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web prefetching performance metrics:: A survey

PERFORMANCE EVALUATION 2006年第9-10期63卷 988-1004页

作者： Domenech, Josep Gil, Jose A. Sahuquillo, Julio Pont, Ana Univ Politecn Valencia Dept Comp Engn E-46071 Valencia Spain

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. (C) 2005 Elsevier B.V. All rights reserved.

关键词： web prefetching performance evaluation metrics taxonomy

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web prefetching between low-bandwidth clients and proxies: Potential and performance

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Performance Evaluation Review 1999年第1期27卷 178-187页

作者： Fan, Li Cao, Pei Lin, Wei Jacobson, Quinn Department of Computer Science University of Wisconsin Madison WI United States Dept. of Elec. and Comp. Engineering University of Wisconsin Madison WI United States

The majority of the Internet population access the World Wide web via dial-up modem connections. Studies have shown that the limited modem bandwidth is the main contributor to latency perceived by users. In this paper, we investigate one approach to reduce latency: prefetching between caching proxies and browsers. The approach relies on the proxy to predict which cached documents a user might reference next, and takes advantage of the idle time between user requests to push or pull the documents to the user. Using traces of modem web accesses, we evaluate the potential of the technique at reducing client latency, examine the design of prediction algorithms, and investigate their performance varying the parameters and implementation concerns. Our results show that prefetching combined with large browser cache and delta-compression can reduce client latency up to 23.4%. The reduction is achieved using the Prediction-by-Partial-Matching (PPM) algorithm, whose accuracy ranges from 40% to 73% depending on its parameters, and which generates 1% to 15% extra traffic on the modem links. A perfect predictor can increase the latency reduction to 28.5%, whereas without prefetching, large browser cache and delta-compression can only reduce latency by 14.4%. Depending on the desired properties of the algorithm, several configurations for PPM can be best choices. Among several attractive simplifications of the scheme, some do more harm than others; in particular, it is important for the predictor to observe all accesses made by users, including browser cache hits.

关键词： World Wide web Algorithms Bandwidth Buffer storage Client server computer systems Congestion control (communication) Data communication systems Data compression Modems Signal filtering and prediction Telecommunication links Telecommunication traffic web browsers Delta compression Prediction by partial matching (PPM) algorithm web prefetching

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An SPN-Based Integrated Model for web prefetching and Caching

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Journal of Computer Science & Technology 2006年第4期21卷 482-489页

作者：石磊韩英杰丁晓光卫琳古志民 Department of Computer Science and Engineering Beijing Institute of Technology Beijing 100081 P.R. China School of Information Engineering Zhengzhou University Zhengzhou 450052 P.R. China

The World Wide web has become the primary means for information dissemination. Due to the limited resources of the network bandwidth, users always suffer from long time waiting. web prefetching and web caching are the primary approaches to reducing the user perceived access latency and improving the quality of services. In this paper, a Stochastic Petri Nets （SPN） based integrated web prefetching and caching model （IWPCM） is presented and the performance evaluation of IWPCM is made. The performance metrics, access latency, throughput, HR （hit ratio） and BHR （byte hit ratio） are analyzed and discussed. Simulations show that compared with caching only model （CM）, IWPCM can further improve the throughput, HR and BHR efficiently and reduce the access latency. The performance evaluation based on the SPN model can provide a basis for implementation of web prefetching and caching and the combination of web prefetching and caching holds the promise of improving the QoS of web systems.

关键词： stochastic Petri nets web prefetching web caching performance evaluation

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A data cube model for prediction-based web prefetching

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2003年第1期20卷 11-30页

作者： Yang, Q Huang, JZX Ng, M Univ Hong Kong Dept Math Hong Kong Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci Hong Kong Hong Kong Peoples R China Univ Hong Kong E Business Technol Inst Hong Kong Hong Kong Peoples R China

Reducing the web latency is one of the primary concerns of Internet research. web caching and web prefetching are two effective techniques to latency reduction. A primary method for intelligent prefetching is to rank potential web documents based on prediction models that are trained on the past web server and proxy server log data, and to prefetch the highly ranked objects. For this method to work well, the prediction model must be updated constantly, and different queries must be answered efficiently. In this paper we present a data-cube model to represent web access sessions for data mining for supporting the prediction model construction. The cube model organizes session data into three dimensions. With the data cube in place, we apply efficient data mining algorithms for clustering and correlation analysis. As a result of the analysis, the web page clusters can then be used to guide the prefetching system. In this paper, we propose an integrated web-caching and web-prefetching model, where the issues of prefetching aggressiveness, replacement policy and increased network traffic are addressed together in an integrated framework. The core of our integrated solution is a prediction model based on statistical correlation between web objects. This model can be frequently updated by querying the data cube of web server logs. This integrated data cube and prediction based prefetching framework represents a first such effort in our knowledge.

关键词： data cube data mining clustering transition probability matrices web prefetching

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A user-focused evaluation of web prefetching algorithms

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COMPUTER COMMUNICATIONS 2007年第10期30卷 2213-2224页

作者： Domenech, Josep Pont, Ana Sahuquillo, Julio Gil, Jose A. Univ Politecn Valencia Dept Comp Engn Valencia 46022 Spain

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 user's 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 user's 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 user's 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. (c) 2007 Elsevier B.V. All rights reserved.

关键词： web prefetching performance evaluation user-perceived latency

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Enhancing Accessibility to Data in Data-Intensive web Applications by Using Intelligent web prefetching Methodologies

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2023年第9期33卷 1405-1438页

作者： Buyuktanir, Tolga Sigirci, I. Onur Aktas, Mehmet S. Loodos Technol R&D Dept Istanbul Turkiye Yildiz Tech Univ Comp Engn Dept Istanbul Turkiye

Data-intensive web Applications built using client-server architectures usually provide prefetching mechanisms to enhance data accessibility. prefetching is a strategy of retrieving data before it is requested so that it can be ready when the user requests it. prefetching reduces the load on the web server by making data available before the user requests it. prefetching can be used for static content, such as images and web pages, as well as dynamic content, such as search results. prefetching can also be used to improve the performance of web applications, as the data is available quickly. There are several scheduling methods, such as time-based scheduling, event-based scheduling and priority-based scheduling, for prefetching to ensure that essential data is always ready when the user requests it. In this study, we focus on time-based scheduling for prefetching. We introduce time-based scheduling methodologies using sequential pattern mining techniques and long-term short memory-based deep learning strategies. To show the usefulness of these strategies, we develop a prototype application. We conduct an extensive experimental study to evaluate the performance of the proposed time-based scheduling methodologies using both performance and accuracy metrics. Based on the computed metrics, using proposed prefetching methods provided a promising cache hit rate when using the optimal cache size. The results show that the proposed prefetching methodologies are useful in data-intensive web applications for enhancing data accessibility. Work remains to investigate the use of attention-based sequence-to-sequence models in the web prefetching domain.

关键词： web prefetching sequential pattern mining time-based scheduling data-intensive web applications deep learning

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web user clustering and web prefetching using Random Indexing with weight functions

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KNOWLEDGE AND INFORMATION SYSTEMS 2012年第1期33卷 89-115页

作者： Wan, Miao Jonsson, Arne Wang, Cong Li, Lixiang Yang, Yixian Linkoping Univ Dept Comp & Informat Sci S-58183 Linkoping Sweden Beijing Univ Posts & Telecommun Informat Secur Ctr Beijing 100876 Peoples R China

Users of a web site usually perform their interest-oriented actions by clicking or visiting web pages, which are traced in access log files. Clustering web user access patterns may capture common user interests to a web site, and in turn, build user profiles for advanced web applications, such as web caching and prefetching. The conventional web usage mining techniques for clustering web user sessions can discover usage patterns directly, but cannot identify the latent factors or hidden relationships among users' navigational behaviour. In this paper, we propose an approach based on a vector space model, called Random Indexing, to discover such intrinsic characteristics of web users' activities. The underlying factors are then utilised for clustering individual user navigational patterns and creating common user profiles. The clustering results will be used to predict and prefetch web requests for grouped users. We demonstrate the usability and superiority of the proposed web user clustering approach through experiments on a real web log file. The clustering and prefetching tasks are evaluated by comparison with previous studies demonstrating better clustering performance and higher prefetching accuracy.

关键词： web user clustering Random Indexing Weight functions web prefetching

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Objective-optimal algorithms for long-term web prefetching

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IEEE TRANSACTIONS ON COMPUTERS 2006年第1期55卷 2-17页

作者： Wu, B Kshemkalyani, AD Univ Illinois Dept Comp Sci Chicago IL 60607 USA

web prefetching is based on web caching and attempts to reduce user-perceived latency. Unlike on-demand caching, web prefetching fetches objects and stores them in advance, hoping that the prefetched objects are likely to be accessed in the near future and such accesses would be satisfied from the caches rather than by retrieving the objects from the web server. This paper reviews the popular prefetching algorithms based on Popularity, Good Fetch, APL characteristic, and Lifetime, and then makes the following contributions: 1) The paper proposes a family of linear-time prefetching algorithms, Objective-Greedy prefetching, wherein each algorithm greedily prefetches those web objects that most significantly improve the performance as per the targeted metric. 2) The Hit rate-Greedy and Bandwidth-Greedy algorithms are shown to be optimal for their respective objective metrics. A linear-time optimal prefetching algorithm that maximizes the H/B metric as the performance measure is proposed. 3) The paper shows the results of a performance analysis via simulations, comparing the proposed algorithms with the existing algorithms in terms of the respective objectives - the hit rate, bandwidth, and the H/B metrics. The proposed prefetching algorithms are seen to provide better objective-based performance than any existing algorithms. Further, H/B-Greedy performs almost as well as H/B-Optimal.

关键词： web server World Wide web web caching web prefetching content distribution web object hit rate bandwidth optimal object selection randomized algorithm.

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Mobile prefetching and web prefetching: A Systematic Literature Review 22nd

Mobile Prefetching and Web Prefetching: A Systematic Literat...

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22nd International Conference on Computational Science and its Applications (ICCSA)

作者： Buyuktanir, Tolga Aktas, Mehmet S. Yildiz Tech Univ Comp Engn Dept Istanbul Turkey Loodos Tech Istanbul Turkey

ISBN: (纸本)9783031105456;9783031105449

Today, we see that prefetching systems are widely used to decrease the network traffic, data access latency, energy consumption, and computing performance inefficiency of data-intensive operations. However, prefetching is a concept used in different computing and IT fields such as microprocessor design, micro-controller design, hard disk design, database, network, web application, mobile application, etc. The fact that the concept of prefetching is used in different fields causes difficulties in literature review and research. Therefore, these studies need to be organized systematically for each field. This study presents a systematic literature review of prefetching related mobile and web applications to identify research gaps, define new study subject opportunities, and present future directions. We think that this study will make it easier for other researchers to work in this field with the research gaps and opportunities indicated.

关键词： web prefetching Mobile prefetching Systematic literature review Intelligent techniques prefetching strategies

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Intentionality-related Deep Learning Method in web prefetching 27

Intentionality-related Deep Learning Method in Web Prefetchi...

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27th IEEE International Conference on Network Protocols (IEEE ICNP)

作者： Zou, Wenbo Won, Jiwoong Ahn, Jemin Kang, Kyungtae Hanyang Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9781728127002

Many prediction models have been proposed to improve the effectiveness of web prefetching for reducing the response time perceived by users when browsing the web. Most of these models are based on structure learning and are applied at the client side. Currently, considerable attention is being paid to proxy-based prefetching because it is more effective and accurate in predicting the correlated pages of many websites of similar interest for more homogeneous users. Compared with client-based prefetching, more complex prediction tasks must run in the proxy, which implies that a more powerful prediction model is required. Thus, based on the time-series characteristics of browsing records, we proposed the intentionality-related long short-term memory (Ir-LSTM) model, which combines both the Skip-Gram embedding method and the LSTM model while expanding the input features with user information. We also propose a novel dynamic allocation module for detecting real-time traffic bursts and correspondingly adjusting the correlation coefficient of the model's output to achieve higher server-side resource utilization while fully maximizing hit ratio.

关键词： web prefetching web prediction model Intentionality-related long short-term memory (Ir-LSTM)

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