evaluating content management techniques for web proxy caches

Internet ServerInternet ServerEvaluating Content Evaluating Content Management Techniques for Management Techniques for

Web Proxy CachesWeb Proxy Caches

Cho Joon-ho(CA Lab, CS department, KAIST)Cho Joon-ho(CA Lab, CS department, KAIST)

2001 . 11. 62001 . 11. 6

Martin Arlitt, Ludmila Cherkasova, John Diley, Rich Friedrich and Tai Jin (Hewlett-Packard Laboratories)

(in 2nd Workshop on Internet Server Performance, in conjunction with ACM SIGMETRICS 99)

Evaluating Content Management Tech for Web Proxy CachesEvaluating Content Management Tech for Web Proxy Caches 22 / 20 / 20

AgendaAgenda

ProblemsProblemsQuick Tour (Summary)CritiqueDesign & Design Rationale

Data Collection and ReductionKey Workload CharacteristicsExperimental Design

Simulation ResultsVirtual Cache


ProblemsProblems

Current Web Proxy caches utilize simple Current Web Proxy caches utilize simple replacement policiesreplacement policies

Relatively low hit ratesRelatively low hit rates

Additional delaysAdditional delays

So what?Developing a quantitative understanding of Web traffic

How effective are current proxy cache replacement policies for real workloads?Focus on two performance metrics

Hit rate

Byte hit rate

Designing new replacement policiesUtilize frequency for higher performanceAre neither susceptible to cache pollution nor require parameterization


AgendaAgenda

Problems

Quick Tour (Summary)Quick Tour (Summary)CritiqueDesign & Design Rationale




Quick Tour (Summary) Quick Tour (Summary) – 1/3– 1/3

The problems of existing studiesShort-term traces of busy proxies or long-term traces of relatively inactive proxies

Long-term traces in busy environments are neededLong-term traces in busy environments are needed

Trace driven simulationCollect total 117,652,652 requests during five monthUse smaller and more compact log

The points to be consideredObject sizeRecency of ReferenceFrequency of ReferenceTurnover



Existing replacement policyExisting replacement policyLRULRU (Least-Recently-Used)

SizeSize – replaces the largest object

GD-SizeGD-Size (GreedyDual-Size)Replaces the object with the lowest utility

LFULFU - replaces the least frequently used object

New replacement policyNew replacement policyGDSFGDSF (GreedyDual-Size with Frequency)

GD-Size + a frequency factor

LFU-DALFU-DA (Least Frequently Used with Dynamic Aging)LFU-Aging + a dynamic mechanism(Running age L)

Virtual CachesVirtual CachesLogically partitions the cache into N virtual caches

Ki=Ci/Si+L

Ki=Fi*Ci/Si+L

Ki=Ci*Fi+L



Analysis of Virtual Cache Performance; VC0 using GDSF-Hits, VC1 using LFU-DA

Comparison of Proposed Policies to Existing Replacement Policies


AgendaAgenda

ProblemsQuick Tour (Summary)

CritiqueCritiqueDesign & Design Rationale




CritiqueCritique

ProsQuantitative understanding of Web traffic

Long term trace-driven simulation in busy proxy servers

Providing two new replacement algorithms that run efficientlyProviding a new cache management method, ‘Virtual Cache’

ConsNot freshNo consideration of dynamic dataNo consideration of processing overhead for these more complex algorithmsPerformance improvements are insignificant


AgendaAgenda

ProblemsQuick Tour (Summary)Critique

Design & Design RationaleDesign & Design Rationale

Data Collection and ReductionData Collection and Reduction

Key Workload CharacteristicsKey Workload Characteristics

Experimental DesignExperimental DesignSimulation ResultsVirtual Cache


Data Collection and ReductionData Collection and Reduction

Data collectionLong term trace-driven simulationTotal 117,652,652 requests were handled during five month periodData include

Client IP address, request time, response status, the time required for the proxy to complete its response…

Data reductionSmaller, more compact log

Due to storage constraintTo ensure that analyses and simulations could be completed in a reasonable amount of time

Reduction by Storing data in more efficient mannerRemoving information of little value


Key Workload CharacteristicsKey Workload Characteristics

Cacheable ObjectsMost client requests be for cacheable objects (96%)

Object Set Size total 389GB

Object SizesVariable – medium : 4KB, maximum : 148MB video clip

Recency of reference1/3 of all re-references occurred within one hour

Frequency of referenceWeb referencing patterns are non-uniform

TurnoverObjects that were once popular are no longer requested


Experimental Design Experimental Design – 1/2– 1/2

Least-Recently-Used(LRU)Replaces the object requested least recentlyConsiders only a single work load characteristic

SizeReplaces the largest objectTries to minimize the miss ratio (target to byte hit rate)Cache pollution

GreedyDual-Size(GD-Size)

GD-Size(1) for Hit RateGD-Size(Packets) for Byte Hit Rate

Ki=Ci/Si+LCi – the cost associated with bringing object i into the cache

Si – the object size

L – a running age factor


Experimental Design Experimental Design – 2/2– 2/2

LFUReplaces the least frequently used objectLFU-Aging = LFU + Aging → avoids cache pollutionParameterization problem still remains

Greedy Dual-Size with Frequency(GDSF)GD-Size doesn’t take into account frequency

Least Frequently Used with Dynamic Aging(LFU-DA)

LFU-Aging requires parameterization to perform wellLFD-DA uses inflation factor as well as the frequency count

Ki=Fi*Ci/Si+L Fi – a frequency count

Ki=Ci*Fi+L

L – a running age factor


AgendaAgenda

ProblemsQuick Tour (Summary)CritiqueDesign & Design Rationale


Simulation ResultsSimulation ResultsVirtual Cache


Simulation Results Simulation Results – 1/2– 1/2

Figure1. Comparison of existing Replacement Policies


Simulation Results Simulation Results – 2/2– 2/2

Figure2. Comparison of Proposed Policies to Existing Replacement Policies


AgendaAgenda

ProblemsQuick Tour (Summary)CritiqueDesign & Design Rationale


Simulation Results

Virtual CacheVirtual Cache


Virtual Cache Virtual Cache – 1/2– 1/2

An approach that can focus on both of An approach that can focus on both of hit ratehit rate and and byte hit ratebyte hit rate simultaneously simultaneously

MechanismLogically partitions the cache into N virtual cachesEach virtual cache(VC)is managed with its own replacement policySteps

Initially all objects are in VC0

Replacements from VCi are moved to VCi+1

Replacements from VCi+1 are evicted form the cache

When reaccessed, objects are reinserted in VC0


Virtual Cache Virtual Cache – 2/2– 2/2

Figure 4. Analysis of Virtual Cache Performance; VC0 using LFU-DA, VC1 using GDSF-Hits

Figure 3. Analysis of Virtual Cache Performance; VC0 using GDSF-Hits, VC1 using LFU-DA

evaluating content management techniques for web proxy caches

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