IEEE 7th Annual Workshop on Workload Characterization

The The USARUSARCharacterization ModelCharacterization Model

Adriano Pereira, Gustavo Gorgulho, Leonardo Silva, Wagner Meira Jr., and Walter Santos

{adrianoc,gorgulho,leosilva,meira,walter}@dcc.ufmg.br

Department of Computer ScienceFederal University of Minas Gerais (UFMG)

Belo Horizonte - Brazil

October, 2004

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Presentation IndexPresentation Index

• Introduction

• Goals

• Methodology– Characterization Model– Validation Model

• Case Study

• Conclusion and Ongoing Work

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IntroductionIntroduction

• Understanding the nature and characteristics of Web workloads is a crucial step to improve the quality of the offered service– Design systems with better Performance and

Scalability.

• Workload Characterization Methodologies and Techniques– Guidelines to characterize and generate workloads

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IntroductionIntroduction

• Workload Characterizations typically do not consider reactivity aspects– Do not consider agreggate information from user

and server-side– Generate the same workload despite the server

response time

ServerClientRequest

Response

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e-speedGoalsGoals

• Characterize and replicate the behavior related to user reactivity

• Analyze and model the way users react to variations in the quality of service provided.– Correlate user and server-side

• Validate the model through simulation.

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MethodologyMethodology

• When we look at the interaction process we have sequences of requests and responses– IAT (inter-arrival time): time between requests

• User-side related measure

– Latency: time to process and answer the request• Server-side related measure

LATENCY

IAT

Server

Client

REQUEST

RESPONSE

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MethodologyMethodology

• The USAR Characterization Model– Conceptual views

USER (U)USER (U)

SESSION (S)SESSION (S)

ACTION (A)ACTION (A)

REQUEST (R)REQUEST (R)

• User: user behavior considering quality of service

• Session: actions between a threshold τ

• Action: clicks from the user

• Requests: objects associated with a action

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User Level CharacterizationUser Level Characterization

1) Prepare log: generate a temporary log Lu by aggregating the sessions per user;

2) Analyze users from the following perspectives:– IAT and Latency ratio;– IAT and Latency difference;

3) Discretize IAT and Latency measures using a correlation function;

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User Level Characterization -User Level Characterization - Discretization ModelDiscretization Model

• 7 User Classes (A – G)

DIFDIF

k5k5

k6k6

RATRAT

CC

BB

AA

EE

FF

GG

DD

00 k1k1k2k2

k3k3

k4k4

PatiencePatienceImpatienceImpatience

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User Level CharacterizationUser Level Characterization

4) Transform user sessions into sequences of user classes according to discretization criteria from item 3);– Ex: A A B F B G G G G A

5) Evaluate the sequences in order to group them using similarity; – Sequence mining (SPADE tool) or

matching

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User Level CharacterizationUser Level Characterization

• Classify them in User Profiles:

–Patient

k5k5

k6k6

RATRAT

CC

BB

AA

EE

FF

GG

DD

00 k1k1k2k2

k3k3

k4k4

–Impatient

–Impatient Tendency–Continuous

–Patient Tendency–Inconstant

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User Level CharacterizationUser Level Characterization

6) Process the log Lu applying a function f(Lu), which maps sequence of classes to groups defined in step 5;

7) Apply Clustering;

8) Analyze the clusters and classify them.

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MethodologyMethodology

• Validation using the USAR Simulation Model

USAR

Inputs

• Users Types• Behaviors and Profiles• Session Length Dist.• Action Popularity• Object Size• Object Popularity

Parameters• # User Sessions

Log of User Reactions per Session

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Case StudyCase Study

• Proxy-cache of Federal University of Minas Gerais (UFMG);

• 4 weeks of logs.

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Case StudyCase Study

• Distribution of Sessions and User Profiles

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Case StudyCase Study

• Validation through simulationUSAR

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ConclusionConclusion

• We propose the USAR characterization methodology that comprehend the levels of request, action, session and user– Generic Methodology applicable to any workload

• Model reactivity aspects using inter-arrival time and latency that has been interesting and promising– Present a discretization model based on the radio

and difference between IAT and latency

• Validate the model through simulation

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Ongoing WorkOngoing Work

• We foresee the possibility to generate more realistic workload– Reduce the gap between the existing

models and the actual workloads considering reactivity aspects