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Efficient cache resource aggregation using adaptive multi-level exclusive caching policies

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Efficient cache resource aggregation using adaptive multi-level exclusive caching policies

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dc.contributor.author Cheng, Yuxia es_ES
dc.contributor.author Xiang, Yang es_ES
dc.contributor.author Chen, Wenzhi es_ES
dc.contributor.author Hassan Mohamed, Houcine es_ES
dc.contributor.author Alelaiwi, Abdulhameed es_ES
dc.date.accessioned 2020-03-23T08:46:12Z
dc.date.available 2020-03-23T08:46:12Z
dc.date.issued 2018-09 es_ES
dc.identifier.issn 0167-739X es_ES
dc.identifier.uri http://hdl.handle.net/10251/139155
dc.description.abstract [EN] Multi-level buffer cache hierarchies are now commonly seen in most client/server cluster configurations, especially in today's big data application deployment. However, multi-level caching policies deployed so far typically use independent cache replacement algorithms in each level, which has two major drawbacks: (1) File blocks may be redundantly cached on multiple levels, reducing the actual aggregate cache usable size; (2) Less accurate replacement decisions at lower level caches due to weakened locality. Inefficient cache resource usage may result in noticeable performance degradation for big data applications. To address these problems, we propose new adaptive multi-level exclusive caching policies that can dynamically adjust replacement and placement decisions in response to changing access patterns. (1) First, to capture locality information in multi-level cache hierarchies, we propose a Reuse Distance based Adaptive Replacement Caching (ReDARC) algorithm that adopts reuse distance as the means of locality measure and adaptively balances between the Small Reuse Distance (SRD) set and Large Reuse Distance (LRD) set. (2) Second, to achieve exclusive caching and make global caching decisions, we propose an Adaptive Level-Aware Caching Algorithm (ALACA) that works collaboratively with ReDARC. The ALACA algorithm uses an adaptive probabilistic PUSH technique that allows lower caches to push blocks to higher caches and appropriately decide blocks' caching locations with the ReDARC algorithm. In this way, we achieve multi-level exclusive caching with significant cache performance improvement. Our trace-driven simulation experiments show that the policies we proposed achieve a reduction of the client average response time of 8 percent to 56 percent over other multi-level cache schemes. es_ES
dc.description.sponsorship The authors extend their appreciation to the International Scientific Partnership Program (ISPP) at King Saud University, Riyadh, Saudi Arabia for funding this work through the project No. ISPP#0069 es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation KSU/ISPP0069 es_ES
dc.relation.ispartof Future Generation Computer Systems es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Buffer cache es_ES
dc.subject Multi-level es_ES
dc.subject Exclusive caching es_ES
dc.subject Adaptive policy es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Efficient cache resource aggregation using adaptive multi-level exclusive caching policies es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.future.2017.09.044 es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Cheng, Y.; Xiang, Y.; Chen, W.; Hassan Mohamed, H.; Alelaiwi, A. (2018). Efficient cache resource aggregation using adaptive multi-level exclusive caching policies. Future Generation Computer Systems. 86:964-974. https://doi.org/10.1016/j.future.2017.09.044 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.future.2017.09.044 es_ES
dc.description.upvformatpinicio 964 es_ES
dc.description.upvformatpfin 974 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 86 es_ES
dc.relation.pasarela S\379950 es_ES
dc.contributor.funder King Saud University, Arabia Saudita es_ES


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