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\title{Thesis Background}
\author{Zhengyi Chen}
\date{\today}

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% Phil Karlton's famous quote about the 2 hard problems in CS here, maybe.

The problem of cache replacement is general to computer systems of all scales and topologies: 
topologically massive systems, such as cellular stations\cite{GWHSZ.2014.CacheReplAsMDP-QLearning} 
and CDNs\cites{EHOFK.2020.IBM-LRUvsFIFO}{!BGW.2010.CDN}{KD.2002.Akamai_CoordCacheRepl}, and  
data-path level implementations for processors\cites{QJPSE.2007.DIP}{JTSE.2010.RRIP}{SYS.2021.RLR} 
alike requires good solutions to maintain and maximize application performance 
to various levels of granularity. On the other hand, the set of feasible/performant solutions 
(i.e., cache replacement policies) to one system may or may not be inspiring to performance 
improvement on another system of different scale, objectives, tasks, constrained by a 
(mostly) different context of available inputs, metadata, etc. 

We propose a framework for dynamic cache-replacement-strategy selection that balances computation 
cost, optimality, and working-set estimation for each strategy while incurring minimal performance 
penalties for a shared-kernel cooperative Distributed Shared Memory system. (We identify \dots)

\section[1]{Existing Cache Replacement Strategies}
\subsection[1.1]{LRU-derived Algorithms}
\subsection[1.2]{FIFO-derived Algorithms}
\subsection[1.3]{Cache Replacement in Processors}
\subsection[1.4]{Machine Learning and Heuristics}

\section[2]{The Cache Replacement Problem}

\section[3]{Page Replacement in (SMP or?) Linux}
%-- But LRU_GEN is interop-ed with an array of other systems, 
% how could we trivially implement alternative page replacement algorithms with maximum feature 
% compliance? 
% 

% Cache replacement strategies local to its own resources, for example CPU cache line replacement stategies, may not optimally perform cache eviction and 
% replacement for CDNs which (1) center \textit{freqency} over \textit{recency} and (2) could 
% cooperate to utilize a nearby cache with small additional transfer cost\cite{KD.2002.Akamai_CoordCacheRepl}. 
% Orthogonally, cache replacement strategies that perform well on one task might perform less well on 
% another, as implied by \cite{SYS.2021.RLR} among others. 

% this is the case for Linux's \textit{multi-gen LRU} page replacement algorithm which 
% by default prioritizes memory access via page table to be stored in cache over those via file 
% descriptors (though it dynamically self-adjusts)\cite{Z.2022.Linux_LRU_GEN} -- the kernel developers 
% assume that the former is costlier upon page fault. This is well and good for programs with 

% This is not to say that some amount of "technological transfer" from cache replacement strategies 
% intended for one specific setting could not be 

% A performant cache replacement strategy, relative to its hosting
% system, needs to strike balance between optimality and the necessary computation needed to make a 
% replacement/eviction decision. 

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