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

An Energy-Aware Methodology for Live Placement of Virtual Machines with Variable Profiles in Large Data Centers Rossella Macchi: Danilo Ardagna: Oriana Benetti: Politecnico di Milano eni s.p.a. Politecnico di Milano eni s.p.a.

Outline 2 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

Goals and motivations 3 2010 CO 2 World consumption: 33.5 billion tons average increase 5% per year 2% due to ICT By 2020 a further ICT increase of 20% Hw efficiencies: Sw efficiencies: Sources: Nasa and T-Systems The greening of business

Goals and motivations 3 Goals: Energy analysis and comparison of Virtual Desktop Energy consumption optimization from virtualisation Hw efficiencies: Green ICT Sw efficiencies: Sources: Nasa and T-Systems The greening of business

Technologies Analysis : Measurements 4 1. Physical virtual desktop comparison 2. Thin Client - Server

Technologies Analysis : break-even point 5

Technologies Analysis : break-even point 5

VM allocation on physical servers 6 Goals: minimize the number of the active servers and VMs live migrations, with performance constraints Solution: Dynamic resources profile (LOW-HIGH) Heuristic placement 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 - Underutilization of the servers

Theoretical problem : Bin Packing Problem 7 Bin-Packing Problem, MCBBP variant (multi-capacity bin packing problem)

Theoretical problem : Bin Packing Problem 7 Bin-Packing Problem, MCBBP variant (multi-capacity bin packing problem) NP-HARD Problem Cannot be resolved efficiently within a reasonable time Placing Heuristic Global solution approximation Parameters fine tuning

VM allocation : MILP model 8 Goals: S min i=1 CV cpu i _use + CF y i + PMig TMig 1 Mig 1 + PMig TMig 2 Mig 2 S (U) Up 1 (Up 2 ) NumServer N1 (N2) Parameters 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) x s,u y s k s1,s2,u Problem s decision variables 1 Users u allocated on server s 0 Else 1 Server is ON 0 Else 1 User U migrated from server s1 to server s2 0 Else Mig 1 Mig 2 Migrations of profile 1 or 2 Language: Ampl Solver: ILOG Cplex

VM allocation : MILP model 8 Goals: S min i=1 CV cpu i _use + CF y i + PMig TMig 1 Mig 1 + PMig TMig 2 Mig 2 Constraints: S 1) x u j U 4) 5) 6) 9) 10) i=1 Up1 Up2 2 ) x y j U, i S ) x + x j Up i S, + 1, x perc_p+ x perc_p i S i, j 1 100 i, j 2 j=1 Up1 i, j j j=1 Up2 i j x RamP+ x RamP RamServer i S i, j 1 i, j 2 i j=1 Up1 j=1 Up2 x CpuP+ x CpuP CpuServer i S i, j 1 i,j 2 i j=1 mig,, 1 1 = k i S z S j Up i, z, j i= 1 z= 1 j= 1 S S UP2 = mig2 ki, z, j i S, z S, j Up2 i= 1 z= 1 j= 1 i j=1 + x 2 k + 1 i S, z S, i z, j i, j z, j i, z, j + x 2 k + 1 i S, z S, i z, i, j z, j i, z, j S S UP1 3 i, j i, j N1 1 7) oldx Up1 8) oldx j Up... 2

Optimization: Heuristic 9

Optimization: Heuristic 9 Stochastic approach adopted to avoid resources saturation

VM allocation : Policy implemented 10 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) Consumption

VM allocation: Time comparison 11

VM allocation: Parameters Tuning 12 Max server threshold to start a VM MAX = 80 MAX = 90 MAX = 100 Variable Value Total consumption 24189,2 Migration Profile 1 186 Total consumption 24170,6 Migration Profile 1 181 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) 20 60 80 20 60 40 40 60 20 60 40 20 Variable Value Total consumption 24287.3 Migration Profile 1 181 Total consumption 24170.5 Migration Profile 1 174 Total consumption 24272.2 Migration Profile 1 186 Total consumption 24262.8 Migration Profile 1 170 Heuristic robust with respect to parameters

VM allocation: Resouces 13 Actual Huristic Policy2 Num Server Cpu On Ram On Max 16,00 97,60% 93,75% Avg 9,81 75,98% 72,98& Max 12,00 86,58% 100,00% Mvg 9,15 66,98% 79,52% Lower use of servers for the same number of users (12 vs. 16) Resource-intensive, cpu always above 60%

Scalability analysis 14 Optimum Huristic Deviation Users Max Value 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 14

Scalability analysis: CO2 savings 15 Total anual for 10240 users 109794,165 KWh = 44 tons CO2 1Kwh = 0,40 Kg CO2

Scalability analysis: Time and Resources 16

Scalability analysis: Time and Resources 16 <1 second

Conclusions and future work 17 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

Questions? 18 Questions?

Policy1 and Policy delta 19

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