Graph4Cache：一种用于缓存预取的图神经网络模型.pdf

计算机研究与发展DOI：10.7544/issn1000-1239.202440190

Journal

of

Computer

Research

and

Development61（8）：1945−1956，2024

Graph4Cache：一种用于缓存预取的图神经网络模型

尚晶武智晖肖智文张逸飞

（中国移动信息技术中心北京100033）

（中移动信息技术有限公司北京100033）

（shangjing@）

Graph4Cache:AGraphNeuralNetworkModelforCachePrefetching

Shang

Jing,

Wu

Zhihui,

Xiao

Zhiwen,

and

Zhang

Yifei

（ChinaMobileInformationTechnologyCenter,Beijing100033）

（ChinaMobileInformationTechnologyCo.,Ltd.,Beijing100033）

AbstractMost

computing

systems

utilize

caching

to

reduce

data

access

latency,

speed

up

data

processing

and

balance

service

load.

The

key

to

cache

management

is

to

determine

the

appropriate

data

to

be

loaded

into

or

discarded

from

the

cache,

as

well

as

the

appropriate

timing

for

cache

replacement,

which

is

critical

to

improving

cache

hit

rate.The

existing

caching

schemes

face

with

two

problems:

In

real-time

and

online

caching

scenarios,

it

is

difficult

to

discern

the

heat

information

of

user

access

to

data

while

ignoring

the

complex

high-order

information

among

data-

access-sequences.

In

this

paper,

we

propose

a

GNN-based

cache

prefetching

network

named

Graph4Cache.

We

model

a

single

access

sequence

into

a

directed

graph

(ASGraph),

where

virtual

nodes

are

used

to

aggregate

the

features

of

all

nodes

in

graph

and

represent

the

whole

sequence.

Then

a

cross

sequence

undirected

graph

(CSGraph)

is

constructed

from

the

virtual

nodes

of

ASGraphs

to

learn

cross-sequence

features,

which

greatly

complements

the

limited

item

transitions

in

a

single

sequence.

By

fusing

the

information

of

these

two

graphs

,

we

learn

the

high-order

correlations

among

sequences

and

get

abundant

user

intents.

Experimental

results

on

multiple

public

data

sets

demonstrate

the

effectiveness

of

this

method.

Gra