1. Xi He Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester, NY 14623 xi.he@mail.rit.edu 1 THERMAL-AWARE RESOURCE MANAGEMENT SYSTEM

2. OUTLINE 2  Introduction  Motivation  Thermal-aware Resource Management Framework  A Motivational Example  Thermal-aware Task Scheduling Algorithm  Related Work  Conclusion

3. INTRODUCTION  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]. 3 [1] http://www.energystar.gov/ia/partners/prod_development/downloads/EPA_Datacenter_Report_Congres s_Final1.pdf

4. 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 Cooling System Data Center Level 4

5. MOTIVATION 5 Why Resource Management Framework? 1. How can end users easily get access to remote resources? 2. How can end users collaborate with each others and integrate resources into their research project? 3. How can make resources efficient? 4. How can the administrator monitor and manage different geographically distributed resources?

6. MOTIVATION 6 Why temperature? 1. System reliability 2. Cooling energy cost

7. 7 ARCHITECTURE OVERVIEW

8. 8 Relationship between workload and temperature A MOTIVATIONAL EXAMPLE

9. 9 Relationship between workload and temperature A MOTIVATIONAL EXAMPLE

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 job1  node2

11. SYSTEM MODEL Where, R indicates the racks in the data center. R is composed of N racks. rack i is the ith rack of R. 11

12. SYSTEM MODEL Where, rack i is composed of M nodes. node ij is the jth node in the rack rack i. node ij has 2 properties. n speed indicates the processor’s performance. temp stands for the node’s current temperature. 12

13. SYSTEM MODEL There are L jobs submitted to be scheduled. j k is the kth job which comes at t arrive. The task contains n instruction Million Instruction. f temp is a temperature predict function. Its input is the node the task is about to run on. Its output is the estimated temperature of the node after the task finishes. 13

14. PROBLEM DEFINITION 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 S, the sum of temperature increase on the nodes caused by the execution of jobs. 14

15. ALGORITHM 15

16. RELATED WORK 16 [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.

17. RELATED WORK 17 [4] Kyong Hoon Kim; Buyya, R.; Jong Kim; Power Aware Scheduling of Bag-of-Tasks Applications with Deadline Constraints on DVS-enabled Clusters. Cluster Computing and the Grid, 2007. Seventh IEEE International Symposium on 14-17 May 2007 Page(s):541 - 548

18. CONCLUSION The research is ongoing. 18

19. THANK YOU Questions? Comments? Suggestions? 19