fundamental characteristics of queues with fluctuating load (appeared in sigmetrics 2006) varun...
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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.
2
Motivation
ClientsServer Farm
Requests
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Motivation
ClientsServer Farm
Requests
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Motivation
ClientsServer Farm
Requests
5
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
RequestsReal
World Fluctuating arrival
and service intensities
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A Simple Model
HL
exp(H)
exp(L)
HighLoad
LowLoad
11
• 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
13
HH
LL
• N = Number of jobs in the fluctuating load system
• Lets try approximating N using (simpler) non-fluctuating systems
14
HH
LL
Method 1
Nmix
15
HH
LL
Q: Is Nmix ≈ N?
A: Only when 0
Method 1
Nmix
½
½
+
,
16
HH
LL
Method 2
17
avg(H,L)avg(H,L)
Method 2
≡ Navg
Q: Is Navg ≈ N?
A: When ,
18
Example
H=1, H=0.99
L=1, L=0.01
E[Nmix] ≈ 49.5 E[Navg] = 1
0
19
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]
20
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!
21
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
22
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…
?
23
Good luck understanding this!
24
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
25
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]
26
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]
27
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]
28
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
29
Outline
Is E[Nmix] ≥ E[Navg], always? No
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
30
Monotonicity of E[N]
:
MeanQueueLength
H L
’ :
MeanQueueLength
H L
31
Monotonicity of E[N]
We show : E[N] is monotonic in
:
MeanQueueLength
H L
’ :
MeanQueueLength
H L
Not obvious that true for all ,’ with ’< !
32
Outline
Is E[Nmix] ≥ E[Navg], always? No
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
33
Outline
Is E[Nmix] ≥ E[Navg], always? No
Is E[N] monotonic in ? Yes
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
34
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
35
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
36
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
37
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]
38
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]
39
Outline
Is E[Nmix] ≥ E[Navg], always? No
Is E[N] monotonic in ? Yes
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
40
Outline
Is E[Nmix] ≥ E[Navg], always? No
Is E[N] monotonic in ? Yes
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
41
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.
42
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
43
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:
44
Outline
Is E[Nmix] ≥ E[Navg], always? No
Is E[N] monotonic in ? Yes
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
45
Outline
Is E[Nmix] ≥ E[Navg], always? No
Is E[N] monotonic in ? Yes
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
46
Scenario
Application: Capacity Provisioning
HH
LL
2HH
2LL
Aim: To keep the mean response times same
47
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?
48
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:
49
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:
50
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:
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
51
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
52
Thank you
53
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
54
– 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!
55
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
56
Monotonicity of E[N]
• 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
57
Monotonicity of E[N]
• 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
Need at least 3 roots for when r=c1
but has at most 2 roots
c1
58
Monotonicity of E[N]
• 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
Need at least 2 positive roots for when r=c2
but for r>1 product of roots is negative
c2
59
Monotonicity of E[N]
• 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
E[N] is monotonic in !
60
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?
61
Why do slacks matter?
when ?
H,H
L,L
exp()
exp()
when 0?
H
H + L
H
H + L?
• 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()
62
Why do slacks matter?
when ?
H,H
L,L
exp()
exp()
when 0?
A H?
• 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
As switching rates decrease, larger fraction of customers experience lower mean response times when sH>sL
H,H
L,L
exp()
exp()
63
Q: What happens to E[N] when we double ’s and ’s?
A:System A: , ,
System B: 2, 2,
?
64
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
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