Web对象可缓存性评价模型研究

为研究Web对象的可缓存性并提高缓存效率,提出了基于请求方式、HTTP状态码及HTTP参数的Web对象可缓存性评价模型,分析讨论了Web对象可缓存性评价指标。利用了Web对象可缓存度及可缓存率概念,研究了Web对象的可缓存性,采用缓存加速比指标,度量了Web缓存效率。实验与分析结果表明,可缓存率越高,缓存的污染越小,缓存加速比越大,缓存效率提升得越明显。     

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。     

一种基于对象属性的Web缓存替换策略

为了降低访问时延,提升用户体验,当前网络交互性能改进的主要手段包括缓存技术与预取技术。当前的缓存替换机制主要考虑对象的访问时间与访问频度,然而Web对象本身存在语义性。本文首先对实际Web数据访问情况进行分析,发现访问间隔的变化率对于命中率的影响具有更高的准确性,进而提出一种基于对象属性的缓存替换策略,该策略通过统计近期缓存对象的平均访问间隔,并结合该对象的标签属性作为对象在缓存中的价值。实验结果表明该策略比基于Aging的缓存策略和基于协作式中心化决策缓存策略提升7%-10%的命中率。     

Web缓存技术研究与应用

文章详细阐述了Web缓存技术的原理,研究了在具有Web缓存器的网络中本地存储器对请求对象拷贝传送的过程,深入讲解了Web缓存替换的思想,以及利用Web缓存如何有效的降低网络业务流量,缓解网络拥塞等问题。     

ASP．NET上的Web应用系统对数据缓存技术的实现

在Web应用系统开发中,缓存是生成高性能的Web应用程序最重要的因素之一。其中数据缓存以编程方式缓存任意对象,为支持这种缓存,ASP．NET提供了全功能的缓存引擎,使访问者能够轻松地在请求间保留数据,节省时间和系统资源。     

基于协同过滤的Web缓存替换算法研究

针对GDSF替换算法中对访问频率缺少预测的不足,提出了一种基于协同过滤的GDSF缓存替换算法(GDSF-CF)。该算法考虑了Web对象之间相似性与用户访问时间间隔,运用协同过滤算法生成Web对象的预测访问频率,并采用齐普夫定律参数对GDSF算法的目标函数进行了改进。当需要进行缓存替换时,利用目标函数价值计算缓存空间中的每个Web对象缓存价值,将最小缓存价值的Web对象进行替换。仿真实验结果表明,该算法的命中率HR和字节命中率BHR都有较大提升。     

.NET平台下自适应缓存对象替换算法

缓存是提高Web应用程序性能的一个重要手段..NET平台下提供了数据缓存与页面缓存技术,在已经实现的缓存对象的基础上,设计了一种缓存对象的自适应替换算法.算法使用了基于缓存对象的价值与被访问频率的综合优先级策略,提出了相应的缓存对象被替换规则.算法充分考虑了缓存对象之间的依赖关系对优先级的影响.从对象命中率与系统的整体响应时间两个方面测试了算法的性能,测试表明所提出算法相对于最小价值算法、最少使用频率算法有较大的改进.     

Web数据库查询结果处理

本文介绍了Web数据库查询结果处理的两种策略：部分缓存和全部缓存,以及缓存的两种实现方法：数据库缓存和CGI缓存。实验表明,对数据库的查询结果进行缓存,极大地提高Web站点的效率。     

利用ZIPF定律建立有效的WEB对象缓存机制

通过对Web通信量的分析,人们发现用户对Web对象的访问模式服从Zipf定律或类Zipf定律。在Web缓存的设计中,为得到所期望的Web对象命中率的要求,设计人员可以根据Zipf定律近似计算出相应的缓存大小。因此,Zipf定律为Web缓存结构的设计提供了重要的依据。适当的缓存大小结合P-LFU替换策略可以得到很高的Web缓存命中率。     

ASP缓存策略探讨

缓存是一项用来提高性能的经典技术,正确地使用缓存,能够优化网站性能、减轻服务器负载、减少客户访问延迟.本文阐述了缓存的定义,利用ASP缓存提高Web性能的重要性以及ASP缓存的分类.重点探讨了利用ASP缓存优化Web性能的策略.     

一个基于集中管理的协作式Web缓存系统

共享不同代理的缓存Web文档是减少通信量和减轻网络瓶颈的重要方法。在分析原有Web缓存通信协议（ICP）的基础上,提出了一种新的协作Web缓存系统（CMCS）并作了分析比较。通过将HTTP请求均匀分散到系统各个代理,消除了代理之间庞大的通信开销以及由此带来的处理负担。在动态变化的网络环境下,有效地将各个代理组织起来,处理来自服务器的文档。另外也克服了以往每个代理里有大量冗余内容,造成各个代理内容趋向的情形。     

Performance evaluation of Web proxy cache replacement policies

The continued growth of the World-Wide Web and the emergence of new end-user technologies such as cable modems necessitate the use of proxy caches to reduce latency, network traffic and Web server loads. In this paper we analyze the importance of different Web proxy workload characteristics in making good cache replacement decisions. We evaluate workload characteristics such as object size, recency of reference, frequency of reference, and turnover in the active set of objects. Trace-driven simulation is used to evaluate the effectiveness of various replacement policies for Web proxy caches. The extended duration of the trace (117 million requests collected over 5 months) allows long term side effects of replacement policies to be identified and quantified.

Our results indicate that higher cache hit rates are achieved using size-based replacement policies. These policies store a large number of small objects in the cache, thus increasing the probability of an object being in the cache when requested. To achieve higher byte hit rates a few larger files must be retained in the cache. We found frequency-based policies to work best for this metric, as they keep the most popular files, regardless of size, in the cache. With either approach it is important that inactive objects be removed from the cache to prevent performance degradation due to pollution.     

A novel server-side proxy caching strategy for large-scale multimedia applications

Nowadays, server-side Web caching becomes an important technique used to reduce the User Perceived Latency (UPL). In large-scale multimedia systems, there are many Web proxies, connected with a multimedia server, that can cache some most popular multimedia objects and respond to the requests for them. Multimedia objects have some particular characteristic, e.g., strict QoS requirements. Hence, even some efficient conventional caching strategies based on cache hit ratio, meant for non-multimedia objects, will confront some problems in dealing with the multimedia objects. If we consider additional resources of proxy besides cache space, say bandwidth, we can readily observe that high hit ratios may deteriorate the entire system performance. In this paper, we propose a novel placement model for networked multimedia systems, referred to as the H^k/T model, which considers the combined influence of arrival rate, size, and playback time to select the objects to be cached. Based on this model, we propose an innovative Web caching algorithm, named as the ART-Greedy algorithm, which can balance the load among the proxies and achieve a minimum Average Response Time (ART) of the requests. Our experimental results conclusively demonstrate that the ART-Greedy algorithm outperforms the most popular and commonly used LFU (Least Frequently Used) algorithm significantly, and can achieve a better performance than the byte-hit algorithm when the system utilization is medium and high.     

A neural network proxy cache replacement strategy and its implementation in the Squid proxy server

As the Internet has become a more central aspect for information technology, so have concerns with supplying enough bandwidth and serving web requests to end users in an appropriate time frame. Web caching was introduced in the 1990s to help decrease network traffic, lessen user perceived lag, and reduce loads on origin servers by storing copies of web objects on servers closer to end users as opposed to forwarding all requests to the origin servers. Since web caches have limited space, web caches must effectively decide which objects are worth caching or replacing for other objects. This problem is known as cache replacement. We used neural networks to solve this problem and proposed the Neural Network Proxy Cache Replacement (NNPCR) method. The goal of this research is to implement NNPCR in a real environment like Squid proxy server. In order to do so, we propose an improved strategy of NNPCR referred to as NNPCR-2. We show how the improved model can be trained with up to twelve times more data and gain a 5–10% increase in Correct Classification Ratio (CCR) than NNPCR. We implemented NNPCR-2 in Squid proxy server and compared it with four other cache replacement strategies. In this paper, we use 84 times more data than NNPCR was tested against and present exhaustive test results for NNPCR-2 with different trace files and neural network structures. Our results demonstrate that NNPCR-2 made important, balanced decisions in relation to the hit rate and byte hit rate; the two performance metrics most commonly used to measure the performance of web proxy caches.     

On optimal replication of data object at hierarchical and transparent Web proxies

This paper investigates the optimal replication of data objects at hierarchical and transparent Web proxies. By transparent, we mean the proxies are capable of intercepting users' requests and forwarding the requests to a higher level proxy if the requested data are not present in their local cache. Two cases of data replication at proxies are studied: 1) proxies having unlimited storage capacities and 2) proxies having limited storage capacities. For the former case, an efficient algorithm for computing the optimal result is proposed. For the latter case, we prove the problem is NP-hard, and propose two heuristic algorithms. Extensive simulations have been conducted and the simulation results have demonstrated significant performance gain by using the proposed data replication algorithms and also shown the proposed algorithms out-perform the standard Web caching algorithm (LRU threshold method).     

基于网络性能的智能Web加速技术——缓存与预取

Web业务在网络业务中占有很大比重,在无法扩大网络带宽时,需要采取一定技术合理利用带宽,改善网络性能。研究了基于RTT（round trip time)等网络性能指标的Web智能加速技术,在对Web代理服务器上的业务进行分析和对网络RTT进行测量分析的基础上,提出了智能预取控制技术及新的缓存（cache）替换方法。对新算法的仿真研究表明,该方法提高了缓存的命中率。研究表明预取技术在不明显增加网络负荷的前提下,提高了业务的响应速度,有效地改进了Web访问性能。     

基于分布式协同缓存机制的Web Proxy研究

提出了一种基于全局内存对象缓冲池的高性能分布式Web Proxy模型，通过在系统范围内构建类似于分布式共享存储器系统的缓冲池，并将分布式Web Proxy系统中访问最频繁的Web对象置于其中，则Web对象的平均服务时间缩短，提高了系统性能。通过实际Proxy服务器的访问日志进行了系统性能仿真，结果表明，该机制可以提高分布式Web Proxy服务器的性能。     

Latency-sensitive hashing for collaborative Web caching

Many geographically distributed proxies are increasingly used for collaborative Web caching to improve performance. In hashing-based collaborative Web caching, the response times can be negatively impacted for those URL requests hashed into geographically distant or overloaded proxies. In this paper, we present and evaluate a latency-sensitive hashing scheme for collaborative Web caching. It takes into account latency delays due to both geographical distances and dynamic load conditions. Each URL request is first hashed into an anchor hash bucket, with each bucket mapping to one of the proxies. Secondly, a number of nearby hash buckets are examined to select the proxy with the smallest latency delay to the browser. Trace-driven simulations are conducted to evaluate the performance of this new latency-sensitive hashing. The results show that (1) with the presence of load imbalance due to skew in request origination or hot-spot references, latency-sensitive hashing effectively balances the load by hashing into geographically distributed proxies for collaborative Web caching, and (2) when the overall system is lightly loaded, latency-sensitive hashing effectively reduces latency delays by directing requests to geographically closer proxies.     

Web proxy cache replacement scheme based on back-propagation neural network

Web proxy caches are used to reduce the strain of contemporary web traffic on web servers and network bandwidth providers. In this research, a novel approach to web proxy cache replacement which utilizes neural networks for replacement decisions is developed and analyzed. Neural networks are trained to classify cacheable objects from real world data sets using information known to be important in web proxy caching, such as frequency and recency. Correct classification ratios between 0.85 and 0.88 are obtained both for data used for training and data not used for training. Our approach is compared with Least Recently Used (LRU), Least Frequently Used (LFU) and the optimal case which always rates an object with the number of future requests. Performance is evaluated in simulation for various neural network structures and cache conditions. The final neural networks achieve hit rates that are 86.60% of the optimal in the worst case and 100% of the optimal in the best case. Byte-hit rates are 93.36% of the optimal in the worst case and 99.92% of the optimal in the best case. We examine the input-to-output mappings of individual neural networks and analyze the resulting caching strategy with respect to specific cache conditions.     

基于对象角色的高精度缓存替换算法*

现有的Web缓存器的实现主要是基于传统的内存缓存算法,由于Web业务请求的异质性,传统的替换算法不能在Web环境中有效工作。研究了Web缓存替换操作的依据,分析了以往替换算法的不足,考虑到Web文档的大小、访问代价、访问频率、访问兴趣度以及最近一次被访问的时间对缓存替换的影响,提出了Web缓存对象角色的概念,建立了一种新的基于对象角色的高精度Web缓存替换算法(ORB算法);并以NASA和DEC的代理服务器数据为例,将该算法与LRU、LFU、SIZE、Hybrid算法进行了仿真实验对比,结果证明,ORB算