Black Swan Based VM Placement and Migration Optimizations

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.

The Black Swan Property

M.Zhanikeev -- maratishe@gmail.com --

Black Swan Based VM Placement and Migration Optimizations -- http://tinyurl.com/kyutech131018 ---

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The Black Swan
• the

Black Swan

property is well known 01
• how common do you think

black swans are around the

world?
• are they common in Japan?
• I have two in a pond near my

home...

01 N.Taleb "The black swan: the impact of highly improbable" Penguin (2008)


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rare
events have more
impact

• basically,

Frequency = probability

The Black Swan : Illustration (trains)
Scheduling
Problem
(±10m)

Human
accident
(±40m)

Accounted
for
Un-accounted
for

Train brakes
down
(±4h)

9.11, 3.11,
….
(±3d)

Events (ordered by prob.)


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The Black Swan : Convex is Bad
.
Black Swan Property
.
When complex systems have
swans.
.
•

convex behavior in their components, they have black

convex = Long Tail

• disasters hide in the Long Tail 09
• because disasters are rare, they are normally unaccounted 06
• when disasters happen, we are completely unprepared

09 R.Kennet+1 "Quality, Risk and the Taleb Quadrants" IBM Research (2009)
06 A.Nafday "Consequence-based stuctural design ... for black swan..." Elsevier ...Safety (2011)


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Black Swan in Engineering



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Two Ways to Handle Black Swans
.
Method 1
.

fast response ... when Black Swan is detected, react promptly
.
• popoular in Black Swan engineering today 05 06

.
Method 2
.
robust

design

• catch

.

Black Swans before they happen

• this method

today!

• not very popular in engineering
• the problem of

noticability threshold

05 L.McGinty+1 "Black Swans, Gray Cygnets and Other Rare Birds" Springer LNAI vol.5650 (2009)
06 A.Nafday "Consequence-based stuctural design ... for black swan..." Elsevier ...Safety (2011)


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Black Swans in Clouds : the Model
object in clouds (VM, etc.)
rank Rk

• each

• risk potential of each object is
• evaluation of risk

gets as

Rk =

wi Fk (v, t)

(1)

Pk = |Rk,i − Rk,i−1 |.

(2)

E = var({Pk }),

(3)

EVO = var({Et }).
∑ ∑
minimize
Pij .

(4)

i=1..t

Pk

E for a given system /

collection of objects
• evolution of evaluation in time

EVO

• finally, optimization (minimization of risk) and

statistical evaluation of risks are possible


(5)

i=1,n j=1,m

∫
• 2nd order norm and variance

∑

ˆ
L=

∫

K

(K−x)f(x)dx
0

and

2
σL

K

(K−x)2 f(x)dx.

=
0


(6)

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Specific Black Swan Method for
Clouds



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Black Swan in Clouds: Terminology
•

VM : virtual machine
◦
◦
◦
◦

•

VMs can migrate when necessary 04
we assume that we can measure the load
or study traffic 02
... or use any other method for performance measurement

PM : physical machine in the a cloud
◦ the load is the sum of VM loads

•

APP : a multi-VM application
04 myself+1 "VM Migration Avoidance based on Flow Workload Classification" IEICEソサエティ学会 (2013)
02 1+myself "Active Network Measurement: Theory, Methods, and Tools" ITU Japan (2009)



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VM Load : Flash Crowd Model

02 1+myself "Active Network Measurement: Theory, Methods, and Tools" ITU Japan (2009)
M.Zhanikeev -- maratishe@gmail.com -Black Swan Based VM Placement and Migration Optimizations -- http://tinyurl.com/kyutech131018 ---

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PM and APP Ranks



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Simulation Setup
• methods:

do nothing, PM swans, and APP swans

D : history depth (for E)
• p : per-VM probability of a Flash Crowd
• Lmin, Lmax, La : VM utilization levels and slope
• PMs : number of PMs, fixed at 30 (5x6) grid
• APPs : number of applications, each needs 3 VMs
• R : how many fixes per epoch
•

• PM

load to response time
1
T = [(L − n) +
2

◦ load above

√

(L − n)2 + k
]
1−L

0.9 considered dangerous (all VMs are impacted)



(7)

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Models
1.

do nothing
◦ do not try to prevent Black Swans

2.

PM swans
◦ prevent Black Swans in PMs

3.

APP swans
◦ prevent Black Swans in APPs



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Results : Snapshot 1 : Do Nothing
2

• raw PDF movie in separate file!


4

0

5

4

10

4

5

1

4

8

1

3

10

7

8

1

6

4

0

3

1

4

Method#do.nothing
EPOCH#387

1

6

• ... color is utilization

6

5

dark PMs can occur

10

1
• several

0

VM LOAD

EVALS

3 JOBS


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Results : Snapshot 2 : PM Swans
1

6

4

6

4

6

3

1

4

7

2

9

6

3

4

5

4

3

0

1

8

3

4

Method#PM.swans
EPOCH#480

1

0


4

4

• PDF movie separately!

3

6
• better overall spread

1

VM LOAD

EVALS

3 JOBS


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Results : Snapshot 3 : APP Swans
4

1

6

4

6

7

2

0

7

5

5

6

6

1

4

1

0

8

7

9

0

8

5

Method#APP.swans
EPOCH#465

2

1


3

7

• PDF movie separately!

2

0
• better overall spread

7

VM LOAD

EVALS

3 JOBS


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Results : Catching Swans (red dot!)
log( runs between failures)

APP.swans
2.6
2.4
2.2
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0

PM.swans

do.nothing

D=10
p=0.05
APPs=30
R=10

4


6

8

10
12
14
16
18
20
22
Number of Group-Failed PMs (0..30)

24

26


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That’s all, thank you ...



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[01] N.Taleb (2008)
The black swan: the impact of highly improbable
Penguin
[02] 1+myself (2009)
Active Network Measurement: Theory, Methods, and Tools
ITU Japan
[03] myself+1 (2012)
Popularity-Based Modeling of Flash Events in Synthetic Packet Traces
IEICE CQ研
[04] myself+1 (2013)
VM Migration Avoidance based on Flow Workload Classification
IEICEソサエティ学会
[05] L.McGinty+1 (2009)
Black Swans, Gray Cygnets and Other Rare Birds
Springer LNAI vol.5650


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[06] A.Nafday (2011)
Consequence-based stuctural design ... for black swan...
Elsevier ...Safety
[07] G.Wright+1 (2009)
Decision making and planning ....low predictability...
...Journal of Forecasting
[08] T.Aven (2009)
Identification of safety and security critical systems....
Journal of Reliability...
[09] R.Kennet+1 (2009)
Quality, Risk and the Taleb Quadrants
IBM Research



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Black Swan Based VM Placement and Migration Optimizations

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