fundamental characteristics of queues with fluctuating load (appeared in sigmetrics 2006) varun...

Fundamental Characteristics of Queues with Fluctuating Load

(appeared in SIGMETRICS 2006)

VARUN GUPTA

Joint with:

Mor Harchol-Balter

Carnegie Mellon Univ.

Alan Scheller-Wolf

Carnegie Mellon Univ.

Uri Yechiali

Tel Aviv Univ.

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Motivation

ClientsServer Farm

Requests

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Motivation

ClientsServer Farm

Requests

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Motivation

ClientsServer Farm

Requests

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Motivation

ClientsServer Farm

Requests

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Motivation

ClientsServer Farm

Requests

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Motivation

ClientsServer Farm

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Motivation

ClientsServer Farm

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Motivation

ClientsServer Farm

RequestsReal

World Fluctuating arrival

and service intensities

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A Simple Model

HL

exp(H)

exp(L)

HighLoad

LowLoad

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• Poisson Arrivals• Exponential Job Size Distribution• H/H > L/L

• H>H possible, only need stability

A Simple Model

HighLoad

LowLoad

H,H

L,L

exp()

exp()

HH

LL

12

The Markov ChainP

has

e

Number of jobs

L

H

H

H

0 1

0 1

2

2

L

L

H

H

L

L

. . .

. . .

Solving the Markov chain provides no behavioral insight

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HH

LL

• N = Number of jobs in the fluctuating load system

• Lets try approximating N using (simpler) non-fluctuating systems

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HH

LL

Method 1

Nmix

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HH

LL

Q: Is Nmix ≈ N?

A: Only when 0

Method 1

Nmix

½

½

+

,

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HH

LL

Method 2

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avg(H,L)avg(H,L)

Method 2

≡ Navg

Q: Is Navg ≈ N?

A: When ,

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Example

H=1, H=0.99

L=1, L=0.01

E[Nmix] ≈ 49.5 E[Navg] = 1

0

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Observations

• Fluctuating system can be worse than non-fluctuating

0 and asymptotes can be very far apart

E[Nmix] > E[Navg]

E[Nmix] E[Navg]

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Questions

• Is fluctuation always bad?

• Is E[N] monotonic in ?

• Is there a simple closed form approximation for E[N] for intermediate ’s?

• How do queue lengths during High Load and Low Load phase compare? How do they compare with Navg?

More than 40 years of research has not

addressed such fundamental questions!

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Outline

Is E[Nmix] ≥ E[Navg], always?

Is E[N] monotonic in ?

Simple closed form approximation for E[N]

Stochastic orderings for the number of jobs seen by an arrival into an H phase, L phase

Application: Capacity Planning

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Prior Work

Fluid/DiffusionApproximations

Transforms Matrix Analytic& Spectral Analysis

- P. Harrison- Adan and Kulkarni

Numerical ApproachesInvolves solution of cubic

- Clarke- Neuts- Yechiali and Naor

Involves solution of cubic

- Massey- Newell- Abate, Choudhary, Whitt

Limiting Behavior

But cubic equations have a close form solution…

?

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Good luck understanding this!

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Asymptotics for E[N] (H<H)

E[Navg]

E[Nmix]

E[N]

(switching rate)Highfluctuation

H=1, H=0.99

L=1, L=0.01

E[Nmix] > E[Navg]

Lowfluctuation

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Asymptotics for E[N] (H<H)

E[N]

E[Nmix]

E[Navg]

• Agrees with our example (H = L)

• Ross’s conjecture for systems with constant service rate:

“Fluctuation increases mean delay”

Q: Is this behavior possible?

A: Yes

E[N]

E[Navg]

E[Nmix]

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Our Results

E[N]

(H-H) > (L-L)(H-H) = (L-L)(H-H) < (L-L)

• Define the slacks during L and H as• sL = L - L

• sH = H - H

E[N]

E[N]

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Our Results

• Define the slacks during L and H as• sL = L - L

• sH = H - H

• Not load but slacks determine the response times!

sH > sLsH = sLsH < sL

KEY IDEA

E[N]

E[N]

E[N]

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Outline

Is E[Nmix] ≥ E[Navg], always?





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Outline

Is E[Nmix] ≥ E[Navg], always? No





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Monotonicity of E[N]

:

MeanQueueLength

H L

’ :

MeanQueueLength

H L

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We show : E[N] is monotonic in

:

MeanQueueLength

H L

’ :

MeanQueueLength

H L

Not obvious that true for all ,’ with ’< !

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Outline






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Outline


Is E[N] monotonic in ? Yes




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Approximating E[N]

• Express the first moment as *

E[N] = E[Nmix]r+E[Navg](1-r)

• Approximate r by the root of a quadratic

KEY IDEA

KEY IDEA

* True for H < H; a similar expression exists for case of transient overload

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Approximating E[N]

1

3

5

7

9

1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+0110-5 10-4 10-3 10-2 10-1 100 101

3

5

7

9

E[N]

ExactApprox.

H=L=1, H=0.95, L=0.2

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Approximating E[N]

1

3

5

7

9

1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+0110-5 10-4 10-3 10-2 10-1 100 101

3

5

7

9

E[N]

ExactApprox.

H=L=1, H=0.95, L=0.2

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Approximating E[N]

2

6

10

14

18

1.E-02 1.E-01 1.E+00 1.E+0110-2 10-1 100 10

ExactApprox.

H=L=1, H=1.2, L=0.2

2

6

10

14

18

E[N]

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Approximating E[N]

2

6

10

14

18

1.E-02 1.E-01 1.E+00 1.E+0110-2 10-1 100 10

ExactApprox.

H=L=1, H=1.2, L=0.2

2

6

10

14

18

E[N]

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Outline






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Outline






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Stochastic Ordering refresher

• For random variables X and Y

X st Y Pr{Xi} Pr{Yi}

for all i.

• X stY E[f(X)] E[f(Y)] for all increasing f

– E[Xk] E[Yk] for all k 0.

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Notation

• NH: Number of jobs in system during H phase

• NL: Number of jobs in system during L phase

• N = (NH+NL)/2

H,H

L,L

exp()

exp()NH NL

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Stochastic Orderings for NL, NH

• NL ≥st NM/M/1/L

• NH ≤st NM/M/1/H

• NH ≥st NL

• NH ≥st Navg

• NL st Navg

?

?

?

?

?

H,H

L,L

exp()

exp()NH NL

NH increasesstochastically as ↓

Conjecture:

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Outline






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Outline






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Scenario

Application: Capacity Provisioning

HH

LL

2HH

2LL

Aim: To keep the mean response times same

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Scenario

Application: Capacity Provisioning

HH

LL

2H2H

2L2L

Question: What is the effect of doubling the arrival and service rates on the mean response time?

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What happens to the mean response time when , are doubled in the fluctuating load queue?

Halves

Remains almost the sameReduces by less than half

Reduces by more than halfA:

D:C:

B:

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Halves



D:C:

B:

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Halves



D:C:

B:

Look at slacks!

A: sH = sL

B: sH > sL

C: sH < sL

D: sH < 0, 0

reduces by half more than half less than half remains same

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Our Contributions

• Give a simple characterization of the behavior of E[N] vs.

• Provide simple (and tight) quadratic approximations for E[N]

• Prove the first stochastic ordering results for the fluctuating load model

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Thank you

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Analysis of E[N]

First steps:

– Note that it suffices to look at switching points

– Express

• NL = f(NH)

• NH = g(NL)

– The problem reduces to finding Pr{NH=0} and Pr{NL=0}

H,H

L,L

NH NL

NL=f(g(NL))

fg

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– Find the root of a cubic (the characteristic matrix polynomial in the Spectral Expansion method)

– Express E[N] in terms of

E[N] =

The simple way forward…

H,H

L,L

fg

A

A-A

H(L -L)0H+ L(H-H)0

L - (L -L)(H-H)

2 (A -A)+

Where 0L = 0

H = (A-A)

L(-1)(H-H)

(A-A)

H(-1)(L-L)

NH NL Difficult to even prove the monotonicity of E[N] wrt

using this!

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Our approach (contd.)

• Express the first moment as

E[N] = f1()r+f0()(1-r)

– r is the root of a (different) cubic– r1 as 0 and r0 as

KEY IDEA

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• E[N] = f1()r+f0()(1-r)

• r is monotonic in E[N] is monotonic in • The cubic for r has maximum power of as 2

1

0

r

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• E[N] = f1()r+f0()(1-r)


1

0

r

Need at least 3 roots for when r=c1

but has at most 2 roots

c1

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• E[N] = f1()r+f0()(1-r)


1

0

r

Need at least 2 positive roots for when r=c2

but for r>1 product of roots is negative

c2

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• E[N] = f1()r+f0()(1-r)


1

0

r

E[N] is monotonic in !

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Why do slacks matter?

• Fact: The mean response time in an M/M/1 queue is (-)-1

– Higher slacks Lower mean response times

• What is the fraction of customers departing during H

H,H

L,L

exp()

exp()

when ?

H,H

L,L

exp()

exp()

when 0?

H

H + L

H

H + L?

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when ?

H,H

L,L

exp()

exp()

when 0?

H

H + L

H

H + L?




H,H

L,L

exp()

exp()

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when ?

H,H

L,L

exp()

exp()

when 0?

A H?




As switching rates decrease, larger fraction of customers experience lower mean response times when sH>sL

H,H

L,L

exp()

exp()

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Q: What happens to E[N] when we double ’s and ’s?

A:System A: , ,

System B: 2, 2,

?

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Q: What happens to E[N] when we double ’s and ’s?

A:System A: , ,

System B: 2, 2,

System C: 2, 2, 2

E[N] remains same in going from A to C

A) sL = sH : remains same

B) sL > sH : increases, but by less than twice

C) sL < sH : decreases

D) 0, H>1 : queue lengths become twice as switching rates halve, E[N] doubles

fundamental characteristics of queues with fluctuating load (appeared in sigmetrics 2006) varun...

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