Intelligent Dynamic Aging Approaches in Web Proxy Cache Replacement

Ali, Waleed; Shamsuddin, Siti Mariyam

doi:10.4236/jilsa.2015.74011

Cited by 8 publications

(5 citation statements)

References 18 publications

(40 reference statements)

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“…The proposed NB-GDS and SVM-GDSF approaches were compared with the most common cache replacement methods used in squid software such as LRU, GD, GDSF and LFU-DA [24], [6]. In addition, NB-GDS and SVM-GDSF were compared with other existing intelligent proxy cache replacement methods, such as NNPCR-2 [6], SVM-LRU [8], and SVM-DA [7].…”

Section: Overall Comparison and Discussionmentioning

confidence: 99%

“…Least-Frequently-Used-Dynamic-Aging (LFU-DA) and Least-Recently-Used (LRU) were enhanced using supervised machine learning in previous works [7], [8], respectively. However, the hit ratio measure achieved by the intelligent LRU and LFU-DA approaches were not good enough compared to GDS and GDSF because neither the size nor the retrieving time of web pages was considered in these approaches.…”

Section: Summary Of Contributionsmentioning

confidence: 99%

“…Basically, the implementation of web caching is done in three levels: client machine, proxy server and/or origin server. However, it is considered that the most significant caching approach is web proxy caching [2]- [7] which is used to save the networks" bandwidth, reduce Internet network traffic and decrease user-perceived latency.…”

Section: Introductionmentioning

confidence: 99%

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Performance Improvement of Web Proxy Cache Replacement using Intelligent Greedy-Dual Approaches

Ali¹

2018

ijacsa

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This paper reports on how intelligent Greedy-Dual approaches based on supervised machine learning were used to improve the web proxy caching performance. The proposed intelligent Greedy-Dual approaches predict the significant web objects' demand for web proxy caching using Naïve Bayes (NB), decision tree (C4.5), or support vector machine (SVM) classifiers. Accordingly, the proposed intelligent Greedy-Dual approaches effectively make the cache replacement decision based on the trained classifiers. The trace-driven simulation results indicated that in terms of byte hit ratio and/or hit ratio, the performance of each of the conventional Greedy-Dual-Size-Frequency (GDSF) and Greedy-Dual-Size (GDS) was noticeably enhanced by applying the proposed Greedy-Dual approaches on five real datasets.

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Section: Overall Comparison and Discussionmentioning

confidence: 99%

Section: Summary Of Contributionsmentioning

confidence: 99%

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Performance Improvement of Web Proxy Cache Replacement using Intelligent Greedy-Dual Approaches

Ali¹

2018

ijacsa

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“…In our prior work, noncyclic ACO-GA (GENACO) [15] outperformed cyclic ACO-GA (CGACA) [11] in terms of an optimal objective function value. In this paper, the proposed LRU-GENACO method is tested more comprehensively using four real proxy-log datasets from IRCache used in [25,35,44]. Subsequently, we tested it by calculating the hit ratio.…”

Section: Simulation and Datasetsmentioning

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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“…Another hybrid of LFU-DA and machine learning technique such as SVM, NB, and C4.5 proposed by Ali and Shamsuddin [15] to enhance the performance of HR and BHR. Though it works well, however, features are selected manually in training machine learning techniques.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Cooperative Adaptive Weight Ranking Policy via dynamic aging based on NB and J48 classifiers

Al-Qudah

Olanrewaju

Azman

2017

IJEEI

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The increased usage of World Wide Web leads to increase in network traffic and create a bottleneck over the internet performance. For most people, the accessing speed or the response time is the most critical factor when using the internet. Reducing response time was done by using web proxy cache technique that storing a copy of pages between client and server sides. If requested pages are cached in the proxy, there is no need to access the server. But, the cache size is limited, so cache replacement algorithms are used to remove pages from the cache when it is full. On the other hand, the conventional algorithms for replacement such as Least Recently Use (LRU), First in First Out (FIFO), Least Frequently Use (LFU), Randomised Policy, etc. may discard essential pages just before use. Furthermore, using conventional algorithms cannot be well optimized since it requires some decision to evict intelligently before a page is replaced. Hence, this paper proposes an integration of Adaptive Weight Ranking Policy (AWRP) with intelligent classifiers (NB-AWRP-DA and J48-AWRP-DA) via dynamic aging factor. To enhance classifiers power of prediction before integrating them with AWRP, particle swarm optimization (PSO) automated wrapper feature selection methods are used to choose the best subset of features that are relevant and influence classifiers prediction accuracy. Experimental Result shows that NB-AWRP-DA enhances the performance of web proxy cache across multi proxy datasets by 4.

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Intelligent Dynamic Aging Approaches in Web Proxy Cache Replacement

Cited by 8 publications

References 18 publications

Performance Improvement of Web Proxy Cache Replacement using Intelligent Greedy-Dual Approaches

Performance Improvement of Web Proxy Cache Replacement using Intelligent Greedy-Dual Approaches

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

Intelligent Cooperative Adaptive Weight Ranking Policy via dynamic aging based on NB and J48 classifiers

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