Load Prediction for Best Effort Real TimePeter A. Dinda

[tmin,tmax] ??InteractiveApplication

Short taskswith deadlines

Unmodified COTS Distributed System

1 3 5 7Measured Load

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5

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25

Exe

cutio

n T

Ime

(Sec

onds

)

42,000 pointsCoefficient of Correlation = 0.998

nominal

tt

t

tdttload

execnow

now

)(1

1

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Host

Production Cluster ResearchCluster

Desktops

+SDev

-SDev

Mean

Title:axp7_tue_19.epsCreator:MATLAB, The Mathworks, Inc.Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.

Title:axp7_19_day_time.epsCreator:MATLAB, The Mathworks, Inc.Preview:This EPS picture was not savedwith a preview included in it.Comment:This EPS picture will print to aPostScript printer, but not toother types of printers.

Time

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tj

jtjt aaz

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.02

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.04

),0(~ 2at WhiteNoisea 2,~ ztz

22za

UnpredictableRandom Sequence Fixed Linear Filter

Partially PredictableLoad Sequence

ARFIMA(p,d,q)

ARIMA(p,d,q)

ARMA(p,q)

AR(p) MA(q)

tt aB

z)(

1

tt aB

Bz

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da

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Bz tdt

Infinite # roots

d roots

a is the confidence interval for t+1 predictions

Map work that would take 100 ms at zero load

axp0: z=0.54, =1.0, a(ARMA(4,4))= 0.109 a(ARFIMA(4,d,4))= 0.108no model: 1.0 +/- 1.06 (95%) => 100 to 306 msARMA: 1.0 +/- 0.22 (95%) => 178 to 222 msARFIMA: 1.0 +/- 0.21 (95%) => 179 to 221 ms

axp7: z=0.14, =0.12, a(ARMA(4,4))= 0.041 a(ARFIMA(4,d,4))= 0.025no model: 0.12 +/- 0.27 (95%) => 100 to 139 msARMA: 0.12 +/- 0.08 (95%) => 104 to 120 msARFIMA: 0.12 +/- 0.05 (95%) => 107 to 117 ms

Delegatewith Hysteresis

LoadPredSyscond

Load Prediction

Engine

Host A

OtherJobs

LoadPredSyscond

ServerReplica

ServerReplica

Load Prediction

Engine

Host B

OtherJobs

ClientApplication code

BBN QuO System

CMU Load Prediction(Peter A. Dinda)

Delegate choosesserver replica basedon load predictions

•CMU load prediction software uses linear time series models to predict load on each host.•QuO delegate choses server replica based on load predictions

Integration by Peter A. Dinda and Xiaoming Liu

Execution Model Execution Time and Host Load Integration In BBN QuO

Load is Self Similar Load Exhibits Epochal Behavior

Linear Time Series Models Realizable Pole-zero Models Real World Benefits of Prediction

ARFIMA ModelsCapture Long-rangeDependence of Self-Similar Signals

Choose host based on predicted load

-1

-0.5

0

0.5

1

1.5

2

Host

+SDev

-SDev

Mean

Production Cluster ResearchCluster

Desktops

Load is Highly Variable

Execution Modeland Integrationinto BBN QuO

StatisticalProperties ofHost Load

Load PredictionWith Linear Time

Series Models