Pre-eminence of Combined Web Pre-fetching and Web Caching Based on Machine Learning Technique

Baskaran, K. R.; Chellan, Kalaiarasan

doi:10.1007/s13369-014-1373-3

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…A prefetching technique that combine the clustering technique with Support Vector Machine (SVM) was proposed in [2]. SMV is a supervised learning technique with associated learning algorithm primarily used for classification or regression.…”

Section: Related Workmentioning

confidence: 99%

“…The former method analyses the web page contents and predicts the HTML links to be followed by the clients. The latter method makes prediction based on observed page access behaviour of the user in the past [2] [14]. In a situation where several users request are processed by the server and the server idle time is not enough to prefetch all the request, it therefore calls to determine which among the several web pages to be prefetched should be of high priority in order to optimize the overall performance.…”

Section: Introductionmentioning

confidence: 99%

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Web Clustering based Prefetching in High Traffic Environment

Atta¹,

Kana²

2018

IJEME

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The need of minimizing the latency perceived by the user in fetching web objects without necessarily increasing the bandwidth has attracted several researchers in the recent years. Although web prefetching and caching is seen as a solution, proposed techniques do not consider the frequency of server idle time in their models. This paper therefore proposes a short time web prefetching framework based on clustering technique that can be effective in high traffic with low bandwidth environment where the server idle time is too minimal to fetch all users anticipated requests. Clusters from different user requests are used to perform an inter domain clustering that prioritizes the prefetching of web pages based on speed at which requests are received from each domain and the popularity of each page. Experimental results show an improvement in hit rate and precision over the classical clustering based prefetching technique when the server idle time is not enough to prefetch all clusters.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Web Clustering based Prefetching in High Traffic Environment

Atta¹,

Kana²

2018

IJEME

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“…In our earlier paper [3] where intra pages of the requested paper was not considered and LFU technique was used for removal of Web objects from the short-term cache, 64% of Improved Performance by Combining Web Pre-Fetching Using Clustering with Web Caching Based on SVM Learning Method 177…”

Section: Hit Ratio and Byte Hit Ratio Analysismentioning

confidence: 99%

Improved Performance by Combining Web Pre-Fetching Using Clustering with Web Caching Based on SVM Learning Method

Baskaran¹,

Chellan²

2016

INT J COMPUT COMMUN

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Combining Web caching and Web pre-fetching results in improving the bandwidth utilization, reducing the load on the origin server and reducing the delay incurred in accessing information. Web pre-fetching is the process of fetching the Web objects from the origin server which has more likelihood of being used in future. The fetched contents are stored in the cache. Web caching is the process of storing the popular objects "closer" to the user so that they can be retrieved faster. In the literature many interesting works have been carried out separately for Web caching and Web pre-fetching. In this work, clustering technique is used for pre-fetching and SVM-LRU technique for Web caching and the performance is measured in terms of Hit Ratio (HR) and Byte Hit Ratio (BHR). With the help of real data, it is demonstrated that the above approach is superior to the method of combining clustering based prefetching technique with traditional LRU page replacement method for Web caching.

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“…This strategy faces the challenge of selecting some cached data from among the many accessible candidates [12]. If the strategy is successfully implemented, the computational load on the cloud network will be reduced so it can reduce latency and save bandwidth utilization [13,14]. We use the perspective of Knapsack Problem 0/1 (KP01) to model the cached data offloading problem.…”

Section: Introductionmentioning

confidence: 99%

LRU-GENACO: A Hybrid Cached Data Optimization Based on the Least Used Method Improved Using Ant Colony and Genetic Algorithms

et al. 2022

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An optimization strategy for cached data offloading plays a crucial role in the edge network environment. This strategy can improve the performance of edge nodes with limited cache memory to serve data service requests from user terminals. The main challenge that must be solved in optimizing cached data offloading is assessing and selecting the cached data with the highest profit to be stored in the cache memory. Selecting the appropriate cached data can improve the utility of memory space to increase HR and reduce LSR. In this paper, we model the cached data offloading optimization strategy as the classic optimization KP01. The cached data offloading optimization strategy is then improved using a hybrid approach of three algorithms: LRU, ACO, and GA, called LRU-GENACO. The proposed LRU-GENACO was tested using four real proxy log datasets from IRCache. The simulation results show that the proposed LRU-GENACO hit ratio is superior to the LRU GDS SIZE algorithms by 13.1%, 26.96%, 53.78%, and 81.69%, respectively. The proposed LRU-GENACO method also reduces the average latency by 25.27%.

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Pre-eminence of Combined Web Pre-fetching and Web Caching Based on Machine Learning Technique

Cited by 5 publications

References 11 publications

Web Clustering based Prefetching in High Traffic Environment

Web Clustering based Prefetching in High Traffic Environment

Improved Performance by Combining Web Pre-Fetching Using Clustering with Web Caching Based on SVM Learning Method

LRU-GENACO: A Hybrid Cached Data Optimization Based on the Least Used Method Improved Using Ant Colony and Genetic Algorithms

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