1. Copyright © 2011, MBL@CS.NCTU Performance Evaluation of a Green Scheduling Algorithm for Energy Savings in Cloud Computing Truong Vinh Truong Duy; Sato, Y.; Inoguchi, Y.; Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on

2. Copyright © 2011, MBL@CS.NCTU Outline Introduction Understanding power consumption The Neural Predictor The Green Scheduling Algorithm Experimental Evaluation Performance Evaluation Conclusion Reference

3. Copyright © 2011, MBL@CS.NCTU Introduction Research shows that running a single 300-watt server during a year can cost about $338, and more importantly, can emit as much as 1,300 kg CO2, without mentioning the cooling equipment [2]. In this paper, we aim to design, implement and evaluate a Green Scheduling Algorithm integrating a neural network predictor for optimizing server power consumption in Cloud computing environments by shutting down unused servers.

4. Copyright © 2011, MBL@CS.NCTU Introduction the algorithm first estimates required dynamic workload on the servers. Then unnecessary servers are turned off in order to minimize the number of running servers, thus minimizing the energy use at the points of consumption to provide benefits to all other levels.

5. Copyright © 2011, MBL@CS.NCTU Understanding power consumption Figure 1. CPU utilization and power consumption.

6. Copyright © 2011, MBL@CS.NCTU Understanding power consumption Figure 2. State transition of the Linux machine.

7. Copyright © 2011, MBL@CS.NCTU Understanding power consumption Figure 3. State transition of the Windows machine.

8. Copyright © 2011, MBL@CS.NCTU System Model Figure 4. The system model.

9. Copyright © 2011, MBL@CS.NCTU System Model(cont.) A request from a Cloud user is processed in several steps as follows. 1.Datacenters register their information to the CIS Registry. 2.A Cloud user/DCBroker queries the CISRegistry for the datacenters’ information. 3.The CISRegistry responds by sending a list of available datacenters to the user. 4.The user requests for processing elements through virtual machine creation. 5.The list of available virtual machines is sent back for serving requests from end users to the services hosted by the user.

10. Copyright © 2011, MBL@CS.NCTU The Neural Predictor Figure 5. A three-layer network predictor.

11. Copyright © 2011, MBL@CS.NCTU The Neural Predictor where O c is the output of the current node, n is the number of nodes in the previous layer, x c,i is an input to the current node from the previous layer, w c,i is the weight modifying the corresponding connection from x c,i, and b c is the bias.

12. Copyright © 2011, MBL@CS.NCTU The Neural Predictor In addition, h(x) is either a sigmoid activation function for hidden layer nodes, or a linear activation function for the output layer nodes.

13. Copyright © 2011, MBL@CS.NCTU The Green Scheduling Algorithm Figure 6. Pseudo-code of the algorithm.

14. Copyright © 2011, MBL@CS.NCTU Experimental Evaluation Figure 7. The modified communication flow.

15. Copyright © 2011, MBL@CS.NCTU Performance Evaluation Figure 8. The NASA and ClarkNet load traces.

16. Copyright © 2011, MBL@CS.NCTU Performance Evaluation TABLE 1. Simulation results on NASA with the best of each case displayed in boldface

17. Copyright © 2011, MBL@CS.NCTU Conclusion This paper has presented a Green Scheduling Algorithm which makes use of a neural network based predictor for energy savings in Cloud computing. The predictor is exploited to predict future load demand based on collected historical demand.

18. Copyright © 2011, MBL@CS.NCTU Reference [1] M. Armbrust et al., “Above the Clouds: A Berkeley View of Cloud computing”, Technical Report No. UCB/EECS-2009-28, University of California at Berkley, 2009. [2] R. Bianchini and R. Rajamony, “Power and energy management for server systems,” IEEE Computer, vol. 37, no. 11, pp. 68–74, 2004. [3] EPA Datacenter Report Congress, http://www.energystar.gov/ia/partners/prod_development/downloa ds/EPA _Datacenter_Report_Congress_Final1.pdf. http://www.energystar.gov/ia/partners/prod_development/downloa ds/EPA _Datacenter_Report_Congress_Final1.pdf [4] Microsoft Environment – The Green Grid Consortium, http://www.microsoft.com/environment/our_commitment/articles/gr een_grid.aspx.

19. Copyright © 2011, MBL@CS.NCTU Thank you!