|
|
| Goal
and Rationale |
|
With the prevailing usage of
high density blade servers, the heat dissipation density of data
centers
increases exponentially. The high temperature of the data centers will
lead
to higher hardware failure costs. Improperly designed or operated data
centers may either suffer from overheated servers and potential system
failures, or from overcooled systems and paying extra utilities cost.
Minimizing the cost of operation (utilities, maintenance, device
upgrade and
replacement) of data centers is one of the key issues to optimize
computing resources and maximize business outcome.
The goal of this project is to
build a dependable, reliable sensing platform using on board sensors
and ambient sensors to collect temperature, humidity, power consumption
and computer load information. Combining this data with a heuristic
control
algorithm, we can dynamically adjust the thermal environment (by making
smarter job
scheduling decisions, by adjusting
air conditioner capacity, fan speed, frequency and voltage scaling,
etc.) to
achieve a better thermal environment, reduce the cost of operations and
improve
the business output of data centers.
|
|
| In
The Press |
|
Forbes
The Future Is Now “Dynamic
thermal
management of the data center –
Developed in conjunction with Arizona
State
University, this research
enables job scheduler software to take into account the temperature of
servers
or server blades before deciding which data center component should do
the
job. The result should be an online thermal control framework that
monitors
and manages data center thermal performance from a holistic viewpoint.
The
researchers say the challenge for the project is to make the system
reactive
so that it knows when servers are starting to fail because of heat
issues.
They say it could be another two years before this project could be
presented
to Intel as a potential product.…”
|
|
| Project
Timeline |
| Timeline |
Achievements |
| 2005 |
Developed abstract heat flow model for data
center and verified with CFD simulation |
| Q4 2005 |
Developed thermal aware scheduling
based on the abstract heat flow model and verified with CFD
simulation |
| Q1 2006 |
Published a paper on thermal aware
scheduling for data centers in DASC 2006 |
| Q2 2006 |
Developed a software architecture for
thermal aware acheduling for Moab
Cluster Manager and successfully demostrated the software architecture
at Research @Intel
Day using the ASU HPC datacenter |
| Q3 2006 |
Demostration of thermal aware scheduling at
Intel Country Fair |
| Q4 2006 |
Published papers on abstract heat
flow
model (in ICISIP 2006) |
| Q1 2007 |
Published a paper on thermal aware scheduling
software
architecture (COMSWARE 2007) |
| Q2 2007 |
Performed power profiling
of Data Center computing equipment Performed analysis
simulations on heterogeneous data center |
| Q3 2007 |
Published a paper on thermal aware scheduling
software
architecture (IEEE Cluster 2007) |
| Q4 2007 |
Performed Simulations on Thermal-aware placement
of queued tasks |
| Q1 2008 |
Performed Performance Simulations of incremental
heuristics |
Q2 2008
|
- Qinghui Tang
successfully defended his PhD thesis
- Paper titled "Energy-Efficient,
Thermal-Aware Task Scheduling for Homogeneous, High Performance
Computing Data Centers: A Cyber-Physical Approach" accepted to
appear in TPDS Special Issue on Power-Aware Parallel and Distributed
Systems.
|
|
|
| Publications |
- Q. Tang, S. K. S. Gupta, and G. Varsamopoulos, Thermal
Aware Task Scheduling for Datacenters through Minimizing Heat
Recirculation. Cluster 2007, Austin TX. Sept. 2007. [PDF]
-
T. Mukherjee, G.
Varsamopoulos and S. K. S. Gupta, Measurement-based Power
Profiling of Data Center Equipment. GreenCom 2007, Austin TX.
Sept. 2007. [PDF]
- M. Jonas, G. Varasamopoulos, and S. K. S. Gupta,
On developing a fast, cost-effective and non-invasive method to derive
data center thermal maps. (Extended Abstract) Workshop on
Green Computing (in conjunction with CLUSTER 2007), Austin, USA,
Sept, 2007. [PDF
| PPT]
-
T.
Mukherjee, Q. Tang, C. Ziesman, and S. K. S. Gupta, Software
Architecture for Dynamic Thermal Management in Datacenters. in
Int’l Conf. Communication System
Software & Middleware (COMSWARE), Jan 2007. [PDF]
-
Q.
Tang, T. Mukherjee, S. K. S. Gupta, and P. Cayton, Sensor-Based
Fast Thermal Evaluation Model For Energy Efficient High-Performance
Datacenters. in Int’l Conf.
Intelligent Sensing & Info. Proc. (ICISIP2006), Dec 2006. [PDF]
-
Q. Tang,
Sandeep. K. S. Gupta, Daniel Stanzione, and Phil Cayton, Thermal-Aware
Task Scheduling to Minimize Energy Usage of Blade Server Based
Datacenters. in 2nd IEEE International
Symposium on Dependable, Autonomic and Secure Computing (DASC'06). [PDF]
|
|
| Research
Issues |
- High-fidelity sensor data collection and
aggregation
- Characterizing the correlation among
temperature
rise, computer load and power consumption
- Building a computational thermal model of a
data
center
- Creating a thermal-aware scheduling algorithm
that incorporates:
- Fault tolerance, prediction, and avoidance
- Thermal interference effects on neighboring
computing nodes (heuristically)
- Mathematically derive an optimal thermal
scheduling algorithm
- Measuring effectiveness of the real-world
solution against the optimal solution
|
|
|
|
| Links |
|
Guideline
- "Thermal Guidelines for Data Processing
Environments" of ASHRAE (2004)
Load Balancing Software
Data Center Related Conference
Organizations
Community, publication and websites
Imote useful sites
Environmental sensors for data center
Power Meters and Systems
Wireless Power Meter
Wireless metering systems
Data Center Products
- ColdFront of Degree
Controls Inc
- Coldfront software: thermal analysis and
design to optimize
- Coldfront NODE temperature
- Coldfront tile
- Idea locations of the placement of sensors
- Sensicast
- EMW Wireless Transceiver
- Temperature, humidity, battery
information
- EMS Management Software
- Cisco Data center
- Liebert Corporation (www. liebert.com),
- a subsidiary of Emerson (NYSE: EMR) and a
part of Emerson Network Power
- precision air conditioner
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