Simulation Evaluation of Web Caching Architectures

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Simulation Evaluation of Web Caching Architectures

• Carey Williamson
• Mudashiru Busari
• Department of Computer Science
• University of Saskatchewan

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Outline

• Introduction Web Caching
• Proxy Workload Generator (ProWGen)
• Evaluation of Single-Level Caches
• Evaluation of Multi-Level Caches
• Conclusions and Future Work
• Questions?

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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?

• Three main possible reasons
• Client-side bottlenecks (PC, modem)
• Solution better access technologies (TRLabs)
• 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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What is a Web proxy cache?

• Intermediary between Web clients (browsers) and
  Web servers
• Controlled Internet access point for an
  institution or organization (e.g., firewall)
• Natural point for Web document caching
• Store local copies of popular documents
• Forward requests to servers only if needed

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Web Caching Proxy
Web Server
Web Server
Internet
Region or Organization Boundary
Proxy
Web Clients
C
C
C
C
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Some Technical Issues

• Size of cache
• Replacement policy when cache is full
• Cache coherence (Get-If-Modified)
• Some content is uncacheable
• Multi-cache coordination, peering (ICP)
• Security and privacy hit metering
• Other issues...

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Our Previous Work

• Collaborative project with CANARIE, through the
  Advanced Networks Applications program
  (July98-June99)
• Design and evaluation of Web caching strategies
  for Canadas CAnet II backbone (National Web
  Caching Infrastructure)
• For more information, see URL http//www.cs.usask.
  ca/faculty/carey/projects/nwci.html

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CAnet II Web Caching Hierarchy (Dec 1998)
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CAnet II Web Caching Hierarchy (Dec 1998)
(selected measurement points for our traffic
analyses 3-6 months of data
from each)
USask
CANARIE (Ottawa)
To NLANR
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Caching Hierarchy Overview
Top-Level/International (20-50 GB)
Cache Hit Ratios
Proxy
5-10
(empirically observed)
Proxy
National (10-20 GB)
Proxy
15-20
Regional/Univ. (5-10 GB)
Proxy
Proxy
Proxy
30-40
...
...
C
C
C
C
C
C
C
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NWCI Project Contributions

• Workload characterization and evaluation of
  CAnet II Web caching hierarchy (IEEE
  Network, May/June 2000)
• Developed Web proxy caching simulator for
  trace-driven simulation evaluation of Web proxy
  caching hierarchies
• Recommendations for CANARIE NWCI about
  configuration of future caches

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Overview of This Talk

• Constructed synthetic Web proxy workload
  generation tool (ProWGen) that captures the
  salient characteristics of empirical Web proxy
  workloads
• Use ProWGen to evaluate sensitivity of proxy
  caches to workload characteristics
• Use ProWGen to evaluate effectiveness of
  multi-level Web caching hierarchies (and
  cache management techniques)

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Research Methodology

• Design, construction, and parameterization of
  workload models
• Validation of ProWGen (statistically, and versus
  empirical workloads)
• Simulation evaluation of single cache
• Sensitivity to workload characteristics
• Different cache sizes, replacement policies
• Simulation evaluation of multi-level cache
• Sensitivity to workload characteristics
• Novel (heterogeneous) cache management policies

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Key Workload Characteristics

• One-timers (60-70 useless!!!)
• Zipf-like document referencing popularity
• Heavy-tailed file size distribution (i.e., most
  files small, but most bytes are in big files)
• Correlations (if any) between document size and
  document popularity (debate!)
• Temporal locality (temporal correlation between
  recent past and near future references) Mahanti
  et al. 2000

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ProWGen Conceptual View
ProWGen Software
Input Parameters
Synthetic Workload
1
Z
a
c
L
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ProWGen Conceptual View
Zipf
P
r
ProWGen Software
Input Parameters
Synthetic Workload
1
Z
a
c
L
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ProWGen Conceptual View
ProWGen Software
Input Parameters
Synthetic Workload
1
Z
a
c
L
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ProWGen Conceptual View
ProWGen Software
Input Parameters
Synthetic Workload
1
Z
a
c
L
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ProWGen Workload Modeling Details

• Modeled workload characteristics
• One-time referencing
• Zipf-like referencing behaviour (Zipfs Law)
• File size distribution
• Body lognormal distribution
• Tail Pareto Distribution
• Correlation between file size and popularity
• Temporal locality
• Static probabilities in finite-size LRU stack
  model
• Dynamic probabilities in finite-size LRU stack
  model

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Validation of ProWGen

• To establish that the synthetic workloads possess
  the desired characteristics (quantitative and
  qualitative), and that the characteristics are
  similar to those in empirical workloads

• Example analyze 5 million requests from a proxy
  server trace and parameterize ProWGen to generate
  a similar workload

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Workload Synthesis
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Zipf-like Referencing Behaviour
Empirical Trace Slope 0.81
Synthetic Trace Slope 0.83
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Transfer Size Distribution
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Research QuestionsSingle-Level Caches

• In a single-level proxy cache, how sensitive is
  Web proxy caching performance to certain workload
  characteristics (one-timers, Zipf-ness,
  heavy-tail index)?
• How does the degree of sensitivity change
  depending on the cache replacement policy?

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Simulation Model
Web Servers
Web Clients
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Factors and Levels

• Cache size
• Cache Replacement Policy
• Recency-based LRU
• Frequency-based LFU-Aging
• Size-based GD-Size
• Workload Characteristics
• One-timers, Zipf slope, tail index, correlation,
  temporal locality model

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Performance Metrics

• Cache hit ratio
• Percent of requested docs found in cache (HR)
• Percent of requested bytes found in cache (BHR)
• User response time
• Estimated analytically using request rates, cache
  hit ratios, and (relative) cache miss penalties

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Simulation Results (Preview)

• Cache performance is very sensitive to
• Slope of Zipf-like doc referencing popularity
• Temporal locality property
• Correlations between size and popularity
• Cache performance relatively insensitive to
• Tail index of heavy-tailed file size distribution
• One-timers

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Sensitivity to One-timers (LRU)
(a) Hit Ratio
(a) Byte Hit Ratio
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Sensitivity to Zipf Slope (LRU)
Difference of 0.2 in Zipf slope impacts
performance by as much as 10-15 in hit ratio
and byte hit ratio
(a) Hit Ratio
(b) Byte Hit Ratio
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Sensitivity to Heavy Tail Index (LRU Replacement
Policy)
(a) Hit Ratio
(b) Byte Hit Ratio
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Sensitivity to Heavy Tail Index (GD-Size
Replacement Policy)
Difference of 0.2 in heavy tail index impacts
performance by less than 3
(a) Hit Ratio
(a) Byte Hit Ratio
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Sensitivity to Correlation (LRU)
(a) Hit Ratio
(a) Byte Hit Ratio
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Sensitivity to Temporal Locality (LRU)
(a) Hit Ratio
(b) Byte Hit Ratio
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Summary Single-Level Caches

• Cache performance is sensitive to
• Slope of Zipf-like document referencing
  popularity
• Temporal locality
• Correlation between size and popularity

• Cache Performance is insensitive to
• Tail index of heavy-tailed file size
  distribution
• One-timers

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Multi-Level Caching...

• Workload characteristics change as you move up
  the Web caching hierarchy (due to filtering
  effects, aggregation, etc)
• Idea 1 Try different cache replacement policies
  at different levels of hierarchy
• Idea 2 Limit replication of cache content in
  overall hierarchy through partitioning (size,
  type, sharing,)

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Research QuestionsMulti-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
Web Servers
Web Clients
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Experiment 1 Different Policies at Different
Levels of the hierarchy
(a) Hit Ratio
(b) Byte Hit Ratio
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Experiment 2 Shared files at the upper level of
the hierarchy
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Experiment 3 Size-based Partitioning

• Partition files across the two levels based on
  sizes (e.g., keep small files at the lower level
  and large files at the upper level) (or vice
  versa)
• Three size thresholds
• 5,000 bytes
• 10,000 bytes
• 100,000 bytes

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Small files at the lower level Large files at
the upper level
Parent
Size threshold 5,000 bytes
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Large files at the lower level Small files at
the upper level
Size threshold 5,000 bytes
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Summary Multi-Level Caches

• Different 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

• Sharing-based approach
• no benefit compared to the other cases studied

• 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

• ProWGen is a valuable tool for the evaluation of
  Web proxy caching architectures, using synthetic
  workloads
• Existing multi-level caching hierarchies are not
  always that effective
• Heterogeneous caching architectures may better
  exploit workload characteristics and improve Web
  caching performance

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Future Work

• Extend ProWGen
• model response time
• model file size modifications

• Extend the multi-level experiments
• look into configurations where there is
  communication between the lower level proxies
• investigate configurations involving more levels
  and and more lower level proxies

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For More Information...

• M. Busari, Simulation Evaluation of Web Caching
  Hierarchies, M.Sc. Thesis, June 2000
• Two papers available soon (under review)
• ProWGen tool is available now
• Email carey_at_cs.usask.ca
• http//www.cs.usask.ca/faculty/carey/