Utility-Function-Driven Energy-Efficient Cooling in Data Centers

1. Utility-Function-Driven Energy-Efficient Cooling in Data Centers Authors: Rajarshi Das, Jeffrey Kephart, Jonathan Lenchner, HendrikHamamn IBM Thomas J. Watson Research Center Presented by: Shivashis Saha University of Nebraska-Lincoln

2. Outline • Introduction • Related Work • Data Center Energy Balance • Utility Functions • Multiplicative utility functions • Additive utility functions • Experiments • Conclusion

3. Introduction • Data center energy management • “50% of existing data centers will have insufficient power and cooling within two years” • “Power is the second-highest operating cost in 70% of all data centers” • “Data centers are responsible for the tens of millions of metric tons of carbon dioxide emissions annually --- more than 5% of the total global emissions”

4. Introduction • Why use autonomic computing? • Large, difficult to manage, complex • Management problem is both qualitatively similar to and quantitatively harder than that of managing IT alone.

5. Contributions • Apply utility functions to save energy • Tradeoff between energy and temperature • Control parameters: • Fan speed • On/off states of individual Computer Room Air Conditioning (CRAC) • Proposed model show 12% reduction in energy without violating temperature contraints

6. Related Work • Saving more energy is not good if administrator does not want that! • Proposed model is flexible • Apply computational fluid dynamics modeling to complex data center environments • Temperature aware workload placement based on inlet temperature or heat recirculation

7. Data Center Energy Balance • PDC, power to run data center is split using switch gear equipment into: • Path to power the IT equipments • Path to power the supporting equipments

8. Data Center Energy Balance • The support path may include • Power for pumping coolant to and from CRACs to the chiller and to and from the chiller to the cooling tower • Power path for IT equipments include • Conversion loss due to the uninterruptible power supply (UPS) systems • Losses associated with the power distribution PPDU • The UPS systems are located outside the raised floor area

9. Data Center Energy Balance • The total power on the floor: • PIT is the power consumed by the IT equipments • Total CRAC fan power and CDU pump power: • The relation between fan power PCRACi and relative fan speed Θi

10. Data Center Energy Balance • Under steady state condition, the total raised floor power equal to the total cooling power • The reduced fan speed reduces the air flow:

11. Data Center Energy Balance • All raised floor power needs to be cooled by the chilling system, which required power for refrigeration • COP: the coefficient of performance of the chiller system (assume, average COP = 4.5)

12. Data Center Energy Balance • Reducing CRAC fan speeds, the fan power is reduced • This reduces both the raised floor power and the power needed from chiller system • However, reducing fan speed also increases the server inlet temperature • A tradeoff between energy consumption and the temperature!!!

13. Utility Functions • Data center operators responsible for the physical environment tend not to be concerned about application level performance, e.g. performance, availability, or security • They are more concerned about cost, energy, temperature, and hardware lifetimes • There are two CRAC units, whose fan speeds are Θ1 and Θ2

14. Utility Functions • Multiplicative utility functions

15. Utility Functions • The previous utility function is very harsh!

16. Utility Functions • Additive utility functions

17. Experiments

18. Experiments

19. Experiments

20. Experiments Each CRAC was: Turned off Turned on at lowest speed (60%) Turned on at max speed (100%)

21. Experiments

22. Experiments • Snorkels were placed

23. Experiments

24. Conclusion • Use of utility functions in data centers • Total reduction of energy consumption by 14% • Dynamic aspects of utility functions are not yet considered • Investigation of techniques combining dynamic workload scheduling with dynamic workload migration

25. Thanks!