Deconstructing on-board disk cache by using block-level real traces

Deng, Yuhui; Zhou, Jipeng; Meng, Xiaohong

doi:10.1016/j.simpat.2011.08.004

Cited by 7 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is consistent with the results of the study by Ma et al (2018) which stated that cache size should not be more than 50% because the cache cost is high. System designers believe that cache size only requires 0.1–0.3% of the total backend storage capacity (Deng et al , 2012).…”

Section: Caching Strategymentioning

confidence: 99%

Caching strategy for Web application – a systematic literature review

Zulfa

Hartanto

Permanasari

2020

IJWIS

View full text Add to dashboard Cite

Purpose Internet users and Web-based applications continue to grow every day. The response time on a Web application really determines the convenience of its users. Caching Web content is one strategy that can be used to speed up response time. This strategy is divided into three main techniques, namely, Web caching, Web prefetching and application-level caching. The purpose of this paper is to put forward a literature review of caching strategy research that can be used in Web-based applications. Design/methodology/approach The methods used in this paper were as follows: determined the review method, conducted a review process, pros and cons analysis and explained conclusions. The review method is carried out by searching literature from leading journals and conferences. The first search process starts by determining keywords related to caching strategies. To limit the latest literature in accordance with current developments in website technology, search results are limited to the past 10 years, in English only and related to computer science only. Findings Note in advance that Web caching and Web prefetching are slightly overlapping techniques because they have the same goal of reducing latency on the user’s side. But actually, the two techniques are motivated by different basic mechanisms. Web caching uses the basic mechanism of cache replacement or the algorithm to change cache objects in memory when the cache capacity is full, whereas Web prefetching uses the basic mechanism of predicting cache objects that can be accessed in the future. This paper also contributes practical guidelines for choosing the appropriate caching strategy for Web-based applications. Originality/value This paper conducts a state-of-the art review of caching strategies that can be used in Web applications. Exclusively, this paper presents taxonomy, pros and cons of selected research and discusses data sets that are often used in caching strategy research. This paper also provides another contribution, namely, practical instructions for Web developers to decide the caching strategy.

show abstract

Section: Caching Strategymentioning

confidence: 99%

Caching strategy for Web application – a systematic literature review

Zulfa

Hartanto

Permanasari

2020

IJWIS

View full text Add to dashboard Cite

show abstract

“…Caching techniques have been widely utilized by various computer storage systems 1 . For instance, not only modern memory hierarchy 2‐4 consists of CPU register, cache, main memory (e.g., RAM), and secondary storage (e.g., SSD 5 and HDD 6‐8 ), remote secondary storage devices can also serve as a larger and slower cache extension in the next layer.…”

Section: Introductionmentioning

confidence: 99%

RUE: A caching method for identifying and managing hot data by leveraging resource utilization efficiency

Liang

Deng

et al. 2021

Softw Pract Exp

Self Cite

View full text Add to dashboard Cite

In this study, we propose a caching method called RUE for dynamic large‐scale data streams. We define a data model to facilitate hot data identification and management. At the heart of RUE model is hot degree that takes into account two factors data resource utilization efficiency and reuse distance, aiming to quantitatively reflect data popularity in a dynamic data stream. Based on data's hot degree, RUE classifies data into four types, each of which is assigned with an associated cache residence time. Guided by RUE model, we develop HM algorithm to identify and manage hot data in a dynamic data stream. HM algorithm is implemented by four stacks, namely, new stack, short stack, long stack, and temp stack. Moreover, an eviction and a migration algorithms are integrated into HM to facilitate block replacement and migration. To evaluate the performance of HM algorithm, we quantitatively compare the performance of RUE with three state‐of‐art algorithms, namely, LRU, LIRS, and ARC under various replacement policies, operations, and workloads. Experimental results show that RUE outperforms these three existing algorithms in terms of both read and write hit rates. Furthermore, we show that with the four stacks in place, the computing overhead of HM is negligible.

show abstract

“…Traditionall y, storage devices have been modeled by means of detailed or analytic models based on devices geometry [26], zone splitting [35,5], or the use of read-ahead caches [9] and request reorderin g [30], reaching the emulatio n level in many cases. An alternate approach is given by the black-bo x simulation models, where almost no knowled ge of the storage device is required.…”

Section: Introductionmentioning

confidence: 99%

A novel black-box simulation model methodology for predicting performance and energy consumption in commodity storage devices

Prada

Calderón

García

et al. 2013

Simulation Modelling Practice and Theory

View full text Add to dashboard Cite

Traditional approache s for storage devices simulation have been based on detailed and analytic models. However, analytic models are difficult to obtain and detailed models require a high computational cost which may be not af fordable for large scale simulations (e.g. detailed data center simulation s). In current systems like large clusters, grids, or clouds, performance and energy studies are critical, and fast simulation s take an impo rtant role on them.A different approach is the black-box statistical modeling, where the storage device, its interface, and the interconnection mechanisms are modeled as a single stochastic process, defining the request response time as a random variable with an unknown distribution. A random variate generator can be built and integrated into a bigger simulation model. This approach allows to generate a simulation model for both real and synthetic complex workloads.This article describes a novel methodology that aims to build fast simulation models for storage devices. Our method uses as starting point a workload and produc es a random variate generator which can be easily integrated into large scale simulation models. A comparison between our variate generator and the widely known simulation tool DiskSim, shows that our variate generator is faster, and can be as accurate as DiskSim for both performa nce and energy consumption predictions.

show abstract

Deconstructing on-board disk cache by using block-level real traces

Cited by 7 publications

References 23 publications

Caching strategy for Web application – a systematic literature review

Caching strategy for Web application – a systematic literature review

RUE: A caching method for identifying and managing hot data by leveraging resource utilization efficiency

A novel black-box simulation model methodology for predicting performance and energy consumption in commodity storage devices

Contact Info

Product

Resources

About