1. Thermal Aware Resource Management Framework Xi He, Gregor von Laszewski, Lizhe Wang Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester, NY 14623 xi.he@mail.rit.edu 1

2. Outline 2 Introduction Motivation Thermal-aware Resource Management Framework Motivational Examples System Model and Problem Definition Thermal-aware Task Scheduling Algorithm Conclusion

3. Introduction 3 Distributed Collaborative Experiment

4. Introduction 4 61 billion kilowatt-hours of power in 2006, 1.5 percent of all US electricity use costing around $4.5 billion. Energy usage doubled between 2000 and 2006. Energy usage will double again by 2011[1]. 61 billion kilowatt-hours of power in 2006, 1.5 percent of all US electricity use costing around $4.5 billion. [1] http://www.energystar.gov/ia/partners/prod_develop ment/downloads/EPA_Datacenter_Report_Congress_Fi nal1.pdf

5. Dynamic Voltage Scaling Hardware Level Dynamic Frequency Scaling Dynamic Voltage Scaling Hardware Level Dynamic Frequency Scaling Virtualization Software Level Job Scheduling Middleware Level Virtual Machine Scheduling Job Scheduling Middleware Level Virtual Machine Scheduling Introduction 5 Cooling System Data Center Level

6. Motivation Why thermal-aware resource management framework? – To allow end users easily collaborate with each other and get access to remote resources. – To implement Green Computing. – To monitor temperature situation in Data Center. 6

7. Architecture Overview 7

8. 8 Different types of task-temperature profiles Motivational Examples

9. 9 Task-temperature profile (Buffalo Data Center) Motivational Examples

10. 10 job 1 =(0,2,20,f(job 1 )) job 2 =(0,1,40,f(job 2 )) node 1 =40C node 2 =32C node 3 =34C node 4 =32C node 1 =40C node 2 =40C node 3 =40C node 4 =40C job 1  node 4 job 1  node 2 job 2  node 3 job 1  node 1 job 1  node 2 job 2  node 3 max=40C σ=0 node 1 =48Cnode 2 =40C node 3 =40CNode 4 =32C Max=48CΣ=5.6 Motivational Examples

11. System Model 11 Where, node i indicates ith node in the data center; Each node has a temperature-time profile that indicates the node’s temperature value over time.

12. System Model 12 Where, t start indicates the starting time of job; The job needs node num processors and lasts t exe ; f temp (t) is a function caused by the execution of the job based on the execution time of the job.

13. Problem Definition 13 Given a set of jobs. Find an optimal schedule to assign each job to the nodes to minimize computing nodes’ temperature deviation. Where, ΔTemp is the temperature increase that job k causes.

14. Problem Definition 14 We use standard deviation as the metric for measuring the temperature distribution.

15. Algorithm 15

16. Algorithm 16 1.Select the node which has the lowest “current” temperature. 2.Sort jobs in descending order of the temperature rise they caused. 3.For each job 4. Assign the job to the selected node. 5. Update the node’s temperature-time profile. 6. Select the node which has the lowest “current” temperature. 7.End For 8.If a node’s temperature exceed the threshold, don’t choose it in the next round and let it cool down.

17. Experiment 17 Task temperature profile Execution Time(s) Temperature

18. Experiment 18 iCore7 cooling profile Time(s) Temperature

19. Result 19 σ ( Thermal aware task scheduling ) σ ( Random task scheduling ) N=10 M=30 6.213.4 N=20 M=30 5.311.1 N=20 M=40 7.316.5 N indicates the number of job groups M indicated the number of jobs in each group

20. Related Work In [1], [2], power reduction is achieved by the power- aware task scheduling on DVS-enabled commodity systems which can adjust the supply voltage and support multiple operating points. [1] K. H. Kim, R. Buyya, and J. Kim, “Power aware scheduling of bag-of- tasks applications with deadline constraints on dvs-enabled clusters,” in CCGRID, 2007, pp. 541–548. [2] R. Ge, X. Feng, and K. W. Cameron, “Performance-constrained distributed dvs scheduling for scientific applications on power-aware clusters,” in SC, 2005, p. 34. 20

21. Related Work In [3], [4] thermodynamic formulation of steady state hot spots and cold spots in data centers is examined and based on the formulation several task scheduling algorithms are presented to reduce the cooling energy consumption. [3] Q. Tang, S. K. S. Gupta, and G. Varsamopoulos, “Thermal-aware task scheduling for data centers through minimizing heat recirculation,” in CLUSTER, 2007, pp. 129–138. [4] J. D. Moore, J. S. Chase, P. Ranganathan, and R. K. Sharma, “Making scheduling ”cool”: Temperature-aware workload placement in data centers,” in USENIX Annual Technical Conference, General Track, 2005, pp. 61–75. 21

22. CONCLUSION My accomplishment in the research:  Grid computing and Cloud computing literature review  Make an analyzing study on Buffalo data center operation.  Scheduling algorithms literature review 22

23. 23

24. Conclusion A novel framework to solve resource management problem. A thermal-aware task scheduling for data center, which will save a lot of cooling energy cost. Future work – Investigate other thermal characteristic of data centers. – Continue the development of thermal-aware resource management framework. 24

25. PUBLICATION G. von Laszewski, F. Wang, A. Younge, X. He, Z. Guo, and M. Pierce, “Cyberaide javascript: A javascript commodity grid kit,” in GCE08 at SC’08. Austin, TX: IEEE, Nov. 16 2008. [Online]. Available: http://cyberaide.googlecode.com/svn/trunk/papers/ http://cyberaide.googlecode.com/svn/trunk/papers/ 08- javascript/vonLaszewski- 08- javascript.pdf G. von Laszewski, A. Younge, X. He, K. Mahinthakumar, and L. Wang, “Experiment and workflow management using cyberaide shell,” in 4th International Workshop on Workflow Systems in e-Science (WSES 09) in conjunction with 9th IEEE International Symposium on Cluster Computing and the Grid. IEEE, 2009. 25

26. 26Appendix

27. Appendix 27

28. Appendix 28