本文選題：緩存　切入點：云應(yīng)用　出處：《計算機科學》2014年04期 　論文類型：期刊論文

【摘要】：性能是云應(yīng)用的重要指標,云應(yīng)用與現(xiàn)有應(yīng)用在數(shù)據(jù)存儲與管理方式和訪問模式等方面存在較大差異,這使得傳統(tǒng)的緩存管理算法難以適應(yīng)云應(yīng)用的要求。針對云應(yīng)用的特性,設(shè)計了基于影響因子的緩存策略,亦即將元數(shù)據(jù)和數(shù)據(jù)緩存分開管理,使用影響因子綜合管理影響緩存被再次訪問的幾率的多種因素,區(qū)別創(chuàng)建、打開、讀取和修改等操作對緩存再次訪問的幾率的影響;設(shè)計了緩存關(guān)聯(lián)管理策略,即利用元數(shù)據(jù)和數(shù)據(jù)之間的關(guān)聯(lián)提高緩存管理的性能;設(shè)計了緩存主動調(diào)度策略,即通過主動淘汰較低影響因子的緩存項和動態(tài)調(diào)整元數(shù)據(jù)與數(shù)據(jù)緩存的大小來提高緩存子系統(tǒng)的適應(yīng)能力和性能。最后實現(xiàn)了原型系統(tǒng),并使用Filebench和Postmark進行了測試和分析,驗證了面向云應(yīng)用緩存子系統(tǒng)原型能提高1%~120%的I/O性能以及2%~87%的操作處理速度。
[Abstract]:Performance is an important index of cloud applications. There are great differences between cloud applications and existing applications in data storage and management methods and access patterns, which makes it difficult for traditional cache management algorithms to adapt to the requirements of cloud applications. The cache strategy based on the influence factor is designed, that is, the metadata and data cache are managed separately, and the influence factors are used to synthetically manage the factors that affect the probability of the cache being accessed again. The influence of reading and modifying operations on the probability of cache re-access; the design of cache association management strategy, which uses the association between metadata and data to improve the performance of cache management; and the design of cache active scheduling strategy. That is, to improve the adaptability and performance of the cache subsystem by actively eliminating the cache items with lower impact factors and dynamically adjusting the size of metadata and data cache. Finally, the prototype system is implemented and tested and analyzed by using Filebench and Postmark. It is verified that the prototype of the cloud-oriented cache subsystem can improve the I / O performance by 120% and the operating speed of 2% by 87%.
【作者單位】： 江蘇大學計算機科學與通信工程學院;
【基金】：高等學校博士學科點專項科研基金(20093227110005) 廣東省自然科學基金(S2011010006118) 江蘇省高校自然科學基金(09KJB520001) 浙江省自然科學基金(LY13F020012)資助
【分類號】：TP333

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