Rossella Macchi : Politecnico di Milano – eni s.p.a. Danilo Ardagna:Politecnico di Milano

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

Rossella Macchi – ICEP 2012

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 motivations3

Goals:

Energy analysis and comparison of Virtual Desktop

Energy consumption optimization from virtualisation

Hw efficiencies:

Sw efficiencies:

Green ICT

Sources: Nasa and T-Systems The greening of business

2010 CO2 World consumption: • 33.5 billion tons • average increase 5% per year• 2% due to ICT

By 2020 a further ICT increase of 20%


Technologies Analysis :Measurements 4

1. Physical – virtual desktop comparison

2. Thin Client - Server


Technologies’ Analysis :break-even point 5


Goals: minimize the number of the active servers and VMs live migrations, with performance constraintsSolution:Dynamic resources profile (LOW-HIGH)Heuristic placement

6VM allocation on physical servers

Break-even point reductionSwitching profiles: 1. Low High

- Find new location for the new VM, when it does not fit into the current server

2. High Low- Underutilization of the servers


Theoretical problem :Bin Packing Problem

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

7

NP-HARD ProblemCannot be resolved efficiently within a reasonable time

Placing Heuristic

Global solution approximationParameters fine tuning


8VM allocation :

MILP modelGoals:

2

S

1=i211ii gMiTMigPMigMigTMigPMigyCF +_usecpuCVmin

Problem’s decision variablesxs,u

1 Users u allocated on server s 0 Else

ys 1 Server is ON 0 Else

ks1,s2,u 1 User U migrated from server s1 to server s2 0 Else

Mig1 Mig2 Migrations of profile 1 or 2

ParametersS (U)

Up1 (Up2)

NumServer

N1 (N2)

CpuServer (Ram Server)

CpuP1 ( P2) Ram P1 (P2)

oldxs,u

CF CV

Pmig

Tmig1 (Tmig2)

Perc_P1 (Perc_P2)

Language: Ampl

Solver: ILOG Cplex

Constraints:SiUjyx iji ,)2 ,

SiUpjxx Njiji ,1)3 11,,

Siperc_Pxperc_PxUp2

j=12ji,

Up1

j=11ji, 100)4

UjuxS

ijji

1,)1

SiRamServerRamPxRamPx i

Up2

j=12ji,

Up1

j=11ji, )5

SiCpuServerCpuPxCpuPx i

Up2

j=12ji,

Up1

j=11ji, )6

...2,,)101 1

2

1,,2 UpjSzSikmig

S

i

S

z

UP

jjzi

1,,)91 1

1

1,,1 UpjSzSikmig

S

i

S

z

UP

jjzi

2,,,, ,,,12)8 UpjziSzSikxoldx jzijzji 1,,,, ,,,12)7 UpjziSzSikxoldx jzijzji


Optimization:Heuristic 9

Stochastic approach adopted to avoid resources saturation

?Solved by the heuristic


VM allocation :Policy 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

Actual PolicyHeuristic

Global Optimum

∆ Consumption


VM allocation:Time comparison 1

1


12

Max server threshold to start a VM

Variable Value

MAX = 80Total consumption 24189,2

Migration Profile 1 186

MAX = 90Total consumption 24170,6


MAX = 100Total consumption 24180


Min thresholdper to turno off a server

Variable Value

MIN = 10Total consumption 24733,1


MIN = 20Total consumption 24503,5


MIN = 30Total consumption 24589


Priority Weight (sorted by use)

Variable Value

20 60 80Total consumption 24287.3








Heuristic robust with respect to parameters

VM allocation:Parameters Tuning


13

VM allocation: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

ActualMax 16,00 97,60% 93,75%

Avg 9,81 75,98% 72,98&

HuristicPolicy2

Max 12,00 86,58% 100,00%

Mvg 9,15 66,98% 79,52%


Scalability analysis14

Optimum – HuristicDeviationMax 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 analysis: CO2 savings 15

Total anual for 10240 users 109794,165 KWh = 44 tons CO2

1Kwh = 0,40 Kg CO2


16

Scalability analysis:Time and Resources

<1 second


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 constraintsThermal constraintsSecurity constraints

Develop a prototype for the VM migration

17Conclusions and future work


Questions ?

Questions ?

18


19Policy1 and Policy delta


20

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, 10th 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.

8) Zaman, Sharrukh. Combinatorial auction-based dynamic vm provisioning and allocation in clouds.

Rossella Macchi : Politecnico di Milano – eni s.p.a. Danilo Ardagna:Politecnico di Milano

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