On Filter Effects in Web Caching Hierarchies

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On Filter Effects inWeb Caching Hierarchies

Carey WilliamsonDepartment of Computer ScienceUniversity of Calgary

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Introduction

“The Web is both a blessing and a curse…”

Blessing: Internet available to the masses Seamless exchange of information

Curse: Internet available to the masses Stress on networks, protocols, servers, users

Motivation: techniques to improve the performance and scalability of the Web

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Why is the Web so slow?

Client-side bottlenecks (PC, modem) Solution: better access technologies

Server-side bottlenecks (busy Web site) Solution: faster, scalable server designs

Network bottlenecks (Internet congestion) Solutions: caching, replication; improved

protocols for client-server communication

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Example of a Web Proxy Cache

Proxy server

Web server

Web server

Web server

Web Client

Web Client

Web Client

Web Client

Web Client

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Our Previous WorkEvaluation of Canada’s national Web caching

infrastructure for CANARIE’s CA*net II backboneWorkload characterization and evaluation of

CA*net II Web caching hierarchy (IEEE Network, May/June 2000)

Developed Web proxy caching simulator for trace-driven simulation evaluation of Web proxy caching architectures

Developed synthetic Web proxy workload generator called ProWGen [Busari/Williamson INFOCOMM 2001]

CA*net II Web Caching Hierarchy (Dec 1998)

USask

CANARIE(Ottawa)

(selectedmeasurement points for our traffic analyses; 6-9 months of data from each)

To NLANR

Caching Hierarchy Overview

C C C CC C C

Proxy

Proxy

Proxy Proxy

Proxy

Proxy

... ...

Regional/Univ.(5-10 GB)

National(10-20 GB)

Top-Level/International(20-50 GB)

Cache Hit Ratios

30-40%

15-20%

5-10%(empiricallyobserved)

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Some Observationson Multi-Level Caching...

Caching hierarchy not very effective, due to a “diminishing returns” effect

Reason: workload characteristics change as you move up the caching hierarchy (due to filtering effects, etc)

Bigger caches aren’t really the answerBetter caching system design might be...

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Research Goals

Develop better understanding of cache filter effects (intuitively, quantitatively)

Try to do something about it!Idea #1: Try different cache

replacement policies at different levels of hierarchy

Idea #2: Try partitioning cache content in overall hierarchy based on size or type to limit replication, etc.

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Talk Overview

Background/MotivationUnderstanding Cache Filtering

EffectsExploiting Cache Filtering EffectsSummary and Conclusions

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Part I: UnderstandingCache Filter Effects

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Simulation Model

Proxy server

Web Servers

Web Clients

Proxy server

Proxy server

Upper Level (Parent)

Lower Level (Children)

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Experimental MethodologyTrace-driven simulation (empirical

traces)Multi-factor experimental designCache size

1 MB to 32 GBCache Replacement Policy

Recency-based LRU (currently active docs) Frequency-based LFU-Aging (popular docs) Size-based GD-Size (favours smaller docs)

Analyze workload characteristics

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Web Workload Characteristics“One-timers” (60-70% docs are useless!!!)Zipf-like document referencing popularityHeavy-tailed file size distribution (i.e.,

most files small, but most bytes are in big files)

Zero correlations between document size and document popularity (debate!)

Temporal locality (temporal correlation between recent past and near future references) [Mahanti et al. PER 2000]

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Zipf-Like Referencing

An intrinsic “power-law” relationship in the way that humans organize, access, and use information (e.g., library books, English words in text, movie rentals, Web sites, Web pages, ...)

Plot item popularity versus relative rank, on a log-log scale, results in straight line

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Example: Zipf-Like Document Popularity Profile for UofS Trace

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Quiz Time: What do you get AFTER the cache?

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(a)


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(a)


(b)

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(a)


(b)

(c)

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(a)


(b)

(c) (d)

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(c)

Answer: (c)

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Simulation Results for Input Workload Traceswith Different Initial Zipf Slopes

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The Magnitude of the Filter Effect Depends on Cache Size

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Filter Effect Depends on Cache Replacement Policy

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Filter Effect is Most Pronounced at First-Level Cache

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Part II: ExploitingCache Filter Effects

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Research Questions:Multi-Level Caches

In a multi-level caching hierarchy, can overall caching performance be improved by using different cache replacement policies at different levels of the hierarchy?

In a multi-level caching hierarchy, can overall performance be improved by keeping disjoint document sets at each level of the hierarchy?

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Simulation Model

Proxy server

Web Servers

Web Clients

Proxy server

Proxy server

Upper Level (Parent)

Complete Overlap

No Overlap

Partial Overlap (50%) Lower Level

(Children)

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Performance Metrics

Document Hit Ratio Percent of requested docs found in

cache (HR)Byte Hit Ratio

Percent of requested bytes found in cache (BHR)

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010203040506070

1 100 10000Cache Size (MB)

Byte

Hit

Ratio

Children (LRU) Parent (GD-Size)

Parent (LFU-Aging) Parent (LRU)

01020304050607080


Hit R

atio



Experiment 1: Different Policies at Different Levels of the hierarchy

(a) Hit Ratio (b) Byte Hit Ratio

Parent Parent

Children

Children

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01020304050607080


Hit

Rat

ioChildren (LRU) Parent (GD-Size)


Parent

Children

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Experiment 2:Sensitivity to Workload Overlap

The greater the degree of workload overlap amongst the child proxies, the greater the role for the parent cache

In the “no overlap” scenario, the parent cache has negligible hit ratios, particularly when child caches are large

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Experiment 3:Size-based Partitioning

Partition files across the two levels of the hierarchy based on size (e.g., keep small files at the lower level and large files at the upper level) (or vice versa)

Three size thresholds for “small”... 5,000 bytes 10,000 bytes 100,000 bytes

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010203040506070


Hit

Ra

tio


Parent (LRU) Parent (LFU-Aging)

010203040506070


Byt

e H

it R

ati

o


Parent (GD-Size) Children (LRU)

Size threshold = 5,000 bytes

010203040506070


Hit

Ra

tio



010203040506070


Byt

e H

it R

ati

o




Small files at the lower level; Large files at the upper level

Parent

Children

41Size threshold = 5,000 bytes

010

2030

4050

6070


Hit

Ra

tio

Parent (GD-Size) Parent (LRU)

Parent (LFU-Aging) Children (LRU)

010203040506070


Byt

e H

it R

ati

o

Children (LRU) Parent (LRU)

Parent (LFU-Aging) Parent (GD-Size)


0

1020

3040

5060

70


Hit

Ra

tio

Parent (GD-Size) Parent (LRU)

Parent (LFU-Aging) Children (LRU)

010203040506070


Byt

e H

it R

ati

o



ChildrenParent

Large files at the lower level; Small files at the upper level

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Summary: Multi-Level CachesDifferent Policies at different levels

LRU/LFU-Aging at the lower level + GD-Size at the upper level provided improvement in performance

GD-Size + GD-Size provided better performance in hit ratio, but with some penalty in byte hit ratio

Size-threshold approach small files at the lower level + large files at the

upper level provided improvement in performance reversing this policy offered no perf advantage

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Conclusions

Existing multi-level caching hierarchies are not always that effective, due to cache filtering effects

“Heterogeneous” caching architectures may better exploit workload characteristics and improve Web caching performance

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For More Information...

M. Busari, “Simulation Evaluation of Web Caching Hierarchies”, M.Sc. Thesis, Dept of Computer Science, U. Saskatchewan, June 2000

C. Williamson, “On Filter Effects in Web Caching Hierarchies”, ACM Transactions on Internet Technology, 2002 (to appear).

Email: [email protected] http://www.cpsc.ucalgary.ca/~carey/

Documents

On Filter Effects in Web Caching Hierarchies