optimization in data centres - unitn.itfontana/greeninternet/slides/saman halgamuge.pdf · data...

Optimization in Data Centres

Jayantha Siriwardana and

Saman K. Halgamuge

Department of Mechanical Engineering

Melbourne School of Engineering

1Symposium on Sustainability of the Internet and ICT - 25/11/2008

Outline

� Background and Motivation

� Overview and scale of data centre energy

consumption

� Problems of data centres in long term

� Possible strategies to tackle problems

� Conclusions


Outline



consumption

� Current data centre designs



� Conclusions


Background and Motivation (1/2)

� What are data centres� Server farms, IT centres, Computer

rooms, etc.

� Why they are important� Centralized management

� Powerful computation capabilities

� Backbone of internet infrastructure

� Why optimisation of thermal performance is important� Improve reliability

� Reduce system down time

� Save energy cost (more than 40% are cooling cost)


Background and Motivation (2/2)

� During the 15 year or more lifetime of the data

centre, it receives series of equipment upgrades.

� Smaller server size and greater processing speeds

mean more power per m2 in the data centre.

� The cooling system needs to be optimised.

CFD Modelling of data centre to ensure efficient cooling


Outline



consumption



� Conclusions


Overview of data centre energy consumption

� Energy consumed by data centres grow 12%

annually.

� Accounted for 1.5% of the US total electricity

consumption (2006)

Power conversion

& distribution

7%Servers

51%Air conditioning

42%

Only half of energy is used

for powering servers

Typical data centre

energy usage(Source: US Department of Energy

Data Center Energy Efficiency Program

Publications)


Power trends in data centre equipment

Graph based on data centre equipment manufacturers’

forecast of energy consumption of their own products.

(Copyright : The Uptime Institute)


Data centre efficiency improvement targets

� Data centre Infrastructure Efficiency

� Both power conversion and cooling systems can be optimized to save energy

� 10% energy saving target in all US data centre means:

� 10.7 billion kWh

� Equivalent to electricity consumed by 1 million typical households a year.

� Reducing green house emissions by 6.5 million metric tons per year

0 5.

Energy for IT EquipmentDCiE

Total Energy for Data Center=

<


Current data centre design standards (1/2)

� Features

� Raised tile floor

� Hot aisle, cold aisle server rack placement

� Cold air floor rate optimised to reduce hot air

recirculation


Current data centre design standards (2/2)

� Other data centre architectures

Raised floor supply / Ceiling return

Non raised floor / ceiling supply

Raised floor supply / ceiling supply


Outline



consumption



� Conclusions


Problems of data centres in long term (1/3)

� Heat Recirculation

� Hot exhaust air recirculated to cold aisles

� Recirculating air flow is extremely complex

� Causes unexpected server rack inlet temperature rises

� Occurs due to

� Uneven distribution of workload

� Uneven thermal dissipation of data centre equipment

� Non optimised air flow rates of CRACs



� Cold air short circuiting

� Cold air flows directly back to CRAC

� Causes unexpected temperature rise at server inlets

� Occurs due to

� Poor air flow within the data centre

� Non optimised air flow rates of CRACs



� Hot spots

� Unusually hot locations in data centres

� Occurs due to

� Poor air flow within the data centre

� Uneven distribution of workload

� Forces data centre managers to overcool the facility


Outline



consumption



� Conclusions


Strategies to tackle problems

� Use data centre equipment as long as possible.

� Competition, new services and technological advancements limit the equipment lifetime.

� Optimisation of thermal performance

� Maintain maximum possible server inlet temperature

needed for cooling

� Schedule tasks for servers to optimize thermal

dissipation (avoid hot spots)

� Schedule tasks for servers to minimize heat recirculation

Thermal Aware Task Scheduling


Optimisation of thermal performance (1/2)

� Thermal aware task scheduling

� How to divide a total task C among N servers to finish it with minimal energy cost?

� Workload of each server is calculated to minimize its thermal interference to other servers.

� Thereby, minimize the hot air recirculation making it

possible to increase cooler air temperature.

Data centre

task schedulerTask {100}

…

…

2520 2015

30 4015 5


Optimisation of thermal performance (2/2)

� Thermal aware task scheduling

DPTT += supin

Server

node Inlet airflow

(mixture of supplied

cold air and

recirculated hot air)

Outlet air flow

i

outT

∑+

n

iisup PdT

Server inlet

air temperature

Supplied cold

air temperature

Heat recirculation

coefficient matrix

Server power

consumption

Can be calculated

using CFD

Proportional to

server work load


Our approach to the problem

� Modelling thermal energy distribution in data

centres (passive versus active strategies)

� Thermal aware task scheduling for heterogeneous

data centres.

� Optimise multiple online and offline tasks

� Optimise heterogeneous data centre configurations

� Optimisation of data centre upgrading for minimum

possible energy usage

� Thermal interference minimisation among server racks

� Looking into alternative cooling technologies

(Liquid cooling)


Conclusions

� Data centres attract growing concerns on their

large power consumption.

� Reasonable efforts are made for data centres to

be energy efficient at initial design stage.

� However, existing data centre designs can fail to

perform energy efficiently over time

� Thermal performance is a key concern in

upgrading

� Thermal aware task scheduling could be used to

keep cooling costs low in long term

optimization in data centres - unitn.itfontana/greeninternet/slides/saman halgamuge.pdf · data...

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