1 / 20

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

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

sanaa
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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.

  2. Outline • Goals and motivations • Physical – virtual desktop comparison • Mathematical formulation of the VM allocation problem • Heuristic solution • Experimental analysis • Conclusions and future work

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

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

  5. Technologies’ Analysis : break-even point

  6. VM allocation on physical servers 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: Low High - Find new location for the new VM, when it does not fit into the current server High Low - Underutilization of the servers

  7. Theoretical problem: Bin Packing Problem 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 approximationParameters fine tuning

  8. VM allocation : MILP model Goals: Constraints: Language: Ampl Solver: ILOG Cplex

  9. Optimization:Heuristic ? Solved by the heuristic Stochasticapproachadoptedtoavoidresourcessaturation

  10. 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) Global Optimum ∆ Consumption Actual Policy Heuristic

  11. VM allocation: Time comparison

  12. VM allocation: Parameters Tuning Heuristic robust with respect to parameters

  13. VM allocation: Resouces Lower use of servers for the same number of users (12 vs. 16) Resource-intensive, cpu always above 60%

  14. Scalabilityanalysis

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

  16. Scalability analysis: Time and Resources <1 second

  17. Conclusions and future work 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

  18. Questions? Questions ?

  19. Policy1 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.

More Related