Abstract Attribute-Based Prediction of File Properties

We present evidence that attributes that are known to the file system when a file is created, such as its name, permission mode, and owner, are often strongly related to future properties of the file such as its ultimate size, lifespan, and access pattern.

Attribute-Based Prediction of File Properties Daniel Ellard,Michael Mesnier,Eno Thereska,Gregory R.Ganger,Margo Seltzer Abstract

We present evidence that attributes that are known to

the?le system when a?le is created,such as its name,

permission mode,and owner,are often strongly related

to future properties of the?le such as its ultimate size,

lifespan,and access pattern.More importantly,we show

that we can exploit these relationships to automatically

generate predictive models for these properties,and that

these predictions are suf?ciently accurate to enable opti-

mizations.

1Introduction

In“Hints for Computer System Design,”Lampson

tells us to“Use hints to speed up normal execution.”[14]

The?le system community has rediscovered this prin-ciple a number of times,suggesting that hints about a ?le’s access pattern,size,and lifespan can aid in a va-riety of ways including improving the?le’s layout on disk and increasing the effectiveness of prefetching and caching.Unfortunately,earlier hint-based schemes have required the application designer or programmer to sup-ply explicit hints using a process that is both tedious and error-prone,or to use a special compiler that can recog-nize speci?c I/O patterns and automatically insert hints. Neither of these schemes have been widely adopted.

In this paper,we show that applications already give useful hints to the?le system,in the form of?le names and other attributes,and that the?le system can success-fully predict many?le properties from these hints.

We begin by presenting statistical evidence from three contemporary NFS traces that many?le attributes,such as the?le name,user,group,and mode,are strongly re-lated to?le properties including?le size,lifespan,and access patterns.We then present a method for automati-cally constructing tree-based predictors for the properties of a?le based on these attributes and show that these

predictions are accurate.Finally,we discuss uses for such predictions,including an implementation of a sys-tem that uses them to improve?le layout by anticipating which blocks will be the most frequently accessed and grouping these blocks in a small area on the disk,thereby improving reference locality.

The rest of this paper is organized as follows:Sec-tion2discusses related work.Section3describes the collection of NFS traces we analyze in this study.Sec-tion4makes the case for attribute-based predictions by presenting a statistical analysis of the relationship be-tween attributes of?les and their properties.Section5 presents ABLE,a classi?cation-tree-based predictor for several?le properties based on their attributes.Section6 discusses how such models might be used,and demon-strates an example application which increases the local-ity of reference for on-disk block layout.Section7con-cludes.

2Related Work

As the gap between I/O and CPU performance has increased many efforts have attempted to address it.An entire industry and research community has emerged to 1

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