GreenLight Project

Data Collection meetingData Collection meeting

Claudiu Farcas, Filippo Seracini, Ingolf Krueger

2

GreenLight Project Our objectives

• Create an energy model for the BlackBox– Understand energy consumption related to task execution– Optimize task scheduling to decrease the environmental

impact– Provide to user different modes of computation (i.e. max

performance, max energy saving, min computational cost, etc)

• Create the cyberinfrastructure to – Manage and control the Blackbox – Run tasks– Provide green data related to task execution

3

GreenLight Project First steps

• Requirement engineering with the different teams involved in the project– What are the data sources?– What can be measured?– How is data stored?– How is data represented?– Who wants what?

4

GreenLight Project

Stackeholder ProviderConsumer

Networking (Brian)

Ingolf

DC Power Testing

Tom/Phil (storage)

Tajana

Ingolf

Falko

Gupta

Amin

Research Distribution

• GreenLight Researchers are interested in both producing and consuming greening data such as temperature and power measurements

Greenlight Instrument

Single Instrument

Aggregate Instrument

*

Research

Greening ON

Greening OFF

5

GreenLight Project Existing Capabilities

• Default Sun Monitoring interface

6

GreenLight Project Initial Domain Models

• Data Collection Points

Node

Server ConveyStorage

Rack

Thumper Database

GPGPU

Processor

Hard Drive

Memory

Network Interface

Temperature

Heat Exchanger

Fan

Fan Slice

Speed %

5

Panel

A B

Amps

Volts

Watts

2

PDU

IP Address

Phase

3

Phase 1

BlackBox

FrequencyPhase 2

Phase 3

2

1

8

Relative Humidity

Dewpoint

Outlet Inlet

Water

BB1

BB2

BB3HumidityExternal

Internal

7

GreenLight Project Resource Allocation

• Requirements elicitation at the 2nd meeting

8

GreenLight Project Next Steps

• Define a model for “greening data”

• Define a resource model for GreenLight resources (e.g., CPUs, VMs, nodes, etc)

• Model GreenLight resources in the context of the Rich Services framework (SOA derivate)

• Identify usage policies and adequate scheduling algorithms to improve efficiency

• Create the Rich Service infrastructure to use and manage the Blackbox

• Expose the “green data” as web services & portal