I THOUGHT BACKGROUND WRITING WOULD BE EASY
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tex/draft/background.bib
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tex/draft/background.bib
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@inproceedings{!BGW.2010.CDN,
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title={Distributed caching algorithms for content distribution networks},
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author={Borst, Sem and Gupta, Varun and Walid, Anwar},
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booktitle={2010 Proceedings IEEE INFOCOM},
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pages={1--9},
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year={2010},
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organization={IEEE}
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}
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@article{KD.2002.Akamai_CoordCacheRepl,
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title={Coordinated placement and replacement for large-scale distributed caches},
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author={Korupolu, Madhukar R. and Dahlin, Michael},
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journal={IEEE Transactions on Knowledge and Data Engineering},
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volume={14},
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number={6},
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pages={1317--1329},
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year={2002},
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publisher={IEEE}
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}
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@misc{Z.2022.Linux_LRU_GEN,
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title={Multi-Gen LRU},
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url={https://www.kernel.org/doc/html/v6.6-rc5/mm/multigen_lru.html},
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journal={The Linux Kernel documentation},
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author={Zhao, Yu},
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editor={Alumbaugh, T JEditor},
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year={2022}
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}
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tex/draft/background.md
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tex/draft/background.md
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> A High-Performance Framework for Dynamic Cache-Replacement-Strategy-Selection in Distributed Shared Memory Systems
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# Background
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> Various Kinds of (Distributed) Systems (What makes a system "distributed", anyways?) $\rightarrow$
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> (Distributed) Cache Replacement Algorithms (Strategies) $\rightarrow$
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> Limitations to common distributed cache replacement practices in extremely time-sensitive scenarios (like ours) $\rightarrow$
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> Variables that need to be accounted for in cache replacement problms $\rightarrow$
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> Need for dynamic manipulation to cache replacement strategy, which implies probing & measurement & comparison, etc. $\rightarrow$
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> Framework for such a thing, which is what we explore in this paper.
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tex/draft/background.pdf
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tex/draft/background.pdf
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tex/draft/background.tex
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tex/draft/background.tex
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\documentclass{article}
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\usepackage{biblatex}
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\title{Thesis Background}
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\author{Zhengyi Chen}
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\date{\today}
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\addbibresource{../main.bib}
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\addbibresource{background.bib}
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\begin{document}
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\maketitle
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% Phil Karlton's famous quote about the 2 hard problems in CS here, maybe.
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The problem of cache replacement is general to computer systems of all scales and topologies:
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topologically massive systems, such as cellular stations\cite{GWHSZ.2014.CacheReplAsMDP-QLearning}
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and CDNs\cites{EHOFK.2020.IBM-LRUvsFIFO}{!BGW.2010.CDN}{KD.2002.Akamai_CoordCacheRepl}, and
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data-path level implementations for processors\cites{QJPSE.2007.DIP}{JTSE.2010.RRIP}{SYS.2021.RLR}
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alike requires good solutions to maintain and maximize application performance
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to various levels of granularity. On the other hand, the set of feasible/performant solutions
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(i.e., cache replacement policies) to one system may or may not be inspiring to performance
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improvement on another system of different scale, objectives, tasks, constrained by a
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(mostly) different context of available inputs, metadata, etc.
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We propose a framework for dynamic cache-replacement-strategy selection that balances computation
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cost, optimality, and working-set estimation for each strategy while incurring minimal performance
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penalties for a shared-kernel cooperative Distributed Shared Memory system. (We identify \dots)
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\section[1]{Existing Cache Replacement Strategies}
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\subsection[1.1]{LRU-derived Algorithms}
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\subsection[1.2]{FIFO-derived Algorithms}
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\subsection[1.3]{Cache Replacement in Processors}
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\subsection[1.4]{Machine Learning and Heuristics}
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\section[2]{The Cache Replacement Problem}
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\section[3]{Page Replacement in (SMP or?) Linux}
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%-- But LRU_GEN is interop-ed with an array of other systems,
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% how could we trivially implement alternative page replacement algorithms with maximum feature
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% compliance?
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%
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% Cache replacement strategies local to its own resources, for example CPU cache line replacement stategies, may not optimally perform cache eviction and
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% replacement for CDNs which (1) center \textit{freqency} over \textit{recency} and (2) could
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% cooperate to utilize a nearby cache with small additional transfer cost\cite{KD.2002.Akamai_CoordCacheRepl}.
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% Orthogonally, cache replacement strategies that perform well on one task might perform less well on
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% another, as implied by \cite{SYS.2021.RLR} among others.
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% this is the case for Linux's \textit{multi-gen LRU} page replacement algorithm which
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% by default prioritizes memory access via page table to be stored in cache over those via file
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% descriptors (though it dynamically self-adjusts)\cite{Z.2022.Linux_LRU_GEN} -- the kernel developers
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% assume that the former is costlier upon page fault. This is well and good for programs with
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% This is not to say that some amount of "technological transfer" from cache replacement strategies
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% intended for one specific setting could not be
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% A performant cache replacement strategy, relative to its hosting
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% system, needs to strike balance between optimality and the necessary computation needed to make a
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% replacement/eviction decision.
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\printbibliography
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\end{document}
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\author{Zhengyi Chen} % Amir?
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\date{\today}
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\addbibresource{w4_slides.bib}
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\addbibresource{../main.bib}
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\begin{document}
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% Title page
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