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Reducing Data Center Energy Consumption via Coordinated Cooling and Load Management

Reducing Data Center Energy Consumption via Coordinated Cooling and Load Management. By: Luca Parolini, Bruno Sinopoli, Bruce H. Krogh from CMU Presentation: Liang Hao. Motivation. REDUCING the ever growing electricity consumption in data centers

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Reducing Data Center Energy Consumption via Coordinated Cooling and Load Management

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  1. Reducing Data Center Energy Consumption viaCoordinated 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 • This layer interacts with external world through electricity consumption • Each node in the thermal network has an input temperature Tin[], an output temperature Tout[] and an electrical power consumption pw[W]

  7. Modeling(3): Server nodes • Server node is combined of thermal server node and computational server node • We assume every server node has a finite number of possible states denoted by the set P. A state p determines the mean execution rate and the power consumption pw, and pw is positively related to

  8. Modeling(4): Server nodes • We model the computational server node as a G/M/1 queue,while the service time is exponentially distributed with parameter (p(t)) • the thermal part of server node can be modeled as a first-order linear time-invariant (LTI) system defined by the following differential equation:

  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 finiteCMDP we have to identify: a finite set X of states, a finiteset A of actions from which the controller can choose at each step t = k * , a set Pxayof 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. CMDP Formulation • 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

  19. Simulation Results

  20. 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

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