1. Reducing Data Center Energy Consumption via Coordinated Cooling and Load Management By: Luca Parolini, Bruno Sinopoli, Bruce H. Krogh from CMU Presentation: Liang Hao

2. Motivation  REDUCING the ever growing electricity consumption in data centers  COORDINATING cooling and load management which is now mostly independent

3. Previous Work  Computational fluid dynamic models to optimize the delivery of cold air  Optimal load-balancing policy  Temperature-aware manner

4. Modeling

5. Modeling(1): Computational network  Composed of servers nodes that interact through the exchange of workloads  This layer interacts with the external world by exchanging jobs

6. Modeling(2): Thermal network

7. Modeling(3): Server nodes

8. Modeling(4): Server nodes

9. Modeling(5): CRAC nodes  Tin, Tout, Tref  If Tref <= Tin, Tout would tend to Tref  Else Tout would tend to Tin  pw = f(Tin, Tout)

10. Modeling(6): Environment nodes  pw = 0  Tin, Tout

11. Modeling(7): Control Inputs  Controllable variables: the computational workload exchange, the server node power states and the CRAC node reference temperature

12. CMDP Formulation  In order to formulate our optimization problem as a finite CMDP we have to identify: a finite set X of states, a finite set A of actions from which the controller can choose at each step t = k *, a set P xay of transition probabilities representing the probability of moving from a state x to a state y when the action a is applied, and a function c : X £A ! R of immediate costs for each time step. The total cost over a given time horizon is the sum of the cost incurred at each time step.

13. CMDP Formulation

14.  Server nodes n=3  CRAC node r=1  Environment node e=0  Discrete-time model with time step

15. CMDP Formulation: Simplification  Server1 and server2 do not exchange tasks  Ignore electricity consumption by server3, the scheduler  The overall computational network workload exchange is reduced to the choice of the mean value of s

16. CMDP Formulation  Quantize Tout and Tref

17. Solution  Use the Markov Decision Process Toolbox for MATLAB to solve the CMDP problem

18. Simulation Results

21. What’s insight  Build a model that can reflect the real problem and solve it using mature solutions  To transform a real problem into a mathematical model, we quantize sequential variables into discrete ones