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Communication Dans Un Congrès Année : 2023

Investigating Multi-tier and QoS-aware Caching based on ARC

Résumé

Memory caching is a common practice to reduce application latencies by buffering relevant data in high speed memory. When the volume of data to cache is too large or a DRAM-based solution too expensive, several technologies such as NVM or high speed SSDs could complement DRAM to form a multi-tier cache. Additionally, most existing policies focus on categorizing the data based on factors like recency and frequency, setting aside the fact that applications/customers have varying Quality-of-Service requirements. This concept is well established in Cloud environment with Service Level Agreement (SLA). In this paper, by extending the Adaptive Replacement Cache (ARC), that uses recency and frequency lists, we propose a QoSaware Multi-tier Adaptive Replacement Cache (QM-ARC) policy with the ability to take into account data applications/customers priorities through the concept of penalty borrowed from the Cloud. QM-ARC is generic, as it can be applied whatever the number of tiers and can accommodate different penalty functions. Using synthetic and real traces, our solution improved QoS as compared to state-of-the-art work.
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Dates et versions

hal-04290816 , version 1 (17-11-2023)

Identifiants

  • HAL Id : hal-04290816 , version 1

Citer

Lydia Ait-Oucheggou, Stéphane Rubini, Abdella Battou, Jalil Boukhobza. Investigating Multi-tier and QoS-aware Caching based on ARC. 31st International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Oct 2023, Stony Brook, United States. pp.161-164. ⟨hal-04290816⟩
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