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

Transcription

1 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.

2 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

3 Goals and motivations 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

4 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

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

6 Technologies Analysis : break-even point 5

7 Technologies Analysis : break-even point 5

8 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

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

10 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

11 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

12 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 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

13 Optimization: Heuristic 9

14 Optimization: Heuristic 9 Stochastic approach adopted to avoid resources saturation

15 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

16 VM allocation: Time comparison 11

17 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 Total consumption 24170,6 Migration Profile Total consumption Migration Profile Min thresholdper to turno off a server Variable Value MIN = 10 Total consumption 24733,1 Migration Profile MIN = 20 Total consumption 24503,5 Migration Profile MIN = 30 Total consumption Migration Profile Priority Weight (sorted by use) Variable Value Total consumption Migration Profile Total consumption Migration Profile Total consumption Migration Profile Total consumption Migration Profile Heuristic robust with respect to parameters

18 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%

19 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 %

20 Scalability analysis 14

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

22 Scalability analysis: Time and Resources 16

23 Scalability analysis: Time and Resources 16 <1 second

24 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

25 Questions? 18 Questions?

26 Policy1 and Policy delta 19

27 Bibliography 20 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, ) B. Bobro, Kochut. Dynamic placement of virtual machines for managing sla violations. Integrated Network Management, 10 th IEEE International Symposium, ) Borriello. Analisi delle tecnologie intel-vt e amd-v a supporto della virtualizzazione dell'hardware. Master's thesis, Ingegneria Elettronica Napoli, ) 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 ) F. G. Qiang Huang. Power consumption of virtual machine live migration in clouds. Third International Conference on Communications and Mobile Computing, ) T-Systems. White paper green ict: The greening of business. 8) Zaman, Sharrukh. Combinatorial auction-based dynamic vm provisioning and allocation in clouds.