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

Domènech, Josep; Sahuquillo, Julio; Gil, José A.; Pont, Ana

doi:10.1109/wi.2006.166

Cited by 22 publications

(20 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is because predictors falling in this category cannot predict accesses to those resources that have not been requested before to the element where the predictor is placed (first-seen resources). As expected, the ratio of first-seen resources is low when predictors are located at the server but high when located at the client [19]. However, this limitation can be overcome if additional sources of information are provided to the prediction engine.…”

Section: Sequence Of Requestsmentioning

confidence: 78%

“…The element of the web architecture (i.e., client, proxy or server) in which the prediction engine is implemented is also a performance limiting factor for prediction engines of this type [19,29]. This is because predictors falling in this category cannot predict accesses to those resources that have not been requested before to the element where the predictor is placed (first-seen resources).…”

Section: Sequence Of Requestsmentioning

confidence: 99%

See 1 more Smart Citation

A taxonomy of web prediction algorithms

Domènech

Ossa

Sahuquillo

et al. 2012

Expert Systems with Applications

Self Cite

View full text Add to dashboard Cite

Web prefetching techniques are an attractive solution to reduce the user-perceived latency. These techniques are driven by a prediction engine or algorithm that guesses following actions of web users. A large amount of prediction algorithms has been proposed since the first prefetching approach was published, although it is only over the last two or three years when they have begun to be successfully implemented in commercial products. These algorithms can be implemented in any element of the web architecture and can use a wide variety of information as input. This affects their structure, data system, computational resources and accuracy. The knowledge of the input information and the understanding of how it can be handled to make predictions can help to improve the design of current prediction engines, and consequently prefetching techniques.This paper analyzes fifty of the most relevant algorithms proposed along 15 years of prefetching research and proposes a taxonomy where the algorithms are classified according to the input data they use. For each group, the main advantages and shortcomings are highlighted.

show abstract

Section: Sequence Of Requestsmentioning

confidence: 78%

Section: Sequence Of Requestsmentioning

confidence: 99%

A taxonomy of web prediction algorithms

Domènech

Ossa

Sahuquillo

et al. 2012

Expert Systems with Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…With those traces, they conclude that combining a large browser cache with deltacompression could permit to reduce latency only by 14.4%, whereas a perfect predictor could save latency by 28.5%. Domenech et al [9] studied the impact of the web architecture on the limits of latency reduction. They identified that the main constraint to obtain the upper bound in latency savings is due to the situation in which a user access cannot be predicted, e.g., the first access of a session and the first time the predictor sees an object.…”

Section: Related Workmentioning

confidence: 99%

“…However, few studies [7][8][9][10] focus on the maximum performance achievable through web prefetching and the main constraints to reach it. Some of these works study the upper bounds in performance of web prefetching from a theoretical point of view but, to the best of our knowledge, none of them has empirically analyzed how real restrictions affect the benefits of prefetching.…”

mentioning

confidence: 99%

Key factors in web latency savings in an experimental prefetching system

et al. 2011

Self Cite

View full text Add to dashboard Cite

Although Internet service providers and communications companies are continuously offering higher and higher bandwidths, users still complain about the high latency they perceive when downloading pages from the web. Therefore, latency can be considered as the main web performance metric from the user's point of view. Many studies have demonstrated that web prefetching can be an interesting technique to reduce such latency at the expense of slightly increasing the network traffic. In this context, this paper presents an empirical study to investigate the maximum benefits that web users can expect from prefetching techniques in the current web. Unlike previous theoretical studies, this work considers a realistic prefetching architecture using real traces. In this way, the influence of real implementation constraints are considered and analyzed. The results obtained show that web prefetching could improve page latency up to 52% in the prefetchable part of the studied traces.

show abstract

“…The Markov predictor uses prediction by partial match (PPM) to find recurring sequences of events. Many proposals for web prefetching use PPM based on Markov predictors because of the hypertextual nature of the Internet and also the variability of access sequences [8], [4], [7]. Vellanki et al [13] use a Lempel-Ziv (LZ) scheme that builds a directed tree structure based on temporal evaluation of disk accesses.…”

Section: Related Workmentioning

confidence: 99%

An automatic prefetching and caching system

Lewis

Alghamdi

Assaf

et al. 2010

International Performance Computing and Communications Conference

View full text Add to dashboard Cite

Abstract-Steady improvements in storage capacities and CPU clock speeds intensify the performance bottleneck at the I/O subsystem of modern computers. Caching data can efficiently short circuit costly delays associated with disk accesses. Recent studies have shown that disk I/O performance gains provided by a cache buffer do not scale with cache size. Therefore, new algorithms have to be investigated to better utilize cache buffer space. Predictive prefetching and caching solutions have been shown to improve I/O performance in an efficient and scalable manner in simulation experiments. However, most predictive prefetching algorithms have not yet been implemented in realworld storage systems due to two main limitations: first, the existing prefetching solutions are unable to self regulate based on changing I/O workload; second, excessive number of unneeded blocks are prefetched. Combined, these drawbacks make predictive prefetching and caching a less attractive solution than the simple LRU management. To address these problems, in this paper we propose an automatic prefetching and caching system (or APACS for short), which mitigates all of these shortcomings through three unique techniques, namely: (1) dynamic cache partitioning, (2) prefetch pipelining, and (3) prefetch buffer management. APACS dynamically partitions the buffer cache memory, used for prefetched and cached blocks, by automatically changing buffer/cache sizes in accordance to global I/O performance. The adaptive partitioning scheme implemented in APACS optimizes cache hit ratios, which subsequently accelerates application execution speeds. Experimental results obtained from trace-driven simulations show that APACS outperforms the LRU cache management and existing prefetching algorithms by an average of over 50%.

show abstract

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

Cited by 22 publications

References 10 publications

A taxonomy of web prediction algorithms

A taxonomy of web prediction algorithms

Key factors in web latency savings in an experimental prefetching system

An automatic prefetching and caching system

Contact Info

Product

Resources

About