An Energy-Aware Methodology for Live Placement of Virtual Machines with Variable Profiles in Large Data Centers

Rossella Macchi:

Politecnico di Milano – eni s.p.a.

Danilo Ardagna:

Politecnico di Milano

Oriana Benetti:

eni s.p.a.

An Energy-Aware Methodology for Live

Placement of Virtual Machines with Variable

Profiles in Large Data Centers

Outline

Outline

1)

Goals and motivations

2)

Physical – virtual desktop comparison

3)

Mathematical formulation of the VM allocation problem

4)

Heuristic solution

5)

Experimental analysis

6)

Conclusions and future work

2

Goals and motivations

3

3

Hw

efficiencies

:

Sw

efficiencies

:

2010 CO

₂

World consumption:

•33.5 billion tons

•average increase 5% per year

•2% due to ICT

Goals and motivations

3

3

Goals

:

Energy analysis and comparison of Virtual Desktop

Energy consumption optimization from virtualisation

Hw

efficiencies

:

Sw

efficiencies

:

Technologies Analysis :

Measurements

₄

₄

1. Physical – virtual desktop comparison

2. Thin Client - Server

Technologies’ Analysis :

Technologies’ Analysis :

Goals:

minimize the number of the active servers and VMs live migrations, with performance constraints

Solution:

Dynamic resources profile (LOW-HIGH)

Heuristic placement

6

6

VM

VM

allocation

allocation

on

on

physical

physical

servers

servers

Break-even point reduction

Switching profiles:

1.

Low

High

- Find new location for the new VM, when it does

not fit into the current server

2.

High

Low

Theoretical

Theoretical

problem

problem

:

:

Bin Packing Problem

Bin Packing Problem

Bin-Packing Problem, MCBBP variant (multi-capacity bin packing problem)

7

Theoretical

Theoretical

problem

problem

:

:

Bin Packing Problem

Bin Packing Problem

Bin-Packing Problem, MCBBP variant (multi-capacity bin packing problem)

7

7

NP-HARD Problem

Cannot be resolved efficiently within a reasonable time

Placing Heuristic

Global solution approximation

Parameters fine tuning

8

8

VM

VM allocation

allocation

:

:

MILP

MILP model

model

Goals

:

2

S

1

=

i

2

1

1

i

i

_use

+

CF

y

PMig

TMig

Mig

PMig

TMig

Mi

g

cpu

CV

min

∑

∗

∗

+

∗

∗

+

∗

∗

Problem’s decision variables

x

s,u

1 Users u allocated on server s

0 Else

y

_s

1 Server is ON

0 Else

k

_s1,s2,u

1 User U migrated from server s1 to server s2

0 Else

Mig

₁

Mig

₂

Migrations of profile 1 or 2

Parameters

S (U)

Up

₁

(Up

₂

)

NumServer

N1 (N2)

CpuServer (Ram Server)

CpuP

₁

( P

₂

)

Ram P

₁

(P

₂

)

oldx

_s,u

CF CV

Pmig

Tmig

₁

(Tmig

₂

)

Perc_P1 (Perc_P2)

Language: Ampl

Solver: ILOG Cplex

8

8

VM

VM allocation

allocation

:

:

MILP

MILP model

model

Goals

:

2

S

1

=

i

2

1

1

i

i

_use

+

CF

y

PMig

TMig

Mig

PMig

TMig

Mi

g

cpu

CV

min

∑

∗

∗

+

∗

∗

+

∗

∗

Constraints

:

S

i

U

j

y

x

_i

_j

≤

_i

∀

∈

_,

∀

∈

)

2

_,

x

_{i j}

+

x

_{i j N}

≤

∀

j

∈

Up

∀

i

∈

S

+

1

,

)

3

_{, , 1 1}

S

i

perc_P

x

perc_P

x

Up2 j=1 i,j 2 Up1 j=1 i,j 1

∈

∀

≤

∗

+

∗

∑

∑

100

)

4

U

j

u

x

S i i j j

∈

∀

≤

∑

=1 ,

)

1

S

i

RamServer

RamP

x

RamP

x

_i Up2 j=1 i,j 2 Up1 j=1 i,j 1

∈

∀

≤

∗

+

∗

_∑

∑

)

5

S

i

CpuServer

CpuP

x

CpuP

x

_i Up2 j=1 i,j 2 Up1 j=1 i,j 1

∈

∀

≤

∗

+

∗

_∑

∑

)

6

...

2

,

,

)

10

2

Up

j

S

z

S

i

k

mig

S

S

UP

∈

∀

∈

∀

∈

∀

=

∑∑∑

1

,

,

)

9

1 1 1 1 , , 1

k

i

S

z

S

j

Up

mig

S i S z UP j i z j

∈

∀

∈

∀

∈

∀

=

_{∑ ∑ ∑}

= = = 2 , , , ,

2

1

,

,

,

)

8

oldx

_{i j}

+

x

_{z j}

≤

∗

k

_{i z j}

+

∀

i

∈

S

∀

z

∈

S

∀

i

≠

z

∀

j

∈

Up

1

,

,

,

,

2

1

,

,

,

)

7

oldx

_i

_j

+

x

_z

_j

≤

∗

k

_i

_z

_j

+

∀

i

∈

S

∀

z

∈

S

∀

i

≠

z

∀

j

∈

Up

Optimization

Optimization:

:

Heuristic

Heuristic

9

9

Optimization

Optimization:

:

Heuristic

Heuristic

9

9

Stochastic approach adopted to

avoid resources saturation

VM

VM

allocation

allocation

:

:

Policy

Policy implemented

implemented



Enterprise actual policy:

Static profiles



Global optimum:

Obtained by the MILP model solution

Not applicable to real enterprise’s instances

Theoretical comparison



Heuristic:

Dynamic profiles

Different start allocation policy



Policy1: Sequential allocation, avoid boot storm problem (NO SSD)



Policy2: On-demand allocation (SSD)

10

10

VM

VM

allocation:

allocation

:

Time

12

12

Max server threshold to start a VM

Variable

Value

MAX = 80

Total consumption

24189,2

Migration Profile 1

186

MAX = 90

Total consumption

24170,6

Migration Profile 1

181

MAX = 100

Total consumption

24180

Migration Profile 1

186

Min thresholdper to turno off a server

Variable

Value

MIN = 10

Total consumption

24733,1

Migration Profile 1

116

MIN = 20

Total consumption

24503,5

Migration Profile 1

113

MIN = 30

Total consumption

24589

Migration Profile 1

123

Priority Weight (sorted by use)

Variable

Value

20 60 80

Total consumption

24287.3

Migration Profile 1

181

20 60 40

Total consumption

24170.5

Migration Profile 1

174

40 60 20

Total consumption

24272.2

Migration Profile 1

186

60 40 20

Total consumption

24262.8

Migration Profile 1

170

Heuristic robust with respect to

parameters

VM

VM

allocation:

allocation

:

Parameters

13

13

VM

VM

allocation:

allocation

:

Resouces

Resouces

Lower use of servers for the same number of users (12 vs. 16)

Resource-intensive, cpu always above 60%

Num

Server

Cpu On

Ram On

Actual

Max

16,00

97,60%

93,75%

Avg

9,81

75,98%

72,98&

Huristic

Policy2

Max

12,00

86,58%

100,00%

Mvg

9,15

66,98%

79,52%

Scalability

Scalability

analysis

analysis

14

14

Optimum – Huristic

Deviation

Max Value

Users

Percentage

80 1,14 % 160 2,87 % 240 5,75 % 320 5,00 %

Avg Value

Utenti

Percentage

80 1,74 % 160 3,08 % 240 4,81 % 320 4,98 %

Scalability

Scalability

analysis

analysis

14

Scalability

Scalability

analysis:

analysis

CO2 savings

CO2 savings

₁₅

₁₅

Total anual for 10240 users

109794,165 KWh = 44 tons CO2

16

16

Scalability

Scalability

analysis:

analysis

Time

16

16

Scalability

Scalability

analysis:

analysis

Time

Time

and Resources

and

Resources

Conclusions

:



Virtual-Physical desktop comparison



Break-even point



Heuristic solution



Average delta from the global optimum lower then 5%



Energy consumption reduced by about 35 % and resources by 25%



CO2 emission saving for 10,000 users about 44 tons

Future work:



Further integration:

Network constraints

Thermal constraints

Security constraints



Develop a prototype for the VM migration

17

17

Conclusions

Questions

Questions

?

?

Questions ?

18

19

19

Policy1 and Policy delta

Policy1 and Policy delta

20

20

Bibliography

Bibliography

1) Cplex:High-performance mathematical programming solver for linear programming, mixed integer

programming, and quadratic programming

2) T. Aghavendra, Ranganathan. No "power" struggles: coordinated multilevel power management for

the data center. ASPLOS 2008, 2008.

3) B. Bobro, Kochut. Dynamic placement of virtual machines for managing sla violations. Integrated

Network Management, 10

th

_{IEEE International Symposium, 2007.}

4) Borriello. Analisi delle tecnologie intel-vt e amd-v a supporto della virtualizzazione dell'hardware.

Master's thesis, Ingegneria Elettronica Napoli, 2011.

5) Dimitris Economou, Suzanne Rivoire. Full-system power analysis and modeling for server

environments. Workshop on Mode- ling, Benchmarking, and Simulation (MoBS), held at the

International Symposium on Computer Architecture (ISCA), June 2006.

6) F. G. Qiang Huang. Power consumption of virtual machine live migration in clouds. Third

International Conference on Communications and Mobile Computing, 2011.

7) T-Systems. White paper green ict: The greening of business.