A Cyber-Physical Systems Approach to Energy Management in Data Centers

1. A Cyber-Physical Systems Approach to Energy Management in Data Centers Presented by Chen He Adopted form the paper authors

2. Outline • Introduction • Cyber-physical model • Control approach • Simulation results • Discussion

3. Motivation • Load • 7GW peak power consumption in 2006(US) • 12GW projected for 2011 • Cost • $4.5 billion for energy in 2006 • Cost of electricity will soon exceed cost of hardware

4. Motivation • Related Works • Server level • Low-power states(eg. Sleep and hibernate modes), Processor dynamic voltage and frequency scaling, DVFS and on/off states, resource redirection and task scheduling[3,5,7,8,11,15,21,22,23,24] • Data Center level • Change workload placement to reduce A/C costs[12] • Dynamic vary air flows to specific locations to improve cooling efficiency[20] • Tolia [28] proposed unified control of server power and cooling , but in Intra-zone (blade server) level • Can we create a comprehensive model to manage data center level power consumption through unified control?

5. Temperature distribution Image: R.K. Sharma et al. “Balance of Power: Dynamic Thermal Management of Internet Data Center”,Jan. 2005I

6. Cyber-physical coupling • Workload type, execution, and allocation policies affect the cooling system power consumption • Distinct workloads induce differences in server power consumption • Some locations in the data center are easier to cool than others

7. Cyber-physical coupling-Example • Moving jobs(cyber) from servers in zone A to servers in zone B • How will the temperature distribution change? • How will the performance change? • Will this lower the overall power consumption?

8. Data center management problem • Find the best • Job and resource allocation policies • Cooling approach In order to minimize the data center operating cost(power + performance), subject to • Temperature constraints

9. Outline • Introduction • Cyber-physical model • Control approach • Simulation results • Discussion

10. Cyber-physical model • Computational network • Event driven system(wl distribution,QoS) • Thermal network • Time driven system(heat.e, p.c, h.p) • Coupling • Server power consumption

11. Computational network model • Classed open queuing network • J job classes • N nodes • It relates • Job arrival rate: • Available and used computational resources • Server power consumption • Quality of service (QoS) cost

12. Computational network variables

13. Job allocation model

14. Server model • Servers are collections of computational resources • Assumptions • Less allocated resources implies lower QoS • Less allocated resources implies lower power consumption values • For each job class, server resources can be represented by a scalar value

15. Server power state • Models available resources at a server • Concept similar to CPU power state • Lower clock frequence • Slower job execution rate • Lower power consumption • Defined over a finite, countable set • For a computational node • Lower power state values • Slower job execution rate • Lower power consumption • Defined over the interval [0,1]

16. Thermal network

17. Thermal network variables

18. Thermal server nodes

19. CRAC units

20. Environment Nods • Data center level model • Neglect the power consumption of Environment nodes. • Zone level model • Model as same as thermal server node.

21. Outline • Introduction • Cyber-physical model • Control approach • Simulation results • Discussion

22. Control approach

23. Data center level cost Formula

24. Data center level cost

25. Outline • Introduction • Cyber-physical model • Control approach • Simulation results • Discussion

26. Simulation • Environment • Job class:J=1; Thermal constraint: 5<T<25; power consumption is 3 cents/KWhr

27. Simulation • Coordinated (proposed MPC) • Uncoordinated algorithm(seperated) • Find the best trade-off between server powering cost and QoS cost • Minimize CRAC power consumption • Disregard thermal-computational coupling • Uniform algorithm(use all resource) • Maximize QoS • Fix CRAC reference temperatures in order to satisfy thermal constraints for the worst case scenario

29. Total cost over time

34. Conclusions • Workload execution and cooling system power consumption are coupled • Model and control approach have to consider both computational and thermal characteristics of a data center • We proposed a model and a control strategy to realize the best trade-off between energy costs and quality of service • Simulation results suggest a coordinated controller can outperform other uncoordinated control

35. Future research directions • Our queueing model disregards job interaction • Is there a better model able to represent job interactions in a data center? • Proposed control strategy for realizing the best trade-off between satisfying user requests and energy consumption • More research is needed to understand what factors are most significant in determining the effectiveness of coordinated control • Which is the best way to aggregate nodes into single entity at higher hierarchy levels?

36. Discussion • Contributions • Shortcomings • Some coefficients come from single data center statistical results • Need more workload

37. QoS Cost QoS=job execution rate-job arrival rate Back