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

Outline  Introduction  Cyber-physical model  Control approach  Simulation results  Discussion

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

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?

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

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

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?

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

Outline  Introduction  Cyber-physical model  Control approach  Simulation results  Discussion

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

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

Computational network variables

Job allocation model

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

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]

Thermal network

Thermal network variables

Thermal server nodes

CRAC units

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

Outline  Introduction  Cyber-physical model  Control approach  Simulation results  Discussion

Control approach

Data center level cost Formula

Data center level cost

Outline  Introduction  Cyber-physical model  Control approach  Simulation results  Discussion

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

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

Total cost over time

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

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?

Discussion  Contributions  Shortcomings  Some coefficients come from single data center statistical results  Need more workload

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