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. Talk Outline. Introduction and Motivation ProWGen: Proxy Workload Generator Tool for Synthetic Web Proxy Workloads

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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

On the Sensitivity of Web Proxy Cache Performance to Workload Characteristics

Mudashiru Busari

Carey Williamson

Department of Computer Science

University of Saskatchewan


Talk outline

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


Introduction

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


Why is the web so slow

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


Our previous work

Our Previous Work

  • Evaluation of Canada’s national Web caching infrastructure for CANARIE’s CA*net II backbone

  • Workload 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


Ca net ii web caching hierarchy dec 1998

CA*net 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


Caching hierarchy overview

Caching Hierarchy Overview

Cache Hit Ratios

Top-Level/International

(20-50 GB)

5-10%

Proxy

(empirically

observed)

Proxy

National

(10-20 GB)

Proxy

15-20%

Regional/Univ.

(5-10 GB)

30-40%

Proxy

Proxy

Proxy

...

...

C

C

C

C

C

C

C


Overview of this paper

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


Research methodology

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


Prowgen key workload characteristics

ProWGen:Key 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]


Prowgen conceptual view

ProWGen (Conceptual View)

ProWGen Software

Input

Parameters

Synthetic

Workload

1

Z

a

c

L


Prowgen conceptual view1

ProWGen (Conceptual View)

Zipf

P

r

ProWGen Software

Input

Parameters

Synthetic

Workload

1

Z

a

c

L


Prowgen conceptual view2

Zipf

P

r

ProWGen (Conceptual View)

ProWGen Software

Input

Parameters

Synthetic

Workload

1

Z

a

c

L


Prowgen conceptual view3

Zipf

LLCD

P

F

r

s

ProWGen (Conceptual View)

ProWGen Software

Input

Parameters

Synthetic

Workload

1

Z

a

c

L


Prowgen conceptual view4

Zipf

LLCD

P

F

Correlation

r

s

-1 0 +1

ProWGen (Conceptual View)

ProWGen Software

Input

Parameters

Synthetic

Workload

1

Z

a

C

L


Prowgen workload modeling details

ProWGen: Workload Modeling Details

  • Modeled workload characteristics

    • One-time referencing

    • Zipf-like referencing behaviour (Zipf’s 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


Validation of prowgen

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


Workload synthesis

Parameter

Value

Total number of requests

Unique documents (of total requests)

One-timers (of unique documents)

Zipf slope

Tail Index

Documents in the tail

Beginning of the tail (bytes)

Mean of the lognormal file size distribution

Standard deviation

Correlation between file size and popularity

LRU Stack Model for temporal locality

LRU Stack Size

5,000,000

34%

72%

0.807

1.322

22%

10,000

7,000

11,000

Zero

Static and Dynamic

1,000

Workload Synthesis


Zipf like referencing behaviour

Zipf-like Referencing Behaviour

Empirical Trace Slope = 0.81

Synthetic Trace Slope = 0.83


Transfer size distribution

References

Bytes transferred

Transfer Size Distribution


Simulation evaluation of single level web proxy caches some research questions

Simulation Evaluation ofSingle-Level Web Proxy Caches:Some 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?


On the sensitivity of web proxy cache performance to workload characteristics

Simulation Model

Aggregate Workload

Proxy server

Web Servers

Web Clients


Experimental design factors and levels

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


Performance metrics

Performance Metrics

  • Document Hit Ratio

    • Percent of requested docs found in cache (HR)

  • Byte Hit Ratio

    • Percent of requested bytes found in cache (BHR)


Simulation results preview

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


Sensitivity to one timers lru

Sensitivity to One-timers (LRU)

(a) Doc Hit Ratio

(a) Byte Hit Ratio


Sensitivity to zipf slope lru

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


Sensitivity to heavy tail index lru replacement policy

Sensitivity to Heavy Tail Index (LRU Replacement Policy)

(a) Doc Hit Ratio

(b) Byte Hit Ratio


On the sensitivity of web proxy cache performance to workload characteristics

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


Sensitivity to correlation lru

Sensitivity to Correlation (LRU)

(a) Doc Hit Ratio

(a) Byte Hit Ratio


On the sensitivity of web proxy cache performance to workload characteristics

Sensitivity to Temporal Locality (LRU)

(a) Doc Hit Ratio

(b) Byte Hit Ratio


Summary single level caches

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


Conclusions

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


Future work

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


For more information

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: [email protected]

    • http://www.cs.usask.ca/faculty/carey/


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