Audrey Cheng

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2022-270
December 16, 2022

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-270.pdf

Most caching policies focus on increasing object hit rate to improve overall system performance. However, these algorithms are insufficient for transactions. In this work, we define a new metric, transactional hit rate, to capture when caching reduces latency for transactions. We present DeToX, a caching system that leverages transactional dependencies to make eviction and prefetching decisions. DeToX is able to significantly outperform single-object alternatives on real-world workloads and popular OLTP benchmarks, providing up to a 130% increase in transaction hit rate and 3.4x improvement in cache efficiency.

Advisor: Ion Stoica and Natacha Crooks

BibTeX citation:

@mastersthesis{Cheng:EECS-2022-270,
    Author = {Cheng, Audrey},
    Title = {Take Out the TraChe: Maximizing (Tra)nsactional Ca(che) Hit Rate},
    School = {EECS Department, University of California, Berkeley},
    Year = {2022},
    Month = {Dec},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-270.html},
    Number = {UCB/EECS-2022-270},
    Abstract = {Most caching policies focus on increasing object hit rate to improve overall system performance. However, these algorithms are insufficient for transactions. In this work, we define a new metric, transactional hit rate, to capture when caching reduces latency for transactions. We present DeToX, a caching system that leverages transactional dependencies to make eviction and prefetching decisions. DeToX is able to significantly outperform single-object alternatives on real-world workloads and popular OLTP benchmarks, providing up to a 130% increase in transaction hit rate and 3.4x improvement in cache efficiency.}
}

EndNote citation:

%0 Thesis
%A Cheng, Audrey
%T Take Out the TraChe: Maximizing (Tra)nsactional Ca(che) Hit Rate
%I EECS Department, University of California, Berkeley
%D 2022
%8 December 16
%@ UCB/EECS-2022-270
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-270.html
%F Cheng:EECS-2022-270