A novel content classification scheme for web caches

Sajeev, G P; Sebastian, M. P.

doi:10.1007/s12530-010-9026-6

Cited by 15 publications

(4 citation statements)

References 39 publications

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“…Similarly, it only considers the frequency factor of the cache object and ignores other factors. 4) The strategy based on the SIZE [6]. This strategy replaces the object of maximum size from the cache when a new object requests for space.…”

Section: B Cache Replacement Strategymentioning

confidence: 99%

An efficient Intelligent Cache Replacement Policy Suitable for PACS

Wang¹,

Yang²,

Wang³

2021

IJMLC

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An efficient intelligent cache replacement policy suitable for picture archiving and communication systems (PACS) was proposed in this work. By combining the Support vector machine (SVM) with the classic least recently used (LRU) cache replacement policy, we have created a new intelligent cache replacement policy called SVM-LRU. The SVM-LRU policy is unlike conventional cache replacement policies, which are solely dependent on the intrinsic properties of the cached items. Our PACS-oriented SVM-LRU algorithm identifies the variables that affect file access probabilities by mining medical data. The SVM algorithm is then used to model the future access probabilities of the cached items, thus improving cache performance. Finally, a simulation experiment was performed using the trace-driven simulation method. It was shown that the SVM-LRU cache algorithm significantly improves PACS cache performance when compared to conventional cache replacement policies like LRU, LFU, SIZE and GDS.

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Section: B Cache Replacement Strategymentioning

confidence: 99%

An efficient Intelligent Cache Replacement Policy Suitable for PACS

Wang¹,

Yang²,

Wang³

2021

IJMLC

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“…• Filesize-based (SIZE) policies [11]: In filesize-based SIZE policies, the largest items in the cache are replaced when space is needed to store new items. These policies are simple and easy to implement.…”

Section: B Cache Replacement Policies 1) Conventional Cache Replacemmentioning

confidence: 99%

LR-LRU: A PACS-Oriented Intelligent Cache Replacement Policy

Wang

Yang

et al. 2019

IEEE Access

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An intelligent cache replacement policy suitable for picture archiving and communication systems (PACS) was proposed in this work. By combining the logistic regression (LR) algorithm with the classic least recently used (LRU) cache replacement policy, we have created a new intelligent cache replacement policy called LR-LRU. The LR-LRU policy is unlike conventional cache replacement policies, which are solely dependent on the intrinsic properties of the cached items. Our PACS-oriented LR-LRU algorithm identifies the variables that affect file access probabilities by mining medical data. The LR algorithm is then used to model the future access probabilities of the cached items, thus improving cache performance. In addition, 1-regularization was used to reduce the absolute values of the variables' coefficients. This screens some variables that have little influence on the model by causing their coefficients to approach zero, which achieves the effect of screening the variables. Finally, a simulation experiment was performed using the trace-driven simulation method. It was shown that the 1-regularized LR model is superior to the LR and 2-regularized LR models. The LR-LRU cache algorithm significantly improves PACS cache performance when compared to conventional cache replacement policies, such as LRU, LFU, SIZE, GDF, and GDSF.

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“…In learning phase, objects re-accessed at least once in next certain accesses obtain high probabilities, while an object with the lowest probability is replaced first. LRU-M [60] utilizes a multinominal logistic regression model to classify objects into multilevel classes for classful LRU replacement. The model was trained by using an access frequency, recency, delay, object size, type and popularity consistency.…”

Section: Object Cacheabilitymentioning

confidence: 99%

Multi-provider cloud computing network infrastructure optimization

Banditwattanawong

Masdisornchote

Uthayopas

2016

Future Generation Computer Systems

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h i g h l i g h t s• Byte-hit rate and hit rate could be optimal simultaneously in a nonuniform cost model. • i-Cloud outperformed popular LRU, GDSF and LFUDA schemes in a nonuniform cost environment.• i-Cloud's performances were stable and close to those of infinite cache size. • Window size had small performance effect when relative cache sizes were big.• Accounting data-out charge rates improved all performance aspects at small cache sizes. a b s t r a c tCloud-adopting enterprises have been increasingly employing multiple cloud providers concurrently, for example, to consume unique services and to mitigate data lock-in risk. As a consequence, the enterprises must be able to address contrasting quality-of-service degrees offered by the different providers. This paper presents an intelligent cloud cache eviction approach, namely i-Cloud, as the core component of a client-side cloud cache. i-Cloud is capable of reducing public cloud data-out expenses, improving cloud network scalability and lowering cloud service access latencies specifically in multi-provider cloud environments. Trace-driven simulations have shown that i-Cloud outperformed well-known approaches in all performance metrics. In addition, i-Cloud is not only able to achieve optimal performances in all metrics simultaneously but also delivered relatively stable performances across all performance metrics. The results have also indicated that taking the nonuniformity of data-out charge rates into cache eviction decisions improved caching performances in all metrics.

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A novel content classification scheme for web caches

Cited by 15 publications

References 39 publications

An efficient Intelligent Cache Replacement Policy Suitable for PACS

An efficient Intelligent Cache Replacement Policy Suitable for PACS

LR-LRU: A PACS-Oriented Intelligent Cache Replacement Policy

Multi-provider cloud computing network infrastructure optimization

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