Josep Domenech

A user-focused evaluation of web prefetching algorithms

Web prefetching mechanisms have been proposed to benefit web users by hiding the download latencies. Nevertheless, to the knowledge of the authors, there is no attempt to compare different prefetching techniques that consider the latency perceived by the user as the key metric. The lack of performance comparison studies from the users perspective has been mainly due to the difficulty to accurately reproduce the large amount of factors that take part in the prefetching process, ranging from the environment conditions to the workload. This paper is aimed at reducing this gap by using a cost-benefit analysis methodology to fairly compare prefetching algorithms from the users point of view. This methodology has been used to configure and compare five of the most used algorithms in the literature under current and old workloads. In this paper, we analyze the perceived latency versus the traffic increase (both in bytes and in objects) to evaluate the benefits from the users perspective. In addition, we also analyze the performance results from the prediction point of view to provide insights on the observed performance. Results show that higher algorithm complexity does not improve performance, object-based algorithms outperform those based on pages, and performance among object-based algorithms present minor differences in the object traffic increase.

Web prefetching performance metrics: a survey

Web prefetching techniques have been pointed out to be especially important to reduce perceived web latencies and, consequently, an important amount of work can be found in the open literature. But, in general, it is not possible to do a fair comparison among the proposed prefetching techniques due to three main reasons: (i) the underlying baseline system where prefetching is applied differs widely among the studies; (ii) the workload used in the presented experiments is not the same; (iii) different performance key metrics are used to evaluate their benefits.This paper focuses on the third reason. Our main concern is to identify which are the meaningful indexes when studying the performance of different prefetching techniques. For this purpose, we propose a taxonomy based on three categories, which permits us to identify analogies and differences among the indexes commonly used. In order to check, in a more formal way, the relation between them, we run experiments and estimate statistically the correlation among a representative subset of those metrics. The statistical results help us to suggest which indexes should be selected when performing evaluation studies depending on the different elements in the considered web architecture.The choice of the appropriate key metric is of paramount importance for a correct and representative study. As our experimental results show, depending on the metric used to check the system performance, results cannot only widely vary but also reach opposite conclusions.

The Impact of the Web Prefetching Architecture on the Limits of Reducing User's Perceived Latency

Web prefetching is a technique that has been researched for years to reduce the latency perceived by users. For this purpose, several Web prefetching architectures have been used, but no comparative study has been performed to identify the best architecture dealing with prefetching. This paper analyzes the impact of the Web prefetching architecture focusing on the limits of reducing the users perceived latency. To this end, the factors that constrain the predictive power of each architecture are analyzed and these theoretical limits are quantified. Experimental results show that the best element of the Web architecture to locate a single prediction engine is the proxy, whose implementation could reduce the perceived latency up to 67%. Schemes for collaborative predictors located at diverse elements of the Web architecture are also analyzed. These predictors could dramatically reduce the perceived latency, reaching a potential limit of about 97% for a mixed proxy-server collaborative prediction engine

DDG: An Efficient Prefetching Algorithm for Current Web Generation

Web prefetching is one of the techniques proposed to reduce users perceived latencies in the World Wide Web. The spatial locality shown by users accesses makes it possible to predict future accesses based on the previous ones. A prefetching engine uses these predictions to prefetch the Web objects before the user demands them. The existing prediction algorithms achieved an acceptable performance when they were proposed but the high increase in the amount of embedded objects per page has reduced their effectiveness in the current Web. In this paper we show that most of the predictions made by the existing algorithms are useless to reduce the users perceived latency because these algorithms do not take into account how current Web pages are structured, i.e., an HTML object with several embedded objects. Thus, they predict the accesses to the embedded objects in an HTML after reading the HTML itself. For this reason, the prediction advance is not enough to prefetch the objects and therefore there is no latency reduction. As a result of a wide analysis of the behaviour of the most commonly used algorithms, in this paper we present the DDG algorithm that distinguishes between container objects (HTML) and embedded objects to create a new prediction model according to the structure of the current Web. Results show that, for the same amount of extra requests to the server, DDG always outperforms the existing algorithms by reducing the perceived latency between 15% and 150% more without increasing the computing complexity

An experimental framework for testing Web prefetching techniques

The popularity of Web objects, and by extension the popularity of the Web sites, besides the appearance of clear footprints in users accesses that show a considerable spatial locality, make possible to predict future accesses based on the current ones. This fact permits to implement also prefetching techniques in Web architecture in order to reduce the latency perceived by the users. Although the open literature presents some approaches in this sense, the huge variety of prefetching algorithms, and the different scenarios and conditions where they are applied make very difficult to compare performance and to obtain correct conclusions that permit researchers to improve their proposals or even detect in which conditions one solution is more convenient than others. This is the main reason why we propose A new and free available environment in order to implement and study prefetching techniques efficiently. Our framework is a hybrid implementation that combines both real and simulated parts in order to provide flexibility and accuracy. It reproduces in detail the behavior of Web users, proxy severs and original servers. The simulator also includes a module to provide performance results, such as precision (prefetching accuracy), recall, response time, and byte transference.

Using current web page structure to improve prefetching performance

Web prefetching is a technique aimed at reducing user-perceived latencies in the World Wide Web. The spatial locality shown by user accesses makes it possible to predict future accesses from the previous ones. A prefetching engine uses these predictions to prefetch web objects before the user demands them. The existing prediction algorithms achieved an acceptable performance when they were proposed but the high increase in the number of embedded objects per page has reduced their effectiveness in the current web. In this paper, we show that most of the predictions made by the existing algorithms are not useful to reduce the user-perceived latency because these algorithms do not take into account the structure of the current web pages, i.e., an HTML object with several embedded objects. Thus, they predict the accesses to the embedded objects in an HTML after reading the HTML itself. For this reason, the prediction is not made early enough to prefetch the objects and, therefore, there is no latency reduction. In this paper we present the double dependency graph (DDG) algorithm that distinguishes between container objects (HTML) and embedded objects to create a new prediction model according to the structure of the current web. Results show that, for the same number of extra requests to the server, DDG reduces the perceived latency, on average, 40% more than the existing algorithms. Moreover, DDG distributes latency reductions more homogeneously among users.

A web caching and prefetching simulator

The enormous potential of locality based strategies like caching and prefetching to improve Web performance motivates us to propose a novel global framework for performance evaluation in scenarios where different parts of the Web architecture interact. Unlike existing proposals our approach is a fast and flexible tool that allows to represent faithfully the behaviour of each element of the architecture in order to study, reproduce, evaluate and design Web strategies to decrease the userpsilas perceived latency when surfing the Web.

About the Heterogeneity of Web Prefetching Performance Key Metrics

Web prefetching techniques have pointed to be especially important to reduce web latencies and, consequently, an important set of works can be found in the open literature. But, in general, it is not possible to do a fair comparison among the proposed prefetching techniques due to three main reasons: i) the underlying baseline system where prefetching is applied differs widely among the studies; ii) the workload used in the presented experiments is not the same; iii) different performance key metrics are used to evaluate their benefits.

Location and adoption of ICT innovations in the agri-food industry

This article identifies the web technologies adoption pattern of agri-food industries considering a set of characteristics which include location, economic performance and previous history of adopting innovations. Our main results highlight that, together with certain firm characteristics, rural locations act as an accelerator for the adoption of web technology, compared to the slower adoption rate in urban areas.

An Intelligent Technique for Controlling Web Prefetching Costs at the Server Side

Prefetching is an interesting technique for improving Web performance by reducing the user-perceived latency when surfing the Web. Nevertheless, due to its speculative nature, prefetching can increase the network traffic and the server load. This could negatively affect the overall system performance and decrease the quality of service. To minimize and maintain under control these adverse effects, in this paper we propose an intelligent prefetching mechanism that dynamically adjusts the aggressiveness of the prefetching algorithm at the server side. To this end, we also propose a traffic estimation model that permits to accurately calculate, in the server side, the extra load and traffic generated by the prefetching. The performance evaluation study shows that our proposal effectively regulates the adverse effects of Web prefetching without affecting its benefits.