Investigating Multi-Tier and QoS-Aware Caching Based on ARC

Published

October 16, 2023

Author(s)

Lydia Ait Oucheggou, Abdella Battou, Jalil Boukhobza, Stéphane Rubini

Abstract

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 QoS-aware 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.

Proceedings Title

mascots ieee proceedings

Volume

3

Conference Dates

October 16-18, 2023

Conference Location

Stony Brook, NY, US

Conference Title

31st International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems

Pub Type

Conferences

Download Paper

https://doi.org/10.1109/mascots59514.2023.10387601

Local Download

Keywords

Cache, Storage, ARC, Multi-tier, QoS

Citation

Ait Oucheggou, L. , Battou, A. , Boukhobza, J. and Rubini, S. (2023), Investigating Multi-Tier and QoS-Aware Caching Based on ARC, mascots ieee proceedings , Stony Brook, NY, US, [online], https://doi.org/10.1109/mascots59514.2023.10387601, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956763 (Accessed November 23, 2024)