一种支持WebQoS的前置服务器解决方案

服务质量(Qos)描述了-个产品或服务满足消费者需求的能力.如何为不同类型的用户提供区分服务正在引起研究领域的关注.通过改进Web服务器应用软件来提供WebQoS支持是一种非常直观的Web QoS控制思路,但在实际应用中却存在着通用性和可扩展性等方面的局限.以此思路为出发点,将QoS控制部分功能提取出来,在一个前置服务器中去实现,该服务器置于客户端与Web服务器中间,主要负责完成Web请求的截取、QoS控制及请求转发.实验证明以引入前置服务器的方式实现Web请求的QoS控制,能够保证高级别用户请求的较高完成率以及较低响应延时,以低优先级请求的尽早拒绝防止了服务器过载,有效地实现web服务区分.     

分配器上支持QoS的设计与实现

为了让Web集群服务器支持QoS，在分配器上实现了一些QoS的机制，包括区分服务、性能隔离、服务器动态划分、接纳控制和内容自适应。对于高优先级的请求，系统确保其服务质量满足事先商定的服务质量协议；对于低优先级的请求，系统提供尽力而为的服务。尤其，当服务器重载时，分配器不是简单地靠丢弃请求，而是采用内容自适应机制来防止服务器过载。实际测试表明，系统达到了所有的设计要求。     

支持QoS确保的存储服务器服务资源分配算法

在电子商务、工业制造领域中,具有海量存储与高I/O吞吐能力的数据存储服务器得到了广泛应用。数据存储服务器提供QoS支持对应用需求而言很重要,但是实践中却很少有数据存储服务器提供了QoS支持。提出了一种支持服务质量区分并对高优先级用户提供服务质量保证的存储服务区服务资源分配算法,由于对高优先级用户请求与低优先级用户请求采用了可变服务质量区分因子,因而算法在提供服务优先级区分与确保高优先级请求服务质量的同时,减小了低优先级请求的请求丢弃率,同时最大化了服务资源利用率。     

异构Web集群中的比例伸展因子区分服务

Web集群服务器已被广泛用来提高Web服务器的性能。如何保证Web服务的服务质量（QoS）是一个迫切需要解决的问题。区分服务已成为QoS研究领域中的一个焦点。本文分析了Web请求服务时间的数字特性,并使用M／G／1 FCFS排队模型对Web服务器及Web集群服务器进行了建模。在对模型进行分析的基础上,设计并实现了一种异构Web集群服务器中的比例伸展因子区分服务的方案,并提出了基于概率空间的请求调度算法。请求被分成多个类,无论系统的负载如何,系统确保每类请求的平均伸展因子与事先给定的参数成正比。实际测试表明,所设计的方案满足相对区分服务的可预测性和可控制性的要求。     

一种基于在线辨识的Web 服务自适应接纳控制

设计并实现了一种基于量子行为粒子群算法(QPSO)系统模型在线辨识的Web服务自适应接纳控制,根据系统模型的变化在线调节比例积分控制器参数.通过接纳时间比反馈控制机制,调整控制周期内服务器接纳请求的时间长度,进而实现接纳控制.通过仿真实验,并与多种不同控制方法进行比较,所得结果表明,在线辨识自适应控制能够在服务器过载的情况下更有效地控制系统资源,进一步提高了服务质量.     

基于用户会话的Web服务器应用软件的QoS技术分析

传统的Web服务器应用软件对客户的请求不加识别和区分,接收到一个请求便立即进行处理。因此,这种“一视同仁”的服务无法为高优先级的请求提供Web QOS保证。通过改进应用软件,Web服务器可以为不同的客户或请求提供Web QOS。其主要方法是将客户的HTTP请求进行分类,并且实现优先化调度、接纳控制、资源分配等机制。本文介绍了基于用户会话的Web服务器应用软件的QoS技术,并指出其中存在的缺陷。     

集群服务器响应延时预测及其负载调度控制

单纯以负载均衡为目标的Web服务器集群调度不仅增加了算法的复杂度,在集群超载的情况下也无法保证服务的质量,而以响应延时为基础的均衡算法则可确保更好的服务质量.综合运用多级模糊系统与人工神经网络,既基于服务器系统的负载状况,又兼顾Web请求文件的类型与大小,对响应延时进行模糊预测,并在此基础上提出了一种基于响应延时阈值的集群服务器负载控制算法.构建仿真环境对所提出的参数预测及算法的有效性进行了验证.     

一种适合于DiffServ网络的多播路由算法

提出一种适合于DiffServ网络的QoS多播路由算法PQMRD,它针对组成员不同类别的QoS请求采取不同的路由策略来选取路径,同时进行分类的接纳控制和资源预留。实验结果表明,PQMRD缓解了现有多播算法中因所有业务选择相同路径而引起的服务类间的不公平性问题。     

基于非精确网络状态信息的多服务器选择

服务器复制经常被用来作为改进处理大量客户请求的服务能力的方法．在采用服务器复制时．一个关键的问题是客户如何确定哪一个服务器是最好的,通过一个简易的服务器模型，研究了在延时非精确的网络情形下，通过采用智能的票据探测技术，同时搜索延时较小的网络路径并探测目的服务器性能来进行多服务器选择的算法．仿真结果显示，与其它几种多服务器选择算法比较，由于综合考虑了服务器延时和网络延时的最优，我们的算法可以取得最好的性能．     

基于请求目标分类的Web QoS动态控制模型

针对目前通用的Web服务器上未实现有效的QoS控制的现状以及Web QoS控制灵活性不够、通用性不强、可扩展性不好等缺点,提出了一种基于请求目标分类的Web QoS动态控制模型,采用以控制响应时间为中心的动态控制策略,对HTTP请求进行基于目标分类的动态接纳控制以及动态的重配置处理。实验结果表明,该方法可以显著减少系统响应时间,并保证在高负载下的吞吐量的平稳性。     

基于收益驱动请求分类的多目标动态优先请求调度

请求调度通常需要在充分利用现有服务器资源的基础上满足响应时间最小化和系统吞吐量最大化的目标,但对于以盈利为目的的电子商务网站来说,关键还是要提高交易请求和VIP用户发起请求的达成率。针对电子商务网站请求调度的多重目标,首先提出了收益驱动的请求分类多维标准,在此基础上定义了请求优先级和调度优先级的概念,给出了基于请求分类的多目标动态优先调度算法MODP,并引入了基于事前过载判断而非负载测量的调度机制以避免控制延迟,有利于电子商务网站在多变的负载条件下自适应地实现差别服务和QoS保障。仿真实验证明了MODP机制与算法的有效性,将其与传统FCFS调度方法进行对比研究,结果表明:服务器无论在高载还是低载情况下,MODP调度策略在实现收益最大化、平均响应时间最小化的目标方面都具有明显的优势。     

The Analysis and Performance of Multi-Rate Service in Distributed Video-on-Demand Systems

In this paper, we develop an end-to-end analysis of a distributed Video-on-Demand (VoD) system that includes an integrated model of the server and the network subsystems with analysis of its impact on client operations. The VoD system provides service to a heterogeneous client base at multiple playback rates. A class-based service model is developed where an incoming video request can specify a playback rate at which the data is consumed on the client. Using an analytical model, admission control conditions at the server and the network are derived for multi-rate service. We also develop client buffer requirements in presence of network delay bounds and delay jitter bounds using the same integrated framework of server and network subsystems. Results from an extensive simulation show that request handling policies based on limited redirection of blocked requests to other resources perform better than load sharing policies. The results also show that downgrading the service for blocked requests to a lower bitrate improves VoD system performance considerably. Combining the downgrade option with restrictions on access to high bitrate request classes is a powerful tool for manipulating an incoming request mix into a workload that the VoD system can handle.     

Resource Allocation for Session-Based Two-Dimensional Service Differentiation on e-Commerce Servers

A scalable e-commerce server should be able to provide different levels of quality of service (QoS) to different types of requests based on clients' navigation patterns and the server capacity. E-Commerce workloads are composed of sessions. In this paper, we propose a session-based two-dimensional (2D) service differentiation model for online transactions: intersession and intrasession. The intersession model aims to provide different levels of QoS to sessions from different customer classes, and the intrasession model aims to provide different levels of QoS to requests in different states of a session. A primary performance metric of online transactions is slowdown. It measures the waiting time of a request relative to its service time. We present a processing rate allocation scheme for 2D proportional slowdown differentiation. We then introduce service slowdown as a systemwide QoS metric of an e-commerce server. It is defined as the weighted sum of request slowdown in different sessions and in different session states. We formulate the problem of 2D service differentiation as an optimization of processing rate allocation with the objective of minimizing the service slowdown of the server. We prove that the derived rate allocation scheme based on the optimization guarantees client requests' slowdown to be square-root proportional to their prespecified differentiation weights in both intersession and intrasession dimensions. We evaluate this square-root proportional rate allocation scheme and a proportional rate allocation scheme via extensive simulations. Results validate that both schemes can achieve predictable, controllable, and fair 2D service differentiation on e-commerce servers. The square-root proportional rate allocation scheme provides 2D service differentiation at a minimum cost of service slowdown.     

RDRP: Reward-Driven Request Prioritization for e-Commerce web sites

Meeting client Quality-of-Service (QoS) expectations proves to be a difficult task for the providers of e-Commerce services, especially when web servers experience overload conditions, which cause increased response times and request rejections, leading to user frustration, lowered usage of the service and reduced revenues. In this paper, we propose a server-side request scheduling mechanism that addresses these problems. Our Reward-Driven Request Prioritization (RDRP) algorithm gives higher execution priority to client web sessions that are likely to bring more service profit (or any other application-specific reward). The method works by predicting future session structure by comparing its requests seen so far with aggregated information about recent client behavior, and using these predictions to preferentially allocate web server resources. Our experiments using the TPC-W benchmark application with an implementation of the RDRP techniques in the JBoss web application server show that RDRP can significantly boost profit attained by the service, while providing better QoS to clients that bring more profit.     

Web服务器集群系统QoWS研究

请求负载的增加常常导致Web服务器系统性能降低,用户期望的服务质量得不到保证,这是服务级Web系统所面临和必须解决的问题。文章提出了一种Web服务器集群环境下的负载分配策略,通过对用户请求分类、将不同类别的请求进行响应性能隔离、优先为高级别请求提供服务以及请求许可控制等手段,对不同类别的Web请求提供不同质量的服务,保证了服务级用户的服务质量。同时采用最迟分配原则,改善系统的负载均衡能力,缩短系统平均响应时间。最后通过仿真实验,验证了该策略的正确性和有效性。     

Dynamic quota-based admission control with sub-rating in multimedia servers

An admission control algorithm for a multimedia server is responsible for determining if a new request can be accepted without violating the Quality of Service (QoS) requirements of the existing requests in the system. A novel quota-based admission control algorithm with sub-rating for two priority classes of requests is proposed in this study. The server capacity is divided into three partitions based on the quota values: one for each class of requests and one common pool shared by two classes of requests. Reward and penalty are adopted in the proposed system model. High-priority requests are associated with higher values of reward as well as penalty than low-priority ones. Given the characteristics of the system workload, the proposed algorithm finds the best partitions, optimizing the system performance based on the objective function of the total reward minus the total penalty. The sub-rating mechanism will reduce the QoS requirements of several low- priority clients, by cutting out a small fraction of the assigned server capacity, to accept a new high- priority client and to achieve a higher net earning value. A stochastic Petri-Net model is used to find the optimal quota values and two approximation approaches are developed to find sub-optimal settings. The experiment results show that the proposed algorithm performs better than one without sub-rating mechanism, and that the sub-optimal solutions found by the proposed approximation approaches are very close to optimal ones. The approximation approaches enable the algorithm to dynamically adjust the quota values, based on the characteristics of the system workload, to achieve higher system performance.