An Analytical Model for Multi-tier Internet Services and its Applications

UUNIVERSITYNIVERSITY OFOF M MASSACHUSETTS, ASSACHUSETTS, AAMHERST MHERST – – Department of Computer ScienceDepartment of Computer Science

An Analytical Model for Multi-tier Internet Services and its Applications

Bhuvan Urgaonkar, Giovanni Pacifici, Prashant Shenoy, Mike Spreitzer,

Asser Tantawi

University of Massachusetts and IBM TJ Watson

UUNIVERSITYNIVERSITY OFOF M MASSACHUSETTS, ASSACHUSETTS, AAMHERST MHERST – – Department of Computer ScienceDepartment of Computer Science 2

Internet Applications

Proliferation of Internet applications

auction site online game online store

Growing significance in personal, business affairs Focus: Modeling Internet applications


Why Model Internet Applications?

Capacity provisioning How many servers does the application need?

Performance prediction E.g., predict response time

Application configuration Tune various application parameters

Request policing Turn away excess requests during overloads


Internet Application Architecture

Multi-tier architecture Each tier uses services provided by its successor

Session-based workloads Caching, replication

HTTP J2EE Databaserequest processing in an online bookstore

search “moby” queries

response

Melville’s ‘Moby Dick’Music CDs by Moby


Existing Application Models Models for Web servers [Chandra03, Doyle03]

Do not model Java server, database etc.

Black-box models [Kamra04, Ranjan02] Unaware of bottleneck tier

Extensions of single-tier models [Welsh03] Fail to capture interactions between tiers

Existing models inadequate for multi-tier Internet applications


Talk Outline

MotivationApplication Model

Evaluation of the Model Dynamic Capacity Provisioning Summary and Future Research


Baseline Application Model

Model consists of two components Sub-system to capture behavior of clients Sub-system to capture request processing inside the application

clients application


Modeling Clients

Clients think between successive requests Infinite server system to capture think time Z Captures independence of Z from processing in application

Client 1

Client 2

Client N

Z

Z

Z

Q0

applicationclients


Modeling Request Processing

Q1 Q2 QM

tier 1 tier 2 tier M

pM=1p3p1

p2

S1 S2 SM

Transitions defined to capture circulation of requests Request may move to next queue or previous queue

Multiple requests are processed concurrently at tiers Processor sharing scheduling discipline

Caching effects get captured implicitly!

N


Putting It All Together

Q0

Q1 Q2 QM

pM=1p3p1

p2

Z

Z

S1 S2 SM

N

A closed-queuing model that captures a given number of simultaneous sessions being served

tier 1 tier 2 tier M

client

client


Model Solution and Parameter Estimation

Mean Value Analysis (MVA) Algorithm Computes mean response time

Visit ratios Equivalent to trans. probs. for MVA Vi ≈ λi / λreq ; λreq at policer, λi from logs

Service times Use residence time Xi logged at tier i For last tier, SM ≈ XM

Si = Xi – ( Vi+1 / Vi ) · Xi+1

Think time Measured at the entry point of application


Talk Outline

Motivation Application Model

Evaluation of the Model Dynamic Capacity Provisioning Summary and Future Research


Evaluation of Baseline Model

Auction site RUBiS One server per tier

Apache JBOSS Mysql

Concurrency limits not captured

0

5000

10000

15000

20000

25000

30000

0 100 200 300 400 500

ObservedBasic Model

Avg

resp

tim

e (m

sec)

Num sessions

150 75


Q0Q1 Q2 QM

Z

ZS1 S2 SM

N

Requests may be dropped due to concurrency limits Need to model the finiteness of queues!

Handling Concurrency Limits

dropped requests


QMp1 pM

S1 SM

Q0Q1 Q2 QM

Z

ZS1 S2 SM

N

Approach: Subsystems to capture dropped requests Distinguish the processing of dropped requests

Handling Concurrency Limits

dropQ1drop drop

dropdrop

drop


Enhanced model can capture concurrency limits

Response Time Prediction

0

5000

10000

15000

20000

25000

30000

0 100 200 300 400 500

ObservedBasic ModelEnh Model

Avg

resp

tim

e (m

sec)

Num sessions


Query Caching at the Database

0200400600800

100012001400

0 20 40 60 80 100

ObservedModel

Avg

resp

tim

e (m

sec)

% queries cached

Caching effects Captured by tuning Vi and/or Si

Bulletin-board site RUBBoS 50 sessions

SELECT SQL_NO_CACHE causes Mysql to not cache the response to a query

More model enhancements Replication at tiers Multiple session classes


Prototype Data Center

40+ Linux servers Gigabit switches Multi-tier applications

Auction (RUBiS) Bulletin-board (RUBBoS) Apache, JBOSS

(replicable) Mysql database


Dynamic Capacity Provisioning

01000200030004000500060007000

0 10 20 30 40 50 60

Res

p tim

e (m

sec)

Time (min)

Workload Response timeServer allocations

Auction application RUBiS Factor of 4 increase in 30 min

0

2

4

6

8

10

12

0 10 20 30 40 50 60

Web serversApp servers

Num

ber o

f ser

vers

Time (min)

20406080

100120140160

0 10 20 30 40 50 60

Arr

ival

s pe

r min

Time (min)

Server allocations increased to match increased workload Response time kept below 2 seconds


Talk Outline

Motivation Baseline Application Model Evaluation of the Model Dynamic Capacity Provisioning

Summary and Future Research


Summary and Future Work

Analytical model for Multi-tier Internet Applications Mean-value analysis Concurrency limits, replication, caching, multiple classes Model validation using 3-tier applications Dynamic provisioning, request policing

Future work Handling load imbalances at replicated tiers Handling more diverse workloads Handling other kinds of scheduling disciplines at servers


Thank you!

More information at: http://www.cs.umass.edu/~bhuvan

Date post:	23-Mar-2016
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An Analytical Model for Multi-tier Internet Services and its Applications

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