NUI Galway – UL Alliance First Annual ENGINEERING AND - ARAN ...

Data Centre Energy Efficiency
Mark White Jonathan Hanley
Jonathon Shannon Hugh Melvin Michael Schukat
Marcus Keane* Maria Linnane** Wesley Reilly**
m.white1@nuigalway.ie j.hanley3@nuigalway.ie
Abstract
Rising energy costs have focused the attention of data
centre management. Processing power and storage
capacity are no longer the only concern. The operating
cost of hardware and the associated heating and
ventilation systems (HVAC) are becoming more
important, particularly in today’s economic climate.
Both existing and higher capacity data centres (being
rolled out to meet the rising demands of cloud-based
computing) are increasingly required to include these
performance metrics in their monitoring systems.
1. Research Aim
The aim of this research is to specify, design &
develop an integrated hardware / software system which
will monitor and report data centre energy efficiency
using the ISS (Information Solutions & Services) data
centre in NUI Galway as a test bed.
Employing a wireless sensor network for cabinet
temperatures and SNMP (Simple Network Management
Protocol) polling for all other values, the resultant
application will provide real-time information to assist
ISS personnel manage the energy efficiency of their
data centre.
2. System Architecture
The system will consist of two main data capture
schemes illustrated in the figure below.
Figure 1. System architecture
Temperature and humidity data are captured via a
network of Tyndall 25mm platform wireless sensors.
The sensors sample every 30 seconds and transmit the
raw data values to a base station in the Data Centre,
using the IEEE 802.15.4 protocol. This data is then
processed and logged in an SQL database with any
25
network issues flagged to a system administrator via
email.
Additional data centre parameters are captured using
the SNMP protocol. SNMP allows for a wide range of
values to be captured on equipment attached to the ISS
network. Of interest in this application are; CPU Load
(Process queue average), CPU Draw (Watts), CPU Fan
Speed (RPM), HVAC Draw (Watts). These values are
collected using a C#.NET SNMP poller, running on the
base station and transmitting the collected data to a SQL
database.
3. Visualisation & Reporting
External access to the raw dataset is provided by a
website hosted in the IT Department.
(www.enformatics.eu)
Authorised users can access a clickable floor plan of
the data centre. If readings are available for a given
cabinet, then this cabinet will be clickable and the user
is redirected to the reporting page.
The user selects one or more sensors from the
cabinet’s available list, chooses the time range for the
report and clicks ‘Report’. A chart is generated where
visual comparisons can be made between sensors.
4. Implementation
It is proposed to capture data sets across 3 cabinets
within the data centre for a period of two weeks.
Cabinets will be chosen so that different combinations
of server types and general loading conditions will be
monitored. Each cabinet will be fitted with six Tyndall
sensors to monitor temperatures throughout the rack.
The resultant data sets, after analysis, will allow for
identification of hot-spots within racks and the data
centre as a whole as well as correlation analysis.
Through balancing server loads and improving data
centre design in terms of air flow it is hoped that overall
efficiency can be improved and, more importantly,
quantified.
5. Future Work
The basis upon which good decisions are made is
good information. Once reliable data is being received
and analysed, we propose to incorporate an artificial
intelligence agent into an analysis and modelling
engine. This will aid data centre staff making both
short-term optimisation decisions and formulating
energy-saving strategies for the medium to long-term.
[*] Dept of Civil Engineering, NUI Galway.
[**] ISS NUI Galway.