基于Cache内容价值的调度算法研究

现有Web代理Cache调度机制主要是基于传统的数据处理算法,为加快网络对用户请求的响应速度,文章提出了一种新的基于用户代理Cache内容和其内容的语义信息作为基础的层次数据模型,基于这种模型设计并实现了基于内容识别的Web代理Cache调度算法-CAOA算法,并通过实验对算法性能进行了验证。     

一种快速响应的流媒体服务器集群系统

文章提出一种基于对用户请求快速响应的流媒体服务器集群系统。它利用网络负载平衡技术(NLB)来实现服务器集群各节点的负载均衡。并利用一种改进的两级高速缓存模块来保证媒体文件的存储调度的高效性，为了解决用户请求延迟时间太长的问题在两级缓存中增加一台首数据段缓存服务器来预处理用户请求，大大减少了用户请求的等待时间。     

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。     

WiMAX拓展实时轮询调度机制分析

为了提高无线资源的利用率,使WiMAX系统更好地支持语音业务,基于IEEE 802.16e协议的QoS调度体系,提出了针对带有静默压缩语音业务的拓展实时轮询调度机制的具体实现方案.而且通过对方案中关键参数的分析,给出了一种QoS保证方法.方法根据用户时延对数据带宽分配进行补偿,并按照用户负载情况动态地调整轮询请求带宽的分配周期.使用OPNET软件进行了系统级性能评估,结果表明所提出的方法可有效地降低用户上行接入时延和上行丢包率,从而提高了通信质量.     

基于预测机制的分级负载均衡算法

为解决服务器集群负载分配不均的问题,根据用户访问的请求类型,综合考虑用户历史请求引起的负载增量和服务器节点性能,提出了基于预测机制的分级负载均衡算法。负载均衡节点根据用户访问的请求类型建立一次指数平滑预测模型,对相应请求类型引起的负载进行预测,并将预测负载划分为低负载、正常负载、重负载等三个负载等级,根据负载等级对用户请求进行调度,从而实现负载均衡。使用OPNET仿真软件进行测试,结果表明该算法能有效提高负载均衡效率,有较好的负载均衡效果。     

基于多面体的分布式WiMAX Mesh网络带宽调度算法

在轮询带宽调度和随机带宽调度两种经典算法的基础上,提出一种基于多面体优先列表的带宽资源调度算法来分析和优化WiMAX网络Mesh模式的带宽请求和调度过程.该算法通过为各个用户站构建不同的静态优先请求次序,避免或者减少了"带宽资源分配不均"现象,使带宽资源能够在网络局部得到优化调度,并扩展到整个无线网络.分析和仿真结果表明,本算法能够极大地改善整个网络的运行性能.     

大规模用户登录服务的负载平衡实现

PASNMP协议和LDAP目录服务为基础,利用DNS服务对用户请求进行调度,该文提出了一套符合本地实际情况的大最用户登录的解决方案,并实现了一个完整的登录调度系统,很好地解决了集群、网格环境下的大量用户登录的负载平衡问题。     

分布式诊断系统的请求调度模型

分析了分布式远程监测和诊断系统中存在的请求处理没有优先级别、大用户量并发访问时系统稳定性较差等问题,在CORBA的POA机制的基础上提出基于消息驱动的请求调度模型,讨论了实现请求调度模型的关键技术,如CORBA：：Any类型、自定义的消息模型等。重点介绍了基于消息驱动的请求调度模型的实现思路和实施细节。基于消息驱动的请求调度模型已在我国的石化企业实际应用,证实请求调度模型大大提高了分布式系统的效率和可靠性。     

网格经济模型下基于信誉度的资源调度策略

基于资源的信誉度,提出一种经济模型下的网格资源调度策略.由用户自己设定各因素的重要性,调度器根据用户提交任务的需求不同,将任务放到对应的任务队列中,各队列调用不同的调度算法,以得出最优资源分配.同时每次任务完成后,用户的满意度又直接影响到资源的信誉度.理论上能够实现资源和消费者的有效匹配,使消费者请求达到快速响应,让资源提供者和消费者双方获得更高的满意度.     

基于Eucalyptus 网站云的资源调度

网站云是一种多租户的云部署架构.研究了基于Eucalyptus实现的网站云资源综合调度策略,提出了以HTTP请求为细粒度任务的贪婪调度算法,并引入了带权机器学习算法模型,使系统能主动感知请求高峰并进行负载均衡.实验证明达到了较理想效果.     

一种新颖的带模糊截止时限的磁盘调度算法

设计了一种新的基于截止时限的磁盘调度算法,该算法支持带多优先级的请求。对于某些实时要求,其截止时限是不确定的或者不精确的,该算法采用模糊集来描述这类不确定性,模糊截止时限的隶属度函数表示对请求完成时间的满意程度。调度的目的是最优的指定优先级,使得截止时限的满意程度最大化。根据请求截止时限的不同,把满意程度划分为若干连续的区间。在每个不同的区间内,每个请求都对应有修正的截止时限,把请求按照其修正的截止时限非减的顺序分配优先级,才能实现请求优先级的最优配置。仿真结果表明该算法能有效的分配请求的优先级,降低请求的丢失率,保证了更多的请求得到满足。     

考虑交付期限的订单配送策略

近年来,一种新兴的高效同城配送服务模式迅速发展,即针对随机到达的订单实现当天从点到点送货上门具有交付期限的订单配送服务.针对具有交付期限的、随机到达的订单配送问题,考虑平台可通过自有车辆服务订单,同时也可委托第3方物流服务订单的特点,提出一种决定是否接受该订单和确定车辆服务订单集合的订单配送策略.首先,以平台的运营成本最小为目标,建立订单配送的马尔可夫决策模型;然后,分析最优策略的性质;最后,与FCFS策略和按时交付策略进行对比分析.实验结果表明:所提出订单配送策略相比于FCFS策略与按时交付策略,不仅能够降低平台的总运营成本,且能够提高顾客满意度,保证服务质量,并通过数值分析验证了所建立的模型和求解算法的有效性.     

Priority scheduling of requests to web portals

Web portals work as a point of access to a large volume of information on the web. This paper focuses on the performance of Web portals in an E-commerce environment which involves the processing of a large number of users’ requests. It proposes a class-based priority scheme which classifies users’ requests into high and low priorities. In E-commerce, some requests (e.g. buy) are generally considered more important than others (e.g. search or browse). We contend that the requests received from a Web portal should generally get higher priority as such requests are more likely to lead to purchases. We believe that assigning such priorities at multiple service levels can improve the performance of Web portals’ requests of higher priority. The proposed scheme is formally specified and implemented, and performance results are obtained and compared to a server that does not prioritise requests. The results show significant performance improvements in the processing of high priority requests.     

Benefits and Drawbacks of Redundant Batch Requests

Most parallel computing platforms are controlled by batch schedulers that place requests for computation in a queue until access to compute nodes is granted. Queue waiting times are notoriously hard to predict, making it difficult for users not only to estimate when their applications may start, but also to pick among multiple batch-scheduled platforms the one that will produce the shortest turnaround time. As a result, an increasing number of users resort to “redundant requests”: several requests are simultaneously submitted to multiple batch schedulers on behalf of a single job; once one of these requests is granted access to compute nodes, the others are canceled. Using simulation as well as experiments with a production batch scheduler we evaluate the impact of redundant requests on (1) average job performance, (2) schedule fairness, (3) system load, and (4) system predictability. We find that some of the popularly held beliefs about the harmfulness of redundant batch requests are unfounded. We also find that the two most critical issues with redundant requests are the additional load on current middleware infrastructures and unfairness towards users who do not use redundant requests. Using our experimental results we quantify both impacts in terms of the number of users who use redundant requests and of the amount of request redundancy these users employ. This work was supported by the NSF under Award 0546688.     

An MMAP/G/1 queueing system with repeated calls and with absolute priority

In this paper, a single-channel queueing system without an input buffer and with two types of requests is considered. At the input of the system, a marked Markov flow of requests arrives. Requests of the first type have absolute priority over requests of the second type; i.e., if a server is busy with servicing a request of the second type, an incoming request of the first type interrupts this servicing. Interrupted requests of the second type, as well as requests of this type that find the server busy on their arrival, become repeated requests and retry to get servicing later. An incoming first type request that finds the server busy with servicing a request of the same type is lost. The time of servicing requests has an arbitrary distribution dependent on the type of request. A nontrivial existence condition of the stationary operation of the system is obtained. A stationary probability distribution of system’s states at nested and random instants is found. Formulas for the main performance characteristics are obtained. The result of the numerical experiment is presented.     

面向多请求的Web服务全局优化选择模型研究

在大量相似Web服务共存竞争的环境下,基于服务质量的Web服务选择成为服务计算领域的热点问题之一.现有的Web服务选择方法主要研究单个服务请求或多个合作关系的服务请求共同选择某一个服务的情形,未考虑多个独立的服务请求同时请求同种功能服务的互相竞争性.针对该问题,根据Web服务与服务需求之间的匹配度,利用0-1整数规划建立全局优化服务选择模型,并结合实际提出通用可行的解决多请求的全局优化服务选择算法(global optimal service selection for multiple requests, GOSSMR),在保证Web服务需求质量得到满足的情况下,避免过多的请求同时选择同一个服务,做到资源合理利用,避免服务负载失衡,提高系统的性能.仿真实验验证了模型算法的可行性和有效性.     

Scheduling broadcasts with deadlines

We investigate the problem of scheduling broadcasts in data delivering systems via broadcast, where a number of requests from several clients can be simultaneously satisfied by one broadcast of a server. Most of prior work has focused on minimizing the total flow time of requests. It assumes that once a request arrives, it will be held until satisfied. In this paper, we are concerned with the situation that clients may leave the system if their requests are still unsatisfied after waiting for some time, that is, each request has a deadline. The problem of maximizing the throughput, for example, the total number of satisfied requests, is developed, and there are given online algorithms achieving constant competitive ratios.     

Applying ontology-based blog to detect information system post-development change requests conflicts

Post-development change requests are user requirements for information systems changes after development. Conflicts might occur as contradictive or inconsistent relationships between requests and existing system design. Detecting conflicts in post-developmenet change requests is an important task during requests management processes. To address this topic, this article proposes an ontology-based blog for automatically discovering conflicts in the extended use-case models of requests from users. This study proposed an information system maintenance process. The proposed approach applies ontologies to represent domain knowledge. A set of rules are used to detect conflicts. This study developed a prototype and invited two companies to evaluate it. Usage feedback opinions about ontology-based blog from two companies indicated the usefulness. The ontology-based blog is a relatively new approach which bridge requirements blogs with a formal and machine interpretable representational model. The automatic conflicts detection capability of the ontology-based blog can reduce the labor cost in requirements analysis phase.     

A historical probability based noise generation strategy for privacy protection in cloud computing

Cloud computing promises an open environment where customers can deploy IT services in pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness, various malicious service providers can exist. Such service providers may record service requests from a customer and then collectively deduce the customer private information. Therefore, customers need to take certain actions to protect their privacy. Obfuscation with noise injection, that mixes noise service requests with real customer service requests so that service providers will be confused about which requests are real ones, is an effective approach in this regard if those request occurrence probabilities are about the same. However, current obfuscation with noise injection uses random noise requests. Due to the randomness it needs a large number of noise requests to hide the real ones so that all of their occurrence probabilities are about the same, i.e. service providers would be confused. In pay-as-you-go cloud environment, a noise request will cost the same as a real request. Hence, with the same level of confusion, i.e. customer privacy protection, the number of noise requests should be kept as few as possible. Therefore in this paper we develop a novel historical probability based noise generation strategy. Our strategy generates noise requests based on their historical occurrence probability so that all requests including noise and real ones can reach about the same occurrence probability, and then service providers would not be able to distinguish in between. Our strategy can significantly reduce the number of noise requests over the random strategy, by more than 90% as demonstrated by simulation evaluation.     

Priority scheduling service for E-commerce web servers

Service scheduling is one of the crucial issues in E-commerce environment. E-commerce web servers often get overloaded as they have to deal with a large number of customers’ requests—for example, browse, search, and pay, in order to make purchases or to get product information from E-commerce web sites. In this paper, we propose a new approach in order to effectively handle high traffic load and to improve web server’s performance. Our solution is to exploit networking techniques and to classify customers’ requests into different classes such that some requests are prioritised over others. We contend that such classification is financially beneficial to E-commerce services as in these services some requests are more valuable than others. For instance, the processing of “browse” request should get less priority than “payment” request as the latter is considered to be more valuable to the service provider. Our approach analyses the arrival process of distinct requests and employs a priority scheduling service at the network nodes that gives preferential treatment to high priority requests. The proposed approach is tested through various experiments which show significant decrease in the response time of high priority requests. This also reduces the probability of dropping high priority requests by a web server and thus enabling service providers to generate more revenue.