面向成本优化的SBS虚拟化资源分配

在部署云应用问题中,对于结构复杂的基于SBS的资源优化分配问题,目前尚缺少深入研究.针对这一问题,提出了组件服务资源配置的概念及其确定方法,基于此将SBS的资源优化分配建模为资源配置的组合优化.为求解优化模型,给出了一种改进了交叉算子和变异算子的遗传算法.实验验证了优化模型的有效性,同时表明提出的遗传算法具有较快的收敛速度,且与线性规划相比,虽然最优解的质量相近,但是在较大规模问题上求解效率明显优于后者.     

云计算环境中优化遗传算法的资源调度策略

资源调度是云计算的核心问题,传统遗传算法(GA)、Sufferage算法等都可以用于云计算环境中的资源调度,但传统遗传算法存在收敛慢、易早熟等缺点,Sufferage算法则不适用于多聚类环境的密集型任务调度.本文在充分考虑云计算环境的动态异构性和大规模任务处理特性的基础上,提出了一种基于染色体编码方式和适应度函数的改进遗传算法(IGA),并在云仿真器CloudSim上对3种算法进行了仿真.仿真结果表明,该算法在性能和服务质量QoS(Qualityof Service)方面都优于传统遗传算法和Sufferage,能更好地适用于大规模任务下的云计算环境资源调度.     

用遗传算法优化WCDMA网络QoS

3G蜂窝移动通信系统旨在提供多种业务,比如语音业务、数据业务以及基于分组交换网的多媒体业务。为了提供这些业务的服务质量保证,提出了一种优化WCDMA(W ideband Code-D ivision Mu ltip le Access)网络QoS(Quality of Service)的遗传算法(GA:Genetic A lgorithm)。使用该算法,研究了如何实时对WCDMA网络参数进行最优配置,以最大限度地提高使用不同输入通信流量混叠业务用户的满意度。仿真实验结果表明,该算法可以根据混叠输入通信流量动态有效地配置网络参数,以使服务的性能达到最优化:使用GA后,网络的频谱利用率达到18%;GA的收敛速度相当好,例如在假定所有的通信量混和的情况下,经过20~30次的反复后,就可以达到最优的配置参数。     

多业务正交频分复用多址系统中的机会公平调度算法

研究了宽带正交频分复用多址(OFDMA)系统中存在多种不同业务时的无线资源管理问题,给出了多业务情况下基于效用公平的公平调度算法.仿真结果表明,基于效用公平的算法,可以保证各用户间的吞吐量符合比例公平原则,并能明显增加系统总吞吐量.     

流媒体网络教学平台构建关键技术

针对流媒体如语音、视频等在现代网络教育领域中的应用进行研究。首先阐述了流媒体在课堂教学中应用的重要意义,然后讨论了基于流媒体技术的教学平台实现所涉及的3个关键问题,包括宽带无线流媒体接入技术,流媒体对网络服务质量的要求,双向交互多媒体教学对分布式流媒体系统的性能要求。     

电商直播中语言风格对销售效果的影响基于SVM模型

伴随着科技的发展和自2019年以来新型冠状病毒疫情的催化作用，直播逐渐成为一种主流的营销途径之一。然而，对于不同的主播来说，直播间商品的销售情况参差不齐。本研究关注主播的语言风格如何影响直播中不同类别商品的销售。结合现有的理论和分析，本研究搭建了主播的语言风格框架，并运用机器学习模型训练得到一个语言风格分类器。结果显示，相对于享乐品，以商品信息为中心的语言风格对实用品的销售促进作用更大；相反，相对于实用品，以外围信息为中心的语言风格对享乐品的销售促进作用更大。     

Wide Area Analytics for Geographically Distributed Datacenters

Big data analytics,the process of organizing and analyzing data to get useful information,is one of the primary uses of cloud services today.Traditionally,collections of data are stored and processed in a single datacenter.As the volume of data grows at a tremendous rate,it is less efficient for only one datacenter to handle such large volumes of data from a performance point of view.Large cloud service providers are deploying datacenters geographically around the world for better performance and availability.A widely used approach for analytics of geo-distributed data is the centralized approach,which aggregates all the raw data from local datacenters to a central datacenter.However,it has been observed that this approach consumes a significant amount of bandwidth,leading to worse performance.A number of mechanisms have been proposed to achieve optimal performance when data analytics are performed over geo-distributed datacenters.In this paper,we present a survey on the representative mechanisms proposed in the literature for wide area analytics.We discuss basic ideas,present proposed architectures and mechanisms,and discuss several examples to illustrate existing work.We point out the limitations of these mechanisms,give comparisons,and conclude with our thoughts on future research directions.     

基于交叉熵的多约束QoS组播路由蚂蚁算法

基于多媒体传输的网络应用均对服务质量(quality of service,QoS)有迫切的需求,如带宽、延迟、丢包率等。寻找满足上述QoS要求的组播树问题是一个NP-完全问题。在已有的多约束QoS组播路由算法中主要是局部搜索算法或集中式搜索算法,这些均不能保证在实际网络中找到全局解。本文提出用基于交叉熵的蚁群优化算法来求解多约束QoS组播路由问题,NS2仿真结果表明该算法能够快速找到近似最优解。     

流媒体技术在网络教学中的应用

计算机网络技术的发展给基于Internet/Intranet的多媒体应用开发创造了良好的契机和发展空间。更多的基于网络技术开发的多媒体应用如:多媒体网络教学、网络会议、网络可视电话、视频点播等等开始大量涌现,形成了百花争艳的景象。介绍了目前较为流行的流媒体技术的概述、特点、实现并解决方案以及流媒体在网络教学中的实际应用等,使人们能够对流媒体技术有了进一步的了解和认识,并对流媒体技术的发展前景做出了展望。     

用于多媒体的最早截止时间优先算法改进

对经典实时调度算法之一的最早截止时间优先算法进行研究,提出一种基于动态死线时间的改进方法,避免了瞬时过载导致的系统不可预测的行为.实验表明,本方法保证了连续多媒体应用的服务质量,达到较高的资源利用率,使该算法更加适合多媒体应用环境     

Concurrent and Storage-Aware Data Streaming for Data Processing Workflows in Grid Environments

Data streaming applications, usually composed of sequential/parallel data processing tasks organized as a workflow, bring new challenges to workflow scheduling and resource allocation in grid environments. Due to the high volumes of data and relatively limited storage capability, resource allocation and data streaming have to be storage aware. Also to improve system performance, the data streaming and processing have to be concurrent. This study used a genetic algorithm (GA) for workflow scheduling, using on-line measurements and predictions with gray model (GM). On-demand data streaming is used to avoid data overflow through repertory strategies. Tests show that tasks with on-demand data streaming must be balanced to improve overall performance, to avoid system bottlenecks and backlogs of intermediate data, and to increase data throughput for the data processing workflows as a whole.     

基于流媒体技术的校园网直播系统的设计与实现

随着网络技术的飞速发展,校园网具备了高速的数据传送能力。将流媒体技术引入校园网,在校园网内建设网络电视应用平台,为广大师生提供视频直播服务成了各院校的普遍要求。本文简要介绍了流媒体技术的原理,并提出了一种基于流媒体技术的校园网直播系统的完整的解决方案。     

基于QoS约束的计算能力调度算法研究

云计算具有很强的商业性特点,以为用户提供高质量的服务为目标。针对云计算对服务质量QoS的需求问题以及云计算原有计算能力调度算法没有考虑用户多样性的缺点,提出了基于QoS约束的计算能力调度算法。该算法可以在保证为用户提供模拟的独立计算能力基础上,根据QoS参数生成的向量进行资源与任务的匹配,区分用户的不同服务质量需求,为用户提供符合其需求的资源。     

Efficient Location-Aware Data Placement for Data-Intensive Applications in Geo-distributed Scientific Data Centers

Recent developments in cloud computing and big data have spurred the emergence of data-intensive applications for which massive scientific datasets are stored in globally distributed scientific data centers that have a high frequency of data access by scientists worldwide. Multiple associated data items distributed in different scientific data centers may be requested for one data processing task, and data placement decisions must respect the storage capacity limits of the scientific data centers. Therefore, the optimization of data access cost in the placement of data items in globally distributed scientific data centers has become an increasingly important goal.Existing data placement approaches for geo-distributed data items are insufficient because they either cannot cope with the cost incurred by the associated data access, or they overlook storage capacity limitations, which are a very practical constraint of scientific data centers. In this paper, inspired by applications in the field of high energy physics, we propose an integer-programming-based data placement model that addresses the above challenges as a Non-deterministic Polynomial-time(NP)-hard problem. In addition we use a Lagrangian relaxation based heuristics algorithm to obtain ideal data placement solutions. Our simulation results demonstrate that our algorithm is effective and significantly reduces overall data access cost.     

Efficient Multi-Tenant Virtual Machine Allocation in Cloud Data Centers

Virtual Machine(VM) allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and the network distance between a tenant's VMs may considerably impact the tenant's Quality of Service(Qo S). In this study, we define and formulate the multi-tenant VM allocation problem in cloud data centers, considering the VM requirements of different tenants, and introducing the allocation goal of minimizing the sum of the VMs' network diameters of all tenants. Then, we propose a Layered Progressive resource allocation algorithm for multi-tenant cloud data centers based on the Multiple Knapsack Problem(LP-MKP). The LP-MKP algorithm uses a multi-stage layered progressive method for multi-tenant VM allocation and efficiently handles unprocessed tenants at each stage. This reduces resource fragmentation in cloud data centers, decreases the differences in the Qo S among tenants, and improves tenants' overall Qo S in cloud data centers. We perform experiments to evaluate the LP-MKP algorithm and demonstrate that it can provide significant gains over other allocation algorithms.     

无线多跳网络中SVC视频流传输的分布式优化方法

针对无线多跳网络承载多媒体服务所面临的可扩展性、能量有效性和服务质量保证的挑战,研究了多个可伸缩编码（scalable video coding,SVC）视频流在无线多跳网络中传输的分布式跨层优化问题.在分析网络资源分配约束和SVC视频内容自适应模型的基础上,把SVC视频的传输问题表达为一个视频质量和网络功率消耗的跨层权衡优化问题.基于对偶问题的可分解性和次梯度求解方法,提出了一个可分布式实现的SVC视频优化传输算法.该分布式算法避免了控制开销的全网传播,通过节点的局部信息交换和独立解决相应子问题,达到底层资源分配和上层SVC视频传输内容选择的最优匹配.仿真结果表明该算法可以快速收敛到最优解,并能实现SVC视频的平均传输质量和网络总功率消耗的最优权衡.     

一种LTE系统下的基于MBMS的视频流资源分配方案

多媒体广播多播业务（MBMS）为点对多点的多媒体传输提供了有效的服务.但MBMS需要上行反馈信道状况,因此其性能受限于最差用户的信道质量.文中提出了一种LTE系统下基于MBMS传输的视频流频域分组调度方案,该方案将视频流分为基层和扩展层,在点对多点的传输方式下,根据信道质量反馈针对不同视频层采用不同的调度方法,在所有用户都得到服务的同时增加了吞吐量.理论和仿真分析表明,与已有的传统调度方案相比,该方案能够提供更高的系统吞吐量和频谱效率.     

ASIP Approach for Multimedia Applications Based on a Scalable VLIW DSP Architecture

The rapid development of multimedia techniques has increased the demands on multimedia processors.This paper presents a new design method to quickly design high performance processors for new multimedia applications.In this approach,a configurable processor based on the very long instruction-set word architecture is used as the basic core for designers to easily configure new processor cores for multimedia algorithm.Specific instructions designed for multimedia applications efficiently improve the performance of the target processor.Functions not implemented in the digital signal processor (DSP) core can be easily integrated into the target processor as user-defined hardware to increase the performance.Several examples are given based on the architecture.The results show that the processor performance is enhanced approximately 4 times on the H.263 codec and that the processor outperforms both DSPs and single instruction multiple data (SIMD) multimedia extension architectures by up to 8 times when computing the 2-D-IDCT.     

SaaS应用中基于SLA的资源分配研究

随着云计算的普及,软件即服务(Software as a Service,SaaS)得到了广泛的关注.SaaS应用中,需要满足客户服务等级协议(Service Level Agreement,SLA)要求、减少SLA服务违例的同时,尽量提高系统资源的利用率,这对资源分配策略提出了挑战.提出了SaaS模式下的基于SLA的资源分配策略:基于对客户请求动态变化的分析,针对"首次租用服务"和"租用服务升级"两种请求类型,提出了一种用户请求与底层资源的映射策略;提出了提高虚拟机重用度的基于SLA的资源效率优化分配算法.实验表明,该资源分配算法可以在有效保证服务质量的同时,提高资源的利用率.     

WFQ与WRR调度算法的性能分析与改进

不同类别的业务对时延及时延抖动的要求是不同的,为了满足不同业务的服务质量(QoS),选择一种合适的队列调度算法至关重要. 研究了分组长度对WRR及WFQ算法公平性以及时延性能的影响,并提出了一种基于分组长度及队列权重的改进型WRR算法——enhanced-WRR. 仿真结果表明,当分组长度相同时,WRR与WFQ的时延性能几乎一致;当分组长度不同时,WRR算法不能保证高优先级队列的时延要求,而E-WRR算法的时延性能逼近WFQ算法,能很好地保证高优先级队列的时延要求,并且极大降低了算法复杂度.