On the Sensitivity of Web Proxy Cache Performance to Workload Characteristics

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On the Sensitivity of Web Proxy Cache Performance
to Workload Characteristics

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

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Talk Outline

• Introduction and Motivation
• ProWGen Proxy Workload Generator
• Tool for Synthetic Web Proxy Workloads
• Simulation Study
• Simulation Evaluation of Web Proxy Caches
• Conclusions and Future Work

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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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Our Previous Work

• Evaluation of Canadas national Web caching
  infrastructure for CANARIEs CAnet II backbone
• 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 architectures

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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
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C
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Overview of This Paper

• 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 selected Web proxy workload
  characteristics

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Research Methodology

• Design, construction, and parameterization of
  aggregate workload models, based on empirical
  traces (Web proxy access logs)
• Validation of ProWGen (statistically, and versus
  empirical workloads)
• Simulation evaluation of single-level caches
• Sensitivity to workload characteristics
• Effect of cache size
• Effect of cache replacement policy

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ProWGenKey Workload Characteristics

• One-timers (60-70 docs are 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. Perf.Eval. 2000

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ProWGen (Conceptual View)
ProWGen Software
Input Parameters
Synthetic Workload
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a
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ProWGen (Conceptual View)
Zipf
P
r
ProWGen Software
Input Parameters
Synthetic Workload
1
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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 (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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Simulation Evaluation ofSingle-Level Web Proxy
CachesSome Research Questions

• In a single-level proxy cache, how sensitive is
  Web proxy caching performance to certain workload
  characteristics (one-timers, Zipf slope,
  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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Experimental Design Factors and Levels

• Cache size
• 1 MB to 32 GB
• 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

• 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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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
• One-timers
• Tail index of heavy-tailed file size distribution

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Sensitivity to One-timers (LRU)
(a) Doc 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) Doc 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) Doc Hit Ratio
(a) Byte Hit Ratio
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Sensitivity to Temporal Locality (LRU)
(a) Doc 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 (steeper slope implies better caching)
• Temporal locality
• Correlation between size and popularity

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

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Conclusions

• ProWGen is a useful tool for the generation of
  synthetic Web proxy workloads for the evaluation
  of Web proxy caches and Web proxy caching
  architectures
• Web proxy cache performance is quite sensitive to
  Zipf slope, temporal locality, and correlations
  (if any) between document size and document
  popularity

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Future Work

• Extend and improve ProWGen
• Request arrival process (timestamps)
• File modifications, types, and lifetimes
• Web page structure (spatial locality)
• Scaling the workload model(s)...
• Evaluate multi-level Web proxy caches
• Port to network emulation testbed

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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
• ProWGen tool
• http//www.cs.usask.ca/faculty/carey/software/
• Email carey_at_cs.usask.ca
• http//www.cs.usask.ca/faculty/carey/