Exploiting client caches to build large Web caches

Zhu, Yingwu; Hu, Yiming

doi:10.1007/s11227-007-0108-6

Cited by 7 publications

(3 citation statements)

References 25 publications

(34 reference statements)

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“…Some other studies (e.g., [16,17]) exploit cooperative strategy to solve multicache problem. However, they do not address the problem of where to put the requested data.…”

Section: Replacement Strategymentioning

confidence: 99%

Automatically constructing trusted cluster computing environment

Chen

Liu

et al. 2009

J Supercomput

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Trusted Computing is a technology proposed by the Trusted Computing Group (TCG) to solve security problems in computers. A lot of work has been conducted to support Trusted Computing for individual computers; however little has been done for distributed systems (e.g., clusters). If malicious or unqualified applications are deployed on cluster nodes, users may obtain forged results or their data may be leaked out. In this paper, a methodology of the Trusted Cluster Computing (TCC) is proposed to automatically construct user-trustable cluster computing environments. User-specified applications are downloaded from user-specified locations and automatically and dynamically deployed on cluster nodes. To reduce the dynamicdeployment overhead, a novel Heuristics-based Overhead-Reducing (HOR) replacement strategy is also proposed. A highly configurable simulator has been implemented to perform a series of simulations. The simulation results demonstrate that the HOR can produce Average Speedup with up to 14% (light workload), 10% (medium 52 Y. Wu et al. workload), 8% (heavy workload) higher than that of LRU-based strategies, with a typical setting of the Average Ratio of Deployment time to Execution time (ARDE) being 0.2.

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“…Some other studies (e.g., [16,17]) exploit cooperative strategy to solve multicache problem. However, they do not address the problem of where to put the requested data.…”

Section: Replacement Strategymentioning

confidence: 99%

Automatically constructing trusted cluster computing environment

Chen

Liu

et al. 2009

J Supercomput

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“…The hit rate and byte hit rate are two extensively applied performance metrics in Web caching [1,2]. Hit rate is the ratio of the number of requests that reach the proxy cache and the total number of requests.…”

Section: Introductionmentioning

confidence: 99%

Data Warehousing for Rough Web Caching and Pre-fetching

Sulaiman

Shamsuddin

Abraham

2010

2010 IEEE International Conference on Granular Computing

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“…He uses the synthetic workload balances the performance gain from the classifier in a conventional Least Recently Used cache model [4] . Zhu evaluates the potential performance benefit of cooperative proxy caching with exploiting clients caches [5][6][7] [8] .…”

mentioning

confidence: 99%

Web Caching Replacement Based on User's Visiting Action

Wang¹,

Wu²,

Zheng³

2011

AMM

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Web server aims to service clients sensitively and clients wish to explore web sites at fast bandwidth. Nevertheless, sometimes users can only get the web content at a slow response due to the slow communication. Using data buffering technique, web cache provides clients an alternative way to acquire the web content from web server at low cost and high bandwidth. Using web cache technique, users can get fast response at low communication cost. When quantities of data are buffered to the cache server, cache policy becomes an important factor which can clearly affect the performance efficiency. Our contribution is that we take full account of the user's visiting action and redesign the cache policy. Based on this, we compare our policy (UVA) with existing method (LFU) through series of experiments. The results show that our method can efficiently improve cache hit rate.

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Exploiting client caches to build large Web caches

Cited by 7 publications

References 25 publications

Automatically constructing trusted cluster computing environment

Automatically constructing trusted cluster computing environment

Data Warehousing for Rough Web Caching and Pre-fetching

Web Caching Replacement Based on User's Visiting Action

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