1. Zhou Peng, Zuo Decheng, Zhou Haiying Harbin Institute of Technology 1

2. Harbin institution of technology 1.Introducation 2.Workload effect on Energy effective 3.Conclusion & Future works 2

3. Harbin institution of technology Green computing is imperative Increasing of computers Increasing of energy cost Increasing of Carbon emissions 3

4. Harbin institution of technology Moores law Moores law for energy effective 4

5. Harbin institution of technology Explosive growth of the tasks and complexity Linear growth of energy density in battery Exponential growth of code e.g. Linux code in tar.gz format increase from 117K(0.11) to 109M(3.11.1) Explosive growth of applications; e.g. apps for android and apple Explosive growth of amount of computation; e.g.AI & Big data Exponential growth of code e.g. Linux code in tar.gz format increase from 117K(0.11) to 109M(3.11.1) Explosive growth of applications; e.g. apps for android and apple Explosive growth of amount of computation; e.g.AI & Big data Linear improve of battery 5 VS Battery life become shorter and shorter e.g. smart phones Battery life become shorter and shorter e.g. smart phones

6. Harbin institution of technology Main technologies to improve energy effective Hardware level: Low power devices System level: Power-management mechanisms in different levels Application level: Consolidate with virtualization Power-management mechanisms Circuit level: Clock-gating System level: DPM Processor level: DVFS/DFS/DVS, C-state 6 To Shutdown unused component or circuit

7. Harbin institution of technology According to the present researches: C-state can save up to 44% [1] energy DVFS can save 13% [2] to 70% [3] energy Limitation of present research All the results come from particular system with special application or SPAC CPU. Few works can consider the effect of workload to the energy consumption. 7

8. Harbin institution of technology Two solutions: slow down & race-to-halt Objectives: To evaluate the energy effective of DVFS & C-state with different task models 8 Slow downrace-to-halt Typical technologyDVFS C-state Runtime powerDynamic & lowHigher Time to finish taskLonger short Deadline missHigh riskLower risk Energy effectiveSave lots of energy DVFS vs C-state: which is better in energy effective?

9. Harbin institution of technology 1.Introducation 2.Workload effect on Energy effective 3.Conclusion & Future works 9

10. Harbin institution of technology 10 Relationship of the power and the frequency: Relationship of the voltage and frequency: k: is a circuit dependent constant V t : is the threshold voltage C : is the capacitance of the transistor gates f : is the frequency V dd : is the supply voltage of the device. P static : represents power consumed from leakage mechanisms., Note that: The operation frequency almost has a linear relationship with voltage. BUT, decreasing the frequency and keeping the voltage constant does not contribute much to energy saving. It just saves the cost of cache misses [11].

11. Harbin institution of technology DVFS Modeling Defining the amount of computation/ instructions for a task/workload is W, and then within a period of run-to-completion, the energy consumption of task is is energy consumption based on dynamic power is energy consumption based on leakage power Summary: DVFS: compute the energy consumption of processor but ignore the energy cost of cache misses. 11 C: capacitance f : frequency V dd : runtime voltage P static : leakage power V peak : peak voltage T r : Time to finish task T s :Time to sleep W: workload, the instruction cycles of a task T r+ T s = W/f d

12. Harbin institution of technology C-state Modeling Defining the amount of computation/ instructions for a task/workload is W, and then within a period of run-to- completion, the energy consumption of task is T r+ T s is the interval time of a task run-to-completion based on DVFS T r+ T s = W/f d Summary: C-state operates at higher voltage, So C-state finish a task faster than DVFS. If all the tasks is completed, system changes to sleep mode. is very low, which can be ignored. 12 C:capacitance f :frequency V dd : runtime voltage P static : leakage power V peak : peak voltage T r : Time to finish task T s :Time to sleep W: workload, the instruction cycles of a task

13. Harbin institution of technology The derivative of energy model 13 The extreme point in energy model shows that Workload W is not the key influence factor to the minimal energy consumption The minimal energy consumption is only depended on the characteristics of devices In order to minimize the energy consumption and also try to find the best voltage, we can get the derivative of energy models

14. Harbin institution of technology C-state becomes popular because P static (leakage power) increase effects We can consider time t as the workload arrival time, when, rewrite the equation 14 In order to evaluate the energy effective of DVFS and C-state We get the difference value of the two energy models

15. Harbin institution of technology For Poisson distribution workload The average arrival rate of task is λ 0 ; The average interval time of task is t=1/ λ 0 Summary: DVFS and C-state save the same energy in this situation When deadline t deadline < t, C-state saves more energy than DVFS; When the arrival rate λ>λ 0, DVFS is better than C-state 15

16. Harbin institution of technology 16 For Periodic distribution workload C-state saves more energy if and only if the deadline is smaller than period, i.e. t deadline < t; DVFS does not shutdown the processor after the task finished.

17. Harbin institution of technology 1.Introducation 2.Workload effect on Energy effective 3.Conclusion & Future works 17

18. Harbin institution of technology Evaluate the energy effective of DVFS & C-state with different task models The most energy saving voltage is only depended on the characteristics of the device itself. The energy effective of DVFS and C-state is closely related to the arrival rate of the tasks and the features of workloads. For the heavy workload systems, DVFS is better in energy saving than another. The result is consistent with the conclusion in [5]. 18

19. Harbin institution of technology In this paper, we mainly focus on processor and ignore the energy consumption during state transition. So, future works will be: To analyze the effects of cache hit rate on energy effective in the whole system. To take the reliability into consideration. To explore the schedulability analysis methods for the energy and reliability critical system. 19

20. Harbin institution of technology 1. Pavel Somavat. Accounting for the Energy Consumption of Personal Computing Including Portable Devices 2. Rotem, E., et al. Energy Aware Race to Halt: A Down to EARtH Approach for Platform Energy Management. Computer Architecture Letters. 3. Shekar, V. and B. Izadi. Energy aware scheduling for DAG structured applications on heterogeneous and DVS enabled processors. 4. Valentini, Giorgio Luigi, et al. An overview of energy efficiency techniques in cluster computing systems. 5. Petters, S. M. and M. A. Awan., Slow down or race to halt: Towards managing complexity of real-time energy management decisions. 6. Awan, M. A. and S. M. Petters. Enhanced race-to-halt: A leakage-aware energy management approach for dynamic priority systems. Real-Time Systems 7. Naik, R. Biswas, S., Datta, S.; Distributed Sleep-Scheduling Protocols for Energy Conservation in Wireless Networks. System Sciences, 8. Le Sueur, Etienne, Heiser, Gernot. Dynamic voltage and frequency scaling: The laws of diminishing returns. 9. Le Sueur, E. and G. Heiser. Slow Down or Sleep, that is the Question. 10. Schmitz, M.T., et al.; Energy-Efficient Mapping and Scheduling for DVS Enabled Distributed Embedded Systems. 11. Wan Yeon Lee. Energy-Saving DVFS Scheduling of Multiple Periodic Real-Time Tasks on Multi-core Processors. 12. F. Paterna, et al.Variability-Tolerant Workload Allocation for mpsoc Energy Minimization under Real-Time Constraints 20

21. Harbin institution of technology Thank you!