Black Swan Based VM Placement and Migration Optimizations

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The Black Swan Property

M.Zhanikeev -- [email protected] -- Black Swan Based VM Placement and Migration Optimizations -- http://tinyurl.com/kyutech131018 --- 2/19...

2/19

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The Black Swan

• theBlack Swanproperty is well known 01

• how common do you thinkblack swans are around theworld?

• are they common in Japan?

• I have two in a pond near myhome...

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


3/19

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The Black Swan : Illustration (trains)

• basically, rareevents have more

impact

Events (ordered by prob.)

Freq

uenc

y =

prob

abilit

y

Accounted for

Un-accountedfor

Scheduling Problem(±10m)

Humanaccident(±40m) Train brakes

down(±4h) 9.11, 3.11,

….(±3d)


4/19

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The Black Swan : Convex is Bad

.Black Swan Property..

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When complex systems have convex behavior in their components, they have blackswans.

• 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)


5/19

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Black Swan in Engineering


6/19

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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 threshold05 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)


7/19

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Black Swans in Clouds : the Model• each object in clouds (VM, etc.) gets asrankRk

Rk =∑i=1..t

wiFk(v, t) (1)

• risk potential of each object isPk Pk = |Rk,i − Rk,i−1|. (2)

• evaluation of riskE for a given system /collection of objects

E = var({Pk}), (3)

• evolution of evaluation in timeEVO EVO = var({Et}). (4)

• finally, optimization (minimization of risk) andstatistical evaluation of risks are possible

minimize∑i=1,n

∑j=1,m

Pij. (5)

• 2nd order norm and variance L̂ =

∫ K

0(K−x)f(x)dx and σ2

L =

∫ K

0(K−x)2f(x)dx.

(6)


8/19

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Specific Black Swan Method forClouds


9/19

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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)


10/19

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VM Load : Flash Crowd Model

02 1+myself "Active Network Measurement: Theory, Methods, and Tools" ITU Japan (2009)


11/19

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PM and APP Ranks


12/19

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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

T =1

2[(L− n) +

√(L− n)2 + k

1− L] (7)

◦ load above 0.9 considered dangerous (all VMs are impacted)


13/19

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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


14/19

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Results : Snapshot 1 : Do Nothing

• several dark PMs can occur

• ... color is utilization

• raw PDF movie in separate file!

6 0 3 1 4

5 8 1 6 4

1 3 10 7 1

4 5 1 4 8

1 0 5 4 10

2 0 10 6 4Method#do.nothingEPOCH#387

VM LOAD

EVALS

3 JOBS


15/19

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Results : Snapshot 2 : PM Swans

• better overall spread

• PDF movie separately!

0 1 8 3 4

4 5 4 3 0

9 6 3 4 1

3 1 4 7 2

6 4 6 4 6

1 1 3 4 6Method#PM.swansEPOCH#480

VM LOAD

EVALS

3 JOBS


16/19

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Results : Snapshot 3 : APP Swans

• better overall spread

• PDF movie separately!

1 9 0 8 5

7 1 0 8 7

6 6 1 4 2

2 0 7 5 5

0 6 4 6 7

4 7 2 3 1Method#APP.swansEPOCH#465

VM LOAD

EVALS

3 JOBS


17/19

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Results : Catching Swans (red dot!)

4 6 8 10 12 14 16 18 20 22 24 26Number of Group-Failed PMs (0..30)

00.20.40.60.8

11.21.41.61.8

22.22.42.6

log(

runs

bet

wee

n fa

ilure

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

do.nothingPM.swansAPP.swans


18/19

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That’s all, thank you ...


19/19

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[01] N.Taleb (2008)The black swan: the impact of highly improbablePenguin

[02] 1+myself (2009)Active Network Measurement: Theory, Methods, and ToolsITU Japan

[03] myself+1 (2012)Popularity-Based Modeling of Flash Events in Synthetic Packet TracesIEICE CQ研

[04] myself+1 (2013)VM Migration Avoidance based on Flow Workload ClassificationIEICEソサエティ学会

[05] L.McGinty+1 (2009)Black Swans, Gray Cygnets and Other Rare BirdsSpringer LNAI vol.5650


19/19

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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 QuadrantsIBM Research


19/19

Technology

Black Swan Based VM Placement and Migration Optimizations