1. Scheduling for Reduced CPU Energy M. Weiser, B. Welch, A. Demers, and S. Shenker

2. Introduction The Energy Saving of A Typical Laptop Computer Backlight and display & Disk Turning off after a period of no use CPU Simple power-down-when-idle techniques Another way? Opportunities Dynamically varying chip speed & energy consumption Cooperation of the operating system scheduler When to use full power & when not

3. CPU Energy Q = C * V Charge = Capacitance * Volts E = Q * V Energy = Charge * Volts When charging a capacitor E =1/2 C V 2 => Energy spent charging a gate is proprotional to the square of the voltage

4. Reducing CPU Energy Empirically, lower-voltage circuits have longer settling times SGS-Thompson 8051 CMOS microcontroller 6 Mhz @ 5V 4.5 MHz @ 3.3 V 3 MHz @ 2.2 V => max clock rate is proportional to voltage The relationship is close to linear for an interesting range of voltages - 5V to 2.2V Executing the same # cycles at a lower voltage and a slower clock speed results in a net power savings Adjust the CPU speed+voltage in response to scheduling demands

5. An Energy Metric for CPUs MIPJ : Millions of instructions per joule = MIPS/WATTS No effect on changes in clock speed Opportunity For Quadratic Energy Savings As the clock speed is reduced by n, energy per cycle can be reduced n 2 Three methods to achieve this Voltage reduction Reversible logic Adiabatic switching

6. An Energy Metric for CPUs - cont’d Voltage Reduction E/clock is directly proportional to V 2 Lower-voltage, slower-clock chip; less energy per cycle Reducing The Energy Consumption The same # of cycles but lower voltage Ex: a task with 100ms deadline Method 1 50ms - full speed;50ms - idle Method 2 100ms - half speed at half voltage Energy consumption: 4:1

7. Approach of This Paper Energy Saving Technique The fine grain control of CPU clock speed Running slower and at reduced voltage Evaluation Using trace-driven simulation Goal To evaluate the energy savings To measure the effect of running too slow

8. Trace Data From the UNIX scheduler Workloads S/W dev., documentation, e-mail, simulation,... Typing, scrolling Time stamp: microsecond Sleep events: wait on hard, soft events Hard events: disk wait, page fault Soft events: keystroke, awaiting network packets Soft idle can be eliminated by rescheduling Hard idle is mandated by a wait on a device

9. Assumptions Simulation Soft events belongs to idle periods No reordering of trace data events Using no energy when idle Taking no time to switch speeds No consideration of > 30 second period of greater than 90% idleness Lower bound to practical speed: 1.0 5 V 0.66 3.3 V 0.44 2.2 V 0.2 1.0 V

10. Scheduling Algorithms OPT (unbounded-delay perfect-future) Taking the entire trace Stretching all the run times to fill all the idle times Imaginary batch job with perfect knowledge Impractical & undesirable Bad response time FUTURE (bounded-delay limited-future) Taking the future trace of a small window Window sizes: 1 ms ~ 400 sec Impractical but desirable Good response time on a window of 10 to 50 ms

11. Scheduling Algorithms -cont’d PAST (bounded-delay limited-past) Looking a fixed window into the past Assuming the next window will be like the previous one Examine % busy during the pervious interval and adjust speed for the next interval Excess cycles can build up if speed (+voltage) is set too low. => Penalty metric Excess Cycle Penalty At each interval, count up left over cycles that accumulated because you ran too slow Switch to full speed if there were more excess cycles than idle time in the previous interval Hard idle (page fault, disk request) cannot be squeezed

12. Trace Driven Simulation Trace Points Sched: context switch away a process Idle on:enter the idle loop Idle off:leave idle loop to run a process Fork:create a new process Exec:overlay a new process with another program Exit:process termination Sleep:wait on an event Wakeup: notify a sleeping process Traces Short runs during specific tasks, editing etc. Long runs of several hours

13. Evaluation: The Results of Three Algorithms

14. Evaluation: Minimal Voltage & The Excess Cycles Frequency: All the excess cycles previous x-val. Excess cycles: time to run unfinished instructions at full speed Lower min vol. => more cases where excess cycles build up => accumulate in longer interval => peak extends to right

15. Evaluation: Interval Length & Excess Cycles Peak in excess cycles shifts right as interval len. Increases Longer scheduling interval => more excess cycles built up

16. Evaluation: Different Minimum Voltage Limits 2.2 V is almost as good as 1.0 V Relative savings for diff. min. voltages

17. Evaluation: Changing The Inverval Length A longer adjustment period results in more savings

18. Evaluation: Average Excess Cycles [1] Lower min. voltage => more excess cycles Longer intervals => accumulate more excess cycles Energy savings is function of the interval size

19. Evaluation: Average Excess Cycles [2]

20. Discussion & Future Work Feedback source other than idle time To classify jobs into Background, periodic, and foreground Schd. Order: periodic, foreground, background No Reordering vs. Reordering Unless a large job mix, reordering not significant. I/O Wait Model: Hard/Soft Thinking valid but good to verify

21. Conclusions Preliminary Results On CPU Scheduling To Reduce CPU Energy Usage Scheduling jobs at different clock rates. Trace Driven Simulation OPT / FUTURE / PAST PAST with a 50ms window 2.2 Volts => 5.0 Volts: This range provides good savings with moderate penalty Power savings up to 50% (3.3V), 70% (2.2V) The Tortoise Is More Efficient Than The Hare.