Applying Control Theory to Stream Processing Systems Wei Xu ([email protected])[email protected] Bill Kramer ([email protected])[email protected] Joe Hellerstein.

Applying Control Theory to Stream Processing Systems

Wei Xu ([email protected])Bill Kramer ([email protected])

Joe Hellerstein ( [email protected] )

Description of the system

TCQComplex internal structure

Input BufferData Source

TCQ drops tuples silently if result queue is full

Why do we need control?• Data source does not provide accurate data rate

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actual load

Why do we need control?• TCQ node drops tuples when result queue fill up

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tup

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source data rateend-to-end drop rate

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output rate of buffer

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time (ms)

fre

e s

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B)

free space

Buffer

Source

TCQ

Result Q

Control Problems

• Providing an accurate data source– Get the actual data rate

• Regulate queue length on TCQ node– Prevent dropping tuples – Maximize throughput (and adapts when distur

bance happens)

System with Control

Output Rate Controller

ControlledData Source

Queue Length Monitor

2

The Control Architecture

P Controller

PI Controller

Result – An accurate data source

P Controller with Pre-compensation PI Controller

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Result – regulating queue length

Buffer

Source

TCQ

Result Q

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csource data rateend-to-end drop rate

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output rate of buffer

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6x 10

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time (ms)

fre

e s

pa

ce (

KB

)

free space

Result – Under CPU Contention

Buffer

Source

TCQ

Result Q

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tup

les

pe

r se

csource data rateend-to-end drop rate

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time (ms)

tup

les

pe

r se

c

output rate of buffer

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6x 10

5

time (ms)

fre

e s

pa

ce (

KB

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free space

Why theory is useful?• One of my implementations .. What happened?

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source data rateend-to-end drop rate

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output rate of buffer

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6x 10

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time (ms)

fre

e s

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ce

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B)

free space

Buffer

Source

TCQ

Result Q

What is going on?

Queue LengthController

Desired Queue length

Actual Queue Length

Data Rate to TCQControlled

Output Thread

(Code Reuse)

Theory meets reality

Output Y from simulation

Time

Que

ue

leng

th

Tricky part of parameter estimation

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u: number of tuples per sec

y: f

ree

spac

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que

ue

Model evaluation – Making the system operate in desired range

Non-Linear range

Easy for data source, but queue length ..

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free

spac

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free space

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e sp

ace

(K

B)

free space

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-1

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u: number of tuples per sec

y: fr

ee

spa

ce o

n qu

eue

Free Space

Data rate vs free space

Settling Time and Overshoot matters

P Controller

A lot of small disturbance in a Java programIncremental garbage collection

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PI Controller

Conclusion

• Advantages of feedback control– Make system more robust under disturbance– Treat complex systems as black boxes

• Cope with the system characteristics instead of having to change it

– Encourage reporting system statistics– Implementation is easy and has theoretical

guarantees

Future Work

• Load balancer

• Smaller sample time to reduce disturbance caused by Java GC?

• Controller on scheduling of system shared by multiple streams

Backup Slides

Outline

• Problems and Motivation

• Controller design

• Result

• Discussion

Description of the System

Revised

DataSource

Input Buffer

TCQ Node

Queue length

RoutingLogic

Load SplitterTCQ Node

Tuples

Tuples

Tuple Blocks

Operation of Load Splitter1. Arriving blocks wait in Input Buffer2. Tuples are routed to balance TCQ queue lengths3. Stop routing if queue length is too large to avoid tuple discards

Compare to Open Loop Control

We know

Y(k) , and we know what we want y(k+1) to be.. Use transfer function to solve for u(k)…

(Expected result – accuracy and disturbance ) -- do be done

Estimation of the transfer function

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-1000

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3000

u: desired tuples per sec

y: a

ctua

l tup

les

per

sec

y(k+1)=ay(k)+bu(k)

Regression

Tricky part of parameter estimation

Model evaluation – A data rate that make it operate in linear range

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mb

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actual loadend drop

tcq drop

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block response time

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