Video placement and configuration of distributed video servers on cable TV networks

A large-scale, distributed video-on-demand (VOD) system allows geographically dispersed residential and business users to access video services, such as movies and other multimedia programs or documents on demand from video servers on a high-speed network. In this paper, we first demonstrate through analysis and simulation the need for a hierarchical architecture for the VOD distribution network.We then assume a hierarchical architecture, which fits the existing tree topology used in today's cable TV (CATV) hybrid fiber/coaxial (HFC) distribution networks. We develop a model for the video program placement, configuration, and performance evaluation of such systems. Our approach takes into account the user behavior, the fact that the user requests are transmitted over a shared channel before reaching the video server containing the requested program, the fact that the input/output (I/O) capacity of the video servers is the costlier resource, and finally the communication cost. In addition, our model employs batching of user requests at the video servers. We study the effect of batching on the performance of the video servers and on the quality of service (QoS) delivered to the user, and we contribute dynamic batching policies which improve server utilization, user QoS, and lower the servers' cost. The evaluation is based on an extensive analytical and simulation study.     

一个网络文件存储系统TNS关键技术

基于多服务器架构、为多用户服务的网络文件存储系统普遍存在资源分配不均,重复文件多,存储空间浪费严重的问题。设计并实现了TNS网络文件存储系统,该系统基于多服务器存储架构,分别由用户服务器、索引服务器、数据服务器、共享服务器、管理服务器和登录服务器组成,为多用户服务,采用一致性Hash实现负载均衡,支持在客户端进行文件粒度的重复数据删除。经过实际生产环境运行测试,具有良好的负载均衡能力和重复数据删除功能,可以有效节省存储空间,提高存储设备利用率。     

企业资源综合管理系统的研究设计与实现

企业根据估茸的用户访问量购置集群服等器,部署应用提供网络服务。然而访问量的不确定性和现有的资源调度机制的局限性,使现盲资源管理系统既面临当访问量和负载激增时,服务器处于满负荷运行状态导致服务质量下降的问题,又面临用户量下降,大量资源利用低下的问题。本文中,设计了一个企业资源管理系统,并实现了动态的资源调度算法。该系统能够综合利用计算能力、存储能力都达到中小型服务器水平的普通计算机,作为后备资源,缓解大规模并发的用户请求带给服务器的压力,从而提高整个系统的性能、吞吐量和资源利用率,并优化物理资源的负载均衡。     

Performance analysis of a pull-based parallel video server

In conventional video-on-demand systems, video data are stored in a video server for delivery to multiple receivers over a communications network. The video server's hardware limits the maximum storage capacity as well as the maximum number of video sessions that can concurrently be delivered. Clearly, these limits will eventually be exceeded by the growing need for better video quality and larger user population. This paper studies a parallel video server architecture that exploits server parallelism to achieve incremental scalability. First, unlike data partition and replication, the architecture employs data striping at the server level to achieve fine-grain load balancing across multiple servers. Second, a client-pull service model is employed to eliminate the need for interserver synchronization. Third, an admission-scheduling algorithm is proposed to further control the instantaneous load at each server so that linear scalability can be achieved. This paper analyzes the performance of the architecture by deriving bounds for server service delay, client buffer requirement, prefetch delay, and scheduling delay. These performance metrics and design tradeoffs are further evaluated using numerical examples. Our results show that the proposed parallel video server architecture can be linearly scaled up to more concurrent users simply by adding more servers and redistributing the video data among the servers     

Optimal server allocations for streaming multimedia applications on the Internet

In this paper, we address the server selection problem for streaming applications on the Internet. The architecture we consider is similar to the content distribution networks consisting of geographically dispersed servers and user populations over an interconnected set of metropolitan areas. Server selection issues for Web-based applications in such an environment have been widely addressed; the selection is mostly based on proximity measured using packet delay. Such a greedy or heuristic approach to server selection will not address the capacity planning problem evident in multimedia applications. For such applications, admission control becomes an essential part of their design to maintain Quality of Service (QoS). Our objective in providing a solution to the server selection problem is threefold: first, to direct clients to the nearest server; second, to provide multiple sources to diffuse network load; third, to match server capacity to user demand so that optimal blocking performance can be expected. We accomplish all three objectives by using a special type of Linear Programming (LP) formulation called the Transportation Problem (TP). The objective function in the TP is to minimize the cost of serving a video request from user population x using server y as measured by network distance. The optimal allocation between servers and user populations from TP results in server clusters, the aggregated capacity of each cluster designed to meet the demands of its designated user population. Within a server cluster, we propose streaming protocols for request handling that will result in a balanced load. We implement threshold-based admission control in individual servers within a cluster to enforce the fair share of the server resource to its designated user population. The blocking performance is used as a trigger to find new optimal allocations when blocking rates become unacceptable due to change in user demands. We substantiate the analytical model with an extensive simulation for analyzing the performance of the proposed clustered architecture and the protocols. The simulation results show significant difference in overall blocking performance between optimal and suboptimal allocations in as much as 15% at moderate to high workloads. We also show that the proposed cluster protocols result in lower packet loss and latencies by forcing path diversity from multiple sources for request delivery.     

基于云平台的高并发WebGIS服务

随着Internet技术的发展,传统的WebGIS服务器在面对大用户群高并发访问时会出现服务延迟甚至拒绝服务的现象.针对该问题,本文提出了一种基于云平台的应对高并发的WebGIS服务器架构.架构使用云平台为WebGIS服务器提供弹性计算和存储资源,并从负载均衡、缓存设计、数据库集群三方面缓解高并发瓶颈.选择开源服务器端软件GeoServer作为WebGIS应用实验部署.实验数据表明使用缓存机制明显降低了WebGIS服务响应时间.与单物理服务器相比,云平台WebGIS服务器集群架构能够有效处理高并发请求,且随着集群规模扩展,云平台WebGIS系统能够获得良好的加速比.     

移动网格中负载自平衡的集群式边缘流媒体服务器

迅速发展的3G网络和覆盖范围可达整个城域的Wi-Fi网络的逐渐普及,使移动网络环境中对流媒体服务的需求迅速增长.由于移动用户主要依赖Internet上的流媒体服务资源提供服务,所以位于无线网络和Internet交界处的边缘流媒体服务器可以充当桥梁和缓冲区,对于降低网络负载和提高服务质量有显著的效果,因而提出一种新的集群架构的边缘流媒体服务器(CESS)的设计,而且针对集群服务器中最重要的负载均衡问题做了分析和测试,提出了一种新的MCLBS缓存替换算法来使CESS能够达到自适应的负载均衡.最后,实验测试和结果分析证明,相对于传统的缓冲替换算法,MCLBS算法更适合集群式的服务器体系结构,在同样的条件下,缓存命中率明显提高,大大降低远程服务器的带宽.     

共享式Web应用服务器集群的资源整合方法研究

PaaS(platform as a service)平台是一种重要的云计算服务类型,但传统的Web应用服务器集群管理方式无法为PaaS平台多承租和动态资源管理提供有效支持。针对上述问题,提出了一种基于进程隔离的共享式集群拓扑结构,支持多个Web应用服务器集群共享相同的物理服务器环境。同时给出了针对此共享集群的服务器整合方法,以满足租户应用的性能需求和提高服务器的有效资源使用率。实验证明,平台能够根据当前共享集群的状态选择适当数量的服务器,服务器整合方法有效可行。     

CBC: Caching for cloud-based VOD systems

Cloud-based video on demand (VOD) service is a promising next-generation media streaming service paradigm. Being a resource-intensive application, how to maximize resource utilization is a key issue of designing such an application. Due to the special cloud-based VOD system architecture consisting of cloud storage cluster and media server cluster, existing techniques such as traditional caching strategies are inappropriate to be adopted by a cloud-based VOD system directly in practice. Therefore, in this study, we have proposed a systemic caching scheme, which seamlessly integrates a caching algorithm and a cache deployment algorithm together to maximize the resources utilization of cloud-based VOD system. Firstly, we have proposed a cloud-based caching algorithm. The algorithm models the cloud-based VOD system as a multi-constraint optimization problem, so as to balance the resource utilization between cloud storage cluster and media server cluster. Secondly, we have proposed a cache deployment algorithm. The algorithm further manages the bandwidth and cache space resource utilization inside the media server cluster in a more fine-grained manner, and achieves load balancing performance. Our evaluation results show that the proposed scheme enhances the resource utilization of the cloud-based VOD system under resource-constrained situation, and cuts down the reject ratio of user requests.     

视频监控联网系统中云媒体服务器的架构研究与设计

在视频监控联网系统中,传统独立部署的媒体服务器存在因并发导致的I／O负载严重、网络带宽不足以及负载分配不均等诸多问题。云媒体服务器将整体的调度、统一的管理和性能的优化融为一体,可实现资源利用的最优化和性能提升的最大化。本文研究并设计云媒体服务器的整体架构,基于此架构对服务器集群中媒体服务器的负载评判方法进行了设计。实验结果表明,相对于传统媒体服务架构的视频监控系统,云媒体服务器可较好地实现对业务的负载均衡、容错切换等功能,可以应对媒体服务器中对视频源的突发高并发访问。     

Design of a large scale multimedia storage server

Large scale multimedia storage servers will be an integral part of the emerging distributed multimedia computing infrastructure. However, given the modest rate of improvements in storage transfer rates, designing servers that meet the demands of multimedia applications is a challenging task that needs significant architectural innovation.Our research project, called Massively-parallel And Real-time Storage (mars) architecture, is aimed at the design and prototype implementation of a large scale multimedia storage server. It uses some of the well-known techniques in parallel I/O, such as data striping and Redundant Arrays of Inexpensive Disks (raid) and an innovative atm based interconnect inside the server to achieve a scalable architecture that transparently connects storage devices to an atm-based broadband network. The atm interconnect within the server uses a custom asic called ATM Port Interconnect Controller (apic) currently being developed as a part of an arpa sponsored gigabit local atm testbed. Our architecture relies on innovative data striping and real-time scheduling to allow a large number of guaranteed concurrent accesses, and uses separation of metadata from real data to achieve a direct flow of the media streams between the storage devices and the network. This paper presents our system architecture; one that is scalable in terms of the number of supported users and the throughput.     

A novel resource allocation mechanism for live cloud-based video streaming service

With the recent emergence of cloud computing, growing numbers of clients are using online cloud services through the Internet such as video streaming service. The rent costs of cloud service providers increase when the resource utilizations of the cloud-servers are not well. Therefore, resource allocation is a crucial problem for cloud data centers. The resource allocation problem is an NP-hard problem. This paper proposes a novel cloud resource allocation mechanism based on a winning strategy for a Nim game. This mechanism offers all clients an effective number of running cloud servers, and allocates cloud resources rapidly and effectively by using a pre-pairing approach. The proposed mechanism does not require searching for remaining resources of the running cloud server; hence, it can reduce the time taken to arrange resources. The experimental results show that the proposed mechanism can improve utilization of cloud servers and reduce the rent costs of the cloud service providers. The proposed mechanism can reach the utilization of cloud servers by as much as 99.96 %. The proposed mechanism is approximately 9 % more efficient than the market-based grid resource allocation algorithm, and 19 % more efficient than the modified best fit decreasing algorithm.

     

一种基于分散协作的Web缓存集群体系结构

Web对象缓存技术是一种减少Web访问通信量和访问延迟的重要手段，该文通过分析现有的各种Web缓存系统，提出了一种基于分散协作的Web缓存集群体系结构。该体系结构克服了集中式系统需要额外配备一台管理服务器的缺陷，消除了管理服务器瓶颈失效造成系统瘫痪的危险，减少由于管理服务器带来的延迟；同时消除了分散系统的缓存不命中情况下的多级转发的延迟和缓存内容重叠，提高了资源利用率和系统效率，具有良好的可扩展性和健壮性。     

基于异构多核处理器的媒体资源服务器

随着彩铃、IVR等多媒体增值业务需求的增加,电信网络中媒体资源服务器的多媒体并发处理能力逐渐成为这些增值业务发展的主要制约瓶颈。该文在分析媒体资源服务器特性的基础上,提出一种基于异构多核微处理器架构的媒体资源服务器的解决方案,探讨媒体资源服务器在该多核架构下的设计及媒体处理的性能优化,并进行相关测试。仿真测试结果表明,该设计可以获得高于现有通用处理器平台下媒体资源服务器2倍~4倍的性能表现。     

基于期望预测价值的时移代理缓存替换算法

分析移动多媒体广播上时移业务的特点,描述了可预测用户行为的时移代理服务器资源调度策略,并提出一种基于期望预测价值的时移代理服务器缓存替换算法。仿真实验结果表明,该算法与传统的FIFO算法相比较,提高了缓存的预测命中率,减少了用户平均等待延迟。     

基于AHP的Web集群系统负载均衡算法

利用集群技术构建的服务器组在资源利用率上存在较大差异。为此,提出基于层次分析法(AHP)的集群系统负载均衡算法,建立判断矩阵,得到各项评估指标的单项和综合权重。调度器定时接收真实服务器上的4类参数：网络性能,服务器硬件,服务器软件和网络服务类型。根据调度器收到的每一个连接请求,采用动态反馈算法选择负载最小的服务器响应连接请求。实验结果表明,该算法能减少服务器平均响应时间,有效提高集群系统的响应率。     

移动边缘计算中资源受限的动态服务部署策略

移动边缘计算（MEC）的出现使移动用户能够以低延迟访问部署在边缘服务器上的服务。然而,MEC仍然存在各种挑战,尤其是服务部署问题。边缘服务器的数量和资源通常是有限的,只能部署数量有限的服务;此外,用户的移动性改变了不同服务在不同地区的流行度。在这种情况下,为动态请求部署合适的服务就成为一个关键问题。针对该问题,通过了解动态用户请求来部署适当的服务以最小化交互延迟,将服务部署问题表述为一个全局优化问题,并提出了一种基于集群划分的资源聚合算法,从而在计算、带宽等资源约束下初步部署合适的服务。此外,考虑动态用户请求对服务流行度及边缘服务器负载的影响,开发了动态调整算法来更新现有服务,以确保服务质量（QoS）始终满足用户期望。通过一系列仿真实验验证了所提出策略的性能。仿真结果表明,与现有基准算法相比,所提出的策略可以降低服务交互延迟并实现更稳定的负载均衡。     

LTE-A中基于准入控制的切换决策算法

在E-UTRAN架构下,提出一种基于目标小区准入控制的切换决策算法。通过eNB之间的X2接口来交互网络的负载信息、资源信息和服务速率等,以此获得对目标小区准入控制的预测。构建曼哈顿模型场景,仿真结果证明,该切换算法有较高的切换成功率和较小的切换时延;并可将用户终端切换到负载比较轻的小区,使相邻小区的负载得到均衡,提高了无线资源的利用率。     

LVS负载均衡调度算法的改进及实现

采用服务器集群架构提供服务成为互联网服务业中普遍采用的策略,目前使用得比较广泛的网络负载均衡集群是Linux虚拟服务器集群。优秀的负载均衡算法可以将用户请求合理地分配到集群中的真实服务器中．提高集群中服务器的利用率和集群系统的吞吐量。针对LVS默认算法WLC算法的不足之处,对WLC算法进行改进．设计并实现一种基于反馈式的负载均衡算法。实验证明,改进算法能够增大集群的吞吐量,提升集群的整体性能。     

Multimedia resource allocation strategy of wireless sensor networks using distributed heuristic algorithm in cloud computing environment

The virtualized resource allocation (mapping) algorithm is the core issue of network virtualization technology. Universal and excellent resource allocation algorithms not only provide efficient and reliable network resources sharing for systems and users, but also simplify the complexity of resource scheduling and management, improve the utilization of basic resources, balance network load and optimize network performance. Based on the application of wireless sensor network, this paper proposes a wireless sensor network architecture based on cloud computing. The WSN hardware resources are mapped into resources in cloud computing through virtualization technology, and the resource allocation strategy of the network architecture is proposed. The experiment evaluates the performance of the resource allocation strategy. The proposed heuristic algorithm is a distributed algorithm. The complexity of centralized algorithms is high, distributed algorithms can handle problems in parallel, and reduce the time required to get a good solution with limited traffic.