arc: a self-tuning, low overhead replacement cache nimrod megiddo and dharmendra s. modha presented...

ARC: A self-tuning, low overhead Replacement Cache

Nimrod Megiddo andDharmendra S. Modha

Presented by Gim Tcaesvk

Cache

Cache

Auxiliary memory

Cache is expensive.

The replacement policy is the only algorithm of interest.

Cache management problem

Maximize the hit rate.

Minimize the overhead.ComputationSpace

Belady’s MIN

Replaces the furthest referenced page.

Optimum for every case.Provides upper bound of hit ratio.

But,Who knows the future?

LRU (Least Recently Used)

Replaces the least recently used page.Optimum policy for SDD (Stack Depth Distributi

on).Captures recency but not frequency.

0 1 2 3 2 3 2 1

0

1

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1

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2

1

LFU (Least Frequently Used)

Replaces the least frequently used page.Optimum policy for IRM (Independent Referenc

e Model).Captures frequency but not recency.

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0

1

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0

Drawbacks of LFU

Logarithmic complexity in cache size

High frequently used page is hardly to paged out.Even if it is no longer useful.

LRU-2

Replaces the least 2nd recently used page.

Optimum online policy which knows at most 2 most recent reference for IRM.

Limitations of LRU-2

Logarithmic complexityDue to priority queue

Tunable parameter,CIP (Correlated Information Period)

2(2,4)

1(1,3)

0(-∞,0)

2Q (modified LRU-2)

Uses simple LRU list instead of the priority queue.

Two parameter, Kin and Kout

Kin=Correlated Information PeriodKout=Retained Information Period

LIRS (Low Inter-reference Recency Set)

Maintains 2 LRU stacks of different size.Llirs maintains LRU page at least twice recent

ly seen.Lhirs maintains LRU page only once recently s

een.

Works well for IRM, but not for SDD.

FBR (Frequency-Base Replacement)

Divide LRU list into 3 sections;New

Reference count in new section is not incremented.

MiddleOld

Replaces page in old section with smallest reference count.

LRFU (Least Recently/Frequently Used)

1,

0,

otherwise 2

t;at time referenced is if 21

LRU

LFULRFU

xC

xxCxC

Exponential smoothing. λ is hard to tune.

MQ (Multi-Queue)

Uses m LRU queues Q0, Q1, … Qm-1

Qi contains page that have been seen [2i,2i+1) times recently.

Introducing a Class of Replacement Policies

DBL(2c): DouBLe

Maintains 2 variable-sized lists.Holds 2c pages.

cL

cL

cLL

20

0

20

2

1

21

DBL(2c) flow

21 LLxt 2in LMRUxt

cL 1

1in LMRUxt

True

True

False

False

out in 1LLRU cLLLLRU 2 ifout in 212

Most recent c page will always be contained.

: A new class of policy c

ii BMRUTLRU in n recent tha more is in

222 BTL

111 BTL

cTTcLL

BBcLL

2121

2121

FRCp(Fixed Replacement Cache)

FRCp(c) is tuned

We define a parameter p such that.0≤p≤c.

cTT

pcT

pT

cFRCcFRC

cFRC

cFRC

pp

p

p

21

2

1

ccTT

21

FRCp(c) flow

2Tin MRUxt 21 TTxt

Replace

21 BBxt

21 LLxt

Delete

1Tin MRUxt

1Lin LRU

Replace

True

Delete

cLL

LRU

2 if

Bin

21

2

False

21

21

BB

TTxt

cL 1

ARC (Adaptive Replacement Cache)

US Patent 20040098541Assignee Name: IBM Corp.

ARC (Adaptive RC)

Adaptation parameter p∈[0,c]For a given (fixed) value of p,

Exactly same as FRCp.But it learns!

ARC continuously adapts and tunes p.

Learning

We should

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1

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1

,1max,,1max

in hit on increase toby decrease

increase

B

B

B

B

B

B

T

Tp

ARC flow

2Tin MRUxt 21 TTxt

Replace

21 LLxt

Delete

1Tin MRUxt

1Lin LRU

Replace

True

Delete

cLL

LRU

2 if

Bin

21

2

False

22

1

11

2

if ,1max

if ,1max

BxB

Bp

BxB

Bp

p

21 BBxt

cL 1

21

21

BB

TTxt

Scan-Resistant

Long sequence of 1-time-only reads will pass through L1.

Less hits will be encountered in B1 compared to B2.

If hit in B1, T2 will grow by learning.

ARC example

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4T1

B1

B2T2

p=2

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p=3

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0

p=2

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45

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Experiment

Various traces used in this paper.

Experiment: Hit ratio of OLTP

ARCOutperforms online parameter algorithms.Performs as well as offline parameter

algorithms.

Experiment: Hit ratio (cont.)

MQ outperforms LRU, while ARC outperforms all.

Experiment: Hit ratio (cont.)

Comparison for various workloads shows ARC outperforms.

Experiment: Cache size of P1

ARC performs always better than LRU.

Experiment: parameter p

“Dances to the tune being played by workload.”

Summary

ARC Is online and self-tuning.

Is robust for a wider range of workloads.

Has less overhead Is scan-resistant.

Outperforms LRU.