A Cyber-Physical Systems Approach to Energy Management in Data

CentersPresented 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. 2005 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

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