑战.本文提出一种基于服务负载实时预测QoS的深度神经模型(QPSL),它可以为边缘计算中的QoS预测提供缺少的负载状况感知和周期感知.首先,对服务的负载状况进行特征表示,并通过时序分解模块获取时序特征.其次,将CNN和BiLSTM结合,学习潜在的时序关系,生成不同时刻的状态向量.然后,基于Attention机制为历史时刻的状态向量分配权重,从而构造未来时刻的状态向量.最后,将上下文嵌入向量与状态向量送入感知层完成实时QoS预测.基于真实的融合数据集进行了大量的实验,结果表明QPSL在响应时间和吞吐量任务上的MAE分别平均提升了10.28%和10.87%,优于现有的时间感知QoS预测方法.

突发模型下的云服务QoS网络质量保障方法

为研究突发事件下网络的服务质量性能保障措施,重点关注高峰期的QoS网络质量,考虑云计算的技术特征和异构网络服务环境,通过对服务交付系统的服务能力优化,减少大量的网络流量对服务器的阻塞,提出一种突发模型下的云服务QoS保障方法.首先研究了云服务突发流量机制,并对限制流量优化模型性能分析.然后,基于网络演算理论与网络服务融合模型的服务速率和负载进行分析,提出限制突发模型的线上教学服务质量的模型,给出了时延上界的定量解析式为流量优化控制措施提供量化依据.最后通过仿真实验分析了服务速率和负载与时延上界的关系,验证了流量限制突发模型的有效性和可靠性.

BFS-SVM Classifier for QoS and Resource Allocation in Cloud Environment

In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocation in order tomeet the Quality of Service(QoS)requirements of users.For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work.The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection(BFS)in the proposed work,this further reduces the inappropriate features from the data.The similarities that were hidden can be demoralized by the Support Vector Machine(SVM)classifier which is also determine the subspace vector and then a new feature vector can be predicted by using SVM.For an unexpected circumstance SVM model can make a resource allocation decision.The efficiency of proposed SVM classifier of resource allocation can be highlighted by using a singlecell multiuser massive Multiple-Input Multiple Output(MIMO)system,with beam allocation problem as an example.The proposed resource allocation based on SVM performs efficiently than the existing conventional methods;this has been proven by analysing its results.

电力通信网络5G业务承载的QoS优化

探讨了电力通信网络中的服务质量(Quality of Service,QoS)优化策略,以提高电力系统的效率和可靠性。主要分析了高带宽需求和多样化业务对网络性能的挑战,强调了高带宽、低延迟和高可用性的重要性;探讨了QoS优化技术在电力通信网络中的应用,包括承载关键命令调度业务和支撑视频监控需求;同时,论述了业务识别与分级传输、多路径负载均衡、智能调度与优先控制等策略的重要性。电力通信网络通过智能数据管理和控制,为电力系统的监视、控制和安全运行提供了技术支持。

Scheduling Algorithm with Quality of Service Support in IEEE 802.16 Networks

The MAC layer in IEEE802.16 is designed to differentiate service among traffic categories with different multimedia requirements.In this paper,a scheduling algorithm at MAC layer for multiple connections with diverse QoS requirements is proposed.As for this algorithm,each connection is assigned a priority,which is updated dynamically based on its service status concluding queue characteristic and channel state.A connection with the highest priority is scheduled each time.Analytical model is developed by assuming a Finite State Markov Chain(FSMC)channel model.Simulation results show that the proposed scheduling algorithm can improve the performance of mean waiting time and throughput in broadband wireless networks.

基于QoS的网格资源管理

资源管理是网格计算研究领域中的一项重要研究点,目前的研究工作大多致力于解决异构性问题,对于在交付无缝QoS(quality of service)前提下提高资源分配性能方面还缺乏深入的研究.而且,目前对网格QoS的研究集中于将多媒体网络QoS的相关成果融入网格体系架构中以提供对网格QoS的支持,而缺少对网格QoS的特性进行系统化的研究与归纳.为此,提出了网格QoS的层次结构模型,并对其中承上启下的虚拟组织层QoS参数进行了新的分类和测量;然后,利用SNAP(service negotiation and acquisition protocol)协议对基于网格QoS层次结构模型的网格QoS参数的映射转换过程进行了分析;最后,设计了网格资源管理仿真系统,并运用相关的网格QoS的研究改进了现有的Min-Min算法.仿真实验结果表明,基于QoS的网格资源管理可以在满足用户QoS需求下,有效地提高网格资源的利用率和服务请求的成功率.

一种Web Service的服务质量预测方法

服务消费者在选择服务之前,通常需要基于其他消费者的经验对未使用过的服务的质量进行预测.考虑到不同服务消费者对同一服务的服务质量的感受之间可能存在较大的差别,提出了一种QoS(quality of service)预测方法.该方法以消费者的历史经验为基础,计算消费者之间以及服务之间的相似程度,并以此相似度为基础对消费者并未使用过的服务的QoS进行预测.实验结果表明,这种方法可以显著提高Web服务质量预测的准确性.

Web QoS控制研究综述

Internet的服务模式正由传统的数据通信与信息浏览向电子交易与服务转变 ,Web服务器系统作为支持电子商务的核心设施 ,需要对不同的用户或HTTP请求提供区分服务和性能保证 ,因此必须具备建立和支持服务质量 (QualityofService ,QoS)控制的机制与策略 .该文综述了WebQoS控制的研究现状 ,包括Web请求的分类机制、Web服务器应用软件的QoS控制机制、操作系统的WebQoS控制机制、中间件的WebQoS控制机制以及Web服务器集群系统的QoS控制机制与策略 ,并分析了进一步的研究方向 .