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Hala ElAarag
Web Proxy Cache
Replacement Strategies
Simulation, Implementation,
and Performance Evaluation
With Contributions by Sam Romano and Jake Cobb
123
HalaElAarag
Department of Mathematics
and Computer Science
Stetson University
DeLand, FL
USA
ISSN 2191-5768 ISSN 2191-5776 (electronic)
ISBN 978-1-4471-4892-0 ISBN 978-1-4471-4893-7 (eBook)
DOI 10.1007/978-1-4471-4893-7
SpringerLondonHeidelbergNewYorkDordrecht
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(cid:2)HalaElAarag2013
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To my loving parents, husband, and children
Preface
Theneedforthisbookstemsfromthesheeramountofmodernwebtrafficcoupled
with increases in cacheable, bandwidth-consuming multimedia objects. In this
book, we provide the most comprehensive study for proxy cache replacement
strategies. We categorize these strategies into four categories; recency-based,
frequency-based, recency/frequency-based, and function-based. We provide a
quantitativecomparisonofcachereplacementstrategiesonthecategoryleveland
then compare the best strategies of each category based on very important per-
formance metrics. We then diverge from these replacement policies by con-
structing a model, represented in the weights and structure of a neural network,
from actual web traffic. This approach has the advantage of incorporating subtle
traffic pattern information which may be difficult for a human to discern when
designing a top-down algorithm. Furthermore, we provide a single method which
is capable of creating multiple models; this allows models to be created which
target localized traffic patterns as well as general ones. We first provide the
simulationarchitecture,setup,parameters,andresultsofthisnoveltechniquethen
we explain the implementation details in the Squid proxy server.
This book is based on a number of my publications co-authored with my
students Sam Romano, now a software engineer at Google and Jake Cobb, now a
Ph.D.studentatGeorgiaInstituteofTechnology. Iwouldliketothankthemboth
for their contributions to this research. I would like to thank the anonymous
reviewersofSimulation:TransactionsoftheSocietyforModelingandSimulation
International, Sage Publication, The Journal of Systems and Software, Elsevier,
andNeuralComputingandApplications,Springer-Verlag,fortheircommentsand
critique that helped to improve the quality of this research. I would also like to
thank the staff members of Springer, Wayne Wheeler for introducing me to the
SpringerBriefseriesandSimonRees,forthefollowupthroughthepreparationof
this book.
Last, but not least, I am deeply grateful to my family for their continuous
support and encouragement.
vii
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Background Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 Web Request. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Characteristics of Web Objects. . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Web Proxy Servers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.4 Squid. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1 Firing Rule/Squashing Function. . . . . . . . . . . . . . . . . . . . . . . . 12
3.2 Multi-Layer Perceptrons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.4 Objective Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.5 Backpropagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4 A Quantitative Study of Web Cache Replacement Strategies
Using Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2 Replacement Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.3 Simulation Details. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.3.1 Web Cache Components . . . . . . . . . . . . . . . . . . . . . . . 19
4.3.2 Simulation Architecture. . . . . . . . . . . . . . . . . . . . . . . . 20
4.3.3 Details of Implemented Strategies. . . . . . . . . . . . . . . . . 20
4.3.4 Other Implementation Details. . . . . . . . . . . . . . . . . . . . 28
4.4 Performance Metrics and Cost Models. . . . . . . . . . . . . . . . . . . 30
4.4.1 Performance Metrics. . . . . . . . . . . . . . . . . . . . . . . . . . 30
4.4.2 Cost Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
ix
x Contents
4.5 Experiment Setup and Results. . . . . . . . . . . . . . . . . . . . . . . . . 32
4.5.1 Trace Files. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.5.2 Simulation Setup and Parameters . . . . . . . . . . . . . . . . . 33
4.5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5 Web Proxy Cache Replacement Scheme Based
on Backpropagation Neural Network . . . . . . . . . . . . . . . . . . . . . . 61
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.2 Neural Network Proxy Cache Replacement . . . . . . . . . . . . . . . 62
5.3 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.3.1 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.3.2 Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.3.3 Performance Evaluation. . . . . . . . . . . . . . . . . . . . . . . . 67
5.4 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.4.1 Training. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.4.2 Proxy Cache Simulation. . . . . . . . . . . . . . . . . . . . . . . . 70
5.4.3 Neural Network Input/Output Relationships. . . . . . . . . . 74
5.4.4 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.5 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6 Implementation of a Neural Network Proxy Cache Replacement
Strategy in the Squid Proxy Server. . . . . . . . . . . . . . . . . . . . . . . . 83
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.2 Squid’s Implemented Cache Replacement Strategies . . . . . . . . . 84
6.3 NNPCR-2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
6.3.1 NNPCR-2 Implementation Details. . . . . . . . . . . . . . . . . 85
6.3.2 NNPCR-2 Training Setup . . . . . . . . . . . . . . . . . . . . . . 88
6.3.3 Implementation of NNPCR-2 in Squid 3.1. . . . . . . . . . . 89
6.4 NNPCR Training Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
6.4.1 Effect of Hidden Nodes and Input Nodes. . . . . . . . . . . . 91
6.4.2 Effect of the Sliding Window Length . . . . . . . . . . . . . . 93
6.4.3 Effect of the Learning Rate . . . . . . . . . . . . . . . . . . . . . 95
6.5 Emulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.6 Emulation Results and Observations . . . . . . . . . . . . . . . . . . . . 98
6.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Chapter 1
Introduction
Keywords Web proxy servers (cid:2) Proxy cache (cid:2) Cache replacement strategies (cid:2)
Neural networks (cid:2) Squid proxy server
With the debut of Web 2.0, many researchers are interested in studying their
workload characteristics in order to design more efficient servers. YouTube is an
exampleofaverypopular Web2.0site, andhence,itwasthe choice formanyto
conduct such research [1]. Gill et al. [1] conducted an extensive analysis of
YouTube workload and observed 25-million YouTube transactions over a three-
month period that included the downloads of 600,000 videos. They compared
traditional Web workload characteristics to that of YouTube and found many
similarities.TheyconcludedthattrafficcharacterizationofYouTubesuggeststhat
caching popular video files on Web proxy servers reduces network traffic and
increases scalability of YouTube servers [1].
User-perceived delay results from both overload of individual servers and net-
work congestion. Proxy caches are used to address both issues by attempting to
serverequestslocally.Thereareseveraldecisionsthatmustbemadesuchascache
placementandreplacement.Ourmainfocuswillbethecachereplacementproblem,
theprocessofevictingobjectsinthecachetomakeroomfornewobjects,appliedto
Webproxyservers.Proxyserverswanttoserveasmanyobjectsfromthecacheas
possible,serveasmuchdatafromthecacheaspossible,orboth.Optimizingbothis
ideal, butmany practical algorithms optimizefor oneover the other.
There are many replacement strategies to consider when designing a proxy
server. The most commonly known cache replacement strategies are least
frequentlyused(LFU)andleastrecentlyused(LRU).Podlipnigetal.[2]provided
asurveyofWebcachereplacementstrategies.Theyhavedonewelltonotonlylist
well-known strategies, but also categorize the strategies into five groups.
Althoughbothasurveyandclassificationofthesestrategiesareavailable,thereis
not a known record of results comparing the majority of the cache replacement
H.ElAarag,WebProxyCacheReplacementStrategies, 1
SpringerBriefsinComputerScience,DOI:10.1007/978-1-4471-4893-7_1,
(cid:2)HalaElAarag2013
2 1 Introduction
algorithms.BalamashandKunz[3]comparedcachereplacementpoliciesqualita-
tively rather than quantitatively. Many of the works consulted for this research
presentedresultsfordifferentstrategiesagainst,atmost,threetofiveotherstrategies.
However, their results cannot be compared effectively because most literature
devisedtheirexperimentswithdifferencesintheirimplementations,suchastheuse
ofauxiliarycaching,representationofWebobjectcharacteristics.Thereisalsothe
differenceintracefilesbetweenexperimentsandalargerangeofdifferentsizesused
for the cache space. Lastly, different proposals used different criteria on when to
cache an object such as ignoring PHP files, cgi-bin scripts, POST requests, and
simplyjustusingallrequestspresentedinaparticulartracefile.
Inspiteofthemanycachereplacementpoliciesproposedinthepastyears,the
most popular Web proxy software, Squid, uses least recently used as its default
strategy.Webelievethatthisisbecausetherehasnotbeenacomprehensivestudy
presentedthatcomparesthesestrategiesquantitatively.AsWong[4]mentions‘‘all
policies were purported to perform better than others, creating confusion as to
which policy should be used’’.
In this book, we present a study of cache replacement strategies designed for
static Web content, as opposed to dynamic Web content seen in some Web 2.0
applications. Most caching that occurs with dynamic content occurs on the
browsersideanddoesnotoccurfromthestandpointofproxyservers.Asaresult,
these strategieshave notbeen considered.Wehave researched how proxyservers
can still improve performance by caching static Web content such as cascading
style sheets, java script source files, and most importantly larger files such as
images (jpeg, gif, etc.).
This topic is particularly important in wireless ad hoc networks. In such net-
works, mobile devices act as proxy servers for a group of other mobile devices.
However, they have limited storage space. The extensive research on cache
replacementpoliciesweprovideinthisbookiscrucialforsuchenvironmentswith
small cache sizes and limited battery life.
Neural networks have been employed in a number of applications, particularly
intheareaofpatternrecognition.Neuralnetworksareabletolearnbyexperience
andholdaninternalrepresentationofmeaningindata.Anappropriatelystructured
neuralnetworkwillbeabletogeneralizetheknowledgeacquiredfromtrainingto
data that lies outside the training set. This property makes neural networks useful
forsolvingproblemsthatcontainuncertaintyorhaveaproblemspacewhichistoo
large for an exhaustive search. We use neural networks to solve the Web proxy
cache replacement problem. A bottom-up approach is justified by the heavy var-
iabilityinWebtrafficwhichmakesgeneralcharacterizationofthattrafficdifficult.
Neuralnetworksareselectedfortheirstrengthinpatternrecognitioninnoisydata
sets. Additionally, they can learn from example by training against a data set, yet
are able to generalize beyond the training set. Thus, they are well suited for
developing a general replacement strategy from a set of data samples. This
approach has the advantage of incorporating subtle traffic pattern information
which may be difficult for a human to discern when designing a top-down
algorithm.
