XI HE Computing and Information Science Rochester Institute of Technology Rochester, NY USA...

XI HEComputing and Information

ScienceRochester Institute of Technology

Rochester, NY [email protected]

Rochester Institute of Technology Service Oriented Cyberinfrastructure Lab

The data is collected and provided by the Center for Computational Research (CCR) [3] at State University of New York at Buffalo from the U2 cluster.U2 clusters is composed of 1056 Dell PowerEdge SC1425 nodes each of which has 2 processors.

The data from CCR is collected in a 30-days period, from 2009-02-20 to 2009-03-22.


http://www.ccr.buffalo.edu/display/WEB/Node+Map+-U2




We can model the data center ΓΓ = {C, J }

Where, C indicates the U2 cluster, and J represents the jobs submitted to the data center to be scheduled for a period of time.


Where, the cluster C is composed of 1056 nodes. ni stands for the ith node in the cluster. T indicates the temperature in node ni which contain p1 and p2 2 processors.


Where, ji denotes the ith job running on a set of nodes. ji was submitted to the queue at the time of tsubmit and was queued at the time of tq . The job started execution at ts and ended execution at te . It required cpus processors and consumed tcpu CPU time.


Job distribution in terms of its execution time.


Job distribution in terms of its size.


Job distribution in terms of its respond time.


Job arrival rate distribution


Temperature distribution


Relation between workload and temperature


XI HEComputing and Information

ScienceRochester Institute of Technology

Rochester, NY [email protected]


BackgroundProblem Definition

Related workData collectionSystem modelAnalysis result

Conclusion


61 billion kilowatt-hours of power in 2006, 1.5 percent of all US electricity use, worthy of $4.5 billion. Energy usage doubled between 2000 and 2006 Double again by 2011 [1]



Dynamic Voltage Scaling hardware LevelDynamic Frequency ScalingFan Speeding Scaling

Dynamic Voltage Scaling hardware LevelDynamic Frequency ScalingFan Speeding Scaling

Platform Virtualization Software LevelApplication Power Management


Job Scheduling Middleware LevelVirtual Machine Scheduling


The basic idea is make the servers

use as little electricity as

possible.

The basic idea is make the servers

use as little electricity as

possible.

Cooling System Data Center LevelWater management



Dynamic Voltage Scaling Hardware LevelDynamic Frequency ScalingFan Speeding Scaling






Platform Virtualization

makes the server consolidation

possible

Platform Virtualization

makes the server consolidation

possible








My Research Focus

My Research Focus














Save the cooling power and

recycle water

Save the cooling power and

recycle water

Now we define the problem of thermal-aware scheduling as follows:

Given a set of jobs. Find an optimal schedule to assign each job to the nodes to minimize the temperature increase in the data center.


The research is ongoing.Next step is to predict the future temperature. Then according to the future temperature, schedule the jobs.


Thermal-aware Task Placement in Data Centers [2]


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12

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78

910

1112

S1

S3

S5

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1

2

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S1

S2

S3

0

500

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S1

S2

S30

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Different task assignments lead to different power consumption distributions

Different power consumption distributions lead to different temperature distributions

Different temperature distributions lead to different total energy costs.

For example, you have different cooling cost because you have to ensure the highest temperature is below the redline.

Server task distribution

Power consumption distribution

Temperature distribution Energy cost

Questions? Comments? Suggestions?


[1] http://www.energystar.gov/ia/partners/prod_development/downloads/EPA_Datacenter_Report_Congress_Final1.pdf[2] Tang, Qinghui; Gupta, Sandeep Kumar S.; Varsamopoulos, Georgios; Parallel and Distributed Systems, IEEE Transactions on Volume 19, Issue 11, Nov. 2008 Page(s):1458 - 1472 [3] “the Center of Computational Research.” [Online]. Available: http: //www.ccr.buffalo.edu/display/WEB/Home


Node1Temp:113

F

Node1Temp:113

F

Node2Temp:115

F

Node2Temp:115

F

Data CenterData Center

Task1 (2G*1Hour)Task1 (2G*1Hour)

Task2 (1G*3Hour)Task2 (1G*3Hour)

XI HE Computing and Information Science Rochester Institute of Technology Rochester, NY USA...

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