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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. Outline. Goals and motivations - PowerPoint PPT Presentation
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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
Rossella Macchi – ICEP 2012
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%
Rossella Macchi – ICEP 2012
Technologies Analysis :Measurements 4
1. Physical – virtual desktop comparison
2. Thin Client - Server
Rossella Macchi – ICEP 2012
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
Rossella Macchi – ICEP 2012
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
Rossella Macchi – ICEP 2012
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
Rossella Macchi – ICEP 2012
Optimization:Heuristic 9
Stochastic approach adopted to avoid resources saturation
?Solved by the heuristic
Rossella Macchi – ICEP 2012
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
Rossella Macchi – ICEP 2012
12
Max server threshold to start a VM
Variable Value
MAX = 80Total consumption 24189,2
Migration Profile 1 186
MAX = 90Total consumption 24170,6
Migration Profile 1 181
MAX = 100Total consumption 24180
Migration Profile 1 186
Min thresholdper to turno off a server
Variable Value
MIN = 10Total consumption 24733,1
Migration Profile 1 116
MIN = 20Total consumption 24503,5
Migration Profile 1 113
MIN = 30Total consumption 24589
Migration Profile 1 123
Priority Weight (sorted by use)
Variable Value
20 60 80Total consumption 24287.3
Migration Profile 1 181
20 60 40Total consumption 24170.5
Migration Profile 1 174
40 60 20Total consumption 24272.2
Migration Profile 1 186
60 40 20Total consumption 24262.8
Migration Profile 1 170
Heuristic robust with respect to parameters
VM allocation:Parameters Tuning
Rossella Macchi – ICEP 2012
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%
Rossella Macchi – ICEP 2012
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 %
Rossella Macchi – ICEP 2012
Scalability analysis: CO2 savings 15
Total anual for 10240 users 109794,165 KWh = 44 tons CO2
1Kwh = 0,40 Kg CO2
Rossella Macchi – ICEP 2012
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
Rossella Macchi – ICEP 2012
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.