1. System-Level Power-Aware Design Techniques in Real-Time Systems Osman S. Unsal, Israel Koren, System-Level Power-Aware Design Techniques in Real- Time Systems, Proceedings of IEEE, Vol. 91, No. 7, July 2003.System-Level Power-Aware Design Techniques in Real- Time Systems,

2. Power Management Why & What: Power Management?  Rapid growth of worldwide total power dissipation 87M CPUs consumed 160MW in 1992 -> 500M CPUs consumed 9,000 MW in 2001  Battery operated: Laptops, PDAs, Cell phones, Wireless Sensors,...  Heat: Complex Servers (server farms, multiprocessors, etc.)  Power Aware: Maintain QoS while reducing energy Source: Daniel Mosse (CS1651 at Univ. of Pittsburg)

3. Why System-Level Design? Power management at various system layers  Circuit & device level, archiecture & compiler, OS & network design Most existing research regarding power- aware RT systems focus on power-aware RT scheduling  CPU is only a single source of power consumption!

4. Misconceptions about power-aware design Power-aware ≠ low-power (minimize power consumption)  Delay some instruction execution, e.g., to reduce peak power -> Executes longer & consumes more power Decreasing average power does not imply decreasing max power Power ≠ energy efficiency  Energy = ∫power  Perf may degrade resulting in more energy consumption

5. Misconceptions about power-aware design Power-constrained ≠ energy-constrained  solar power (infinite source) vs. battery power Energy-constrained systems do not always target energy minimization  Charge = f(battery capacity, rate of discharge)  Goal is battery lifetime extension

6. Motivations for power-aware design for RT sytems Limited energy-budget & timing constraints, e.g., in space & multimedia applications Limited form factor & low heat dissipation, e.g., in avionics, robotics & space missions Often overdesigned to support timing guarantees under the wosrt case  Tasks do not run until their WCET -> Energy inefficient Fault-tolerance  Reliability via replication -> high power consumption

7. What is system-level power-aware design? Power-awareness embedded in every step of system design Power-compiler can do instruction scheduling to reduce power consumption OS-level heuristic may scale down f and V dd Network-level schems may put the network I/F into standby mode Today, we will focus on OS & network levels

8. OS Level Voltage & frequency scaling I/O devices Power and energy analysis of RTOS Distributed RT systems Soft real-time systems

9. Power Management How?  Power off un-used parts: LCD, disk for Laptop  Gracefully reduce the performance CPU: dynamic power P d = C ef * V dd 2 * f [Chandrakasan-1992, Burd-1995]  C ef : switch capacitance  V dd : supply voltage  f : processor frequency  linear related to V dd Source: Daniel Mosse (CS1651 at Univ. of Pittsburg)

10. Power Aware Scheduling T1T1 D f max time Static Power Management (SPM)  Static slack: uniformly slow down all tasks [Weiser-1994, Yao-1995, Gruian-2000] T2T2 E Static Slack Energy f T1T1 T2T2 idle time T1T1 T2T2 T2T2 T1T1 0.6E time T1T1 T2T2 f max /2 E/4 Source: Daniel Mosse (CS1651 at Univ. of Pittsburg)

11. T1T1 D f max time Dynamic Power Management (DPM)  Dynamic slack: non-worst execution 10% [Ernst- 1997]  DPM: [Krishna-2000, Kumar-2000, Pillai-2001, Shin-2001] T1T1 T2T2 T2T2 f max /2 Static Slack idle E E/4 time T1T1 T2T2 f max /3 0.12E time T1T1 f max /2 Dynamic Slack Multi-Processor  SPM: length of schedule over deadline  DPM ??? Source: Daniel Mosse (CS1651 at Univ. of Pittsburg) Power Aware Scheduling

12. References (Power-Aware RT Scheduling) [Chandrakasan-1992] A. P. Chandrakasan and S. Sheng and R. W. Brodersen. Low-Power CMOS Digital Design. IEEE Journal of Solidstate Circuit, V27, N4, April 1992, pp 473--484 [Burd-1995] T. D. Burd and R. W. Brodersen. Energy efficient cmos microprocessor design. In Proc. of The HICSS Conference, pages 288-297, Maui, Hawaii, Jan. 1995. [Weiser-1994] M. Weiser, B. Welch, A. J. Demers, and S. Shenker. Scheduling for reduced CPU energy. In Operating Systems Design and Implementation, pages 13-23, 1994 [Yao-1995] F. Yao, A. Demers, and S. Shenker. A scheduling model for reduced cpu energy. In Proc. of The 36th Annual Symposium on Foundations of Computer Science, pages 374- 382, Milwaukee, WI, Oct. 1995. [Gruian-2000] F. Gruian. System-Level Design Methods for Low-Energy Architectures Containing Variable Voltage Processors. The Power-Aware Computing Systems 2000 Workshop at ASPLOS 2000, Cambridge, MA, November 2000 [Ernst-1997] R. Ernst and W. Ye. Embedded program timing analysis based on path clustering and architecture classification. In Proc. of The International Conference on Computer-Aided Design, pages 598 – 604, San Jose, CA, Nov. 1997. [Krishna-2000] C. M. Krishna and Y. H. Lee. Voltage clock scaling adaptive scheduling techniques for low power in hard real-time systems. In Proc. of The 6th IEEE Real-Time Technology and Applications Symposium (RTAS00), Washington D.C., May. 2000. [Kumar-2000] P. Kumar and M. Srivastava, Predictive Strategies for Low-Power RTOS Scheduling, Proceedings of the 2000 IEEE International Conference on Computer Design: VLSI in Computers and Processors [Pillai-2001] P. Pillai and K. G. Shin. Real-Time Dynamic Voltage Scaling for Low-Power Embedded Operating Systems, 18th ACM Symposium on Operating Systems Principles (SOSP?1), Banff, Canada, Oct. 2001 [Shin-2001] D. Shin, J. Kim and S. Lee, Intra-Task Voltage Scheduling for Low-Energy Hard Real-Time Applications, IEEE Design and Test of Computers, March 2001 [Zhu-2001] D. Zhu, R. Melhem, and B. Childers. Scheduling with Dynamic Voltage/Speed Adjustment Using Slack Reclamation in Multi-Processor RealTime Systems, RTSS'01 (Real- Time Systems Symposium), London, England, Dec 2001 152 Source: Daniel Mosse (CS1651 at Univ. of Pittsburg)

13. I/O devices Much less work has been done compared to VS (voltage scaling) in real-time systems Swaminathan et al [72]  Power-aware I/O device scheduling for hard real-time systems  Tasks are independent but not periodic  A priori knowledge of task schedule & device usage list required

14. Power & energy analysis of RTOS Power consumption of μc-OS on Fujitsu SPARClite processor  μc-OS: Open-source embedded RT kernel introduced in an earlier lecture for project ideas  Evaluate power consumption by system calls [73] μc-OS, Ecnidna, NOS (baseline “bare-bones” scheduler) [74] RTOS overheads are two to four times higher than NOS Poorly design idele loops can double the energy consumption

15. Power & energy analysis of RTOS Acquaviva [75]  Increasing context switch frequency from 0Hz to 10Khz does not affect energy consumption -> Context switch mechanism in RTOS is energy efficient  More energy is consumed when cache flushing effect during context-switch is considered  I/O: CPU sends data bursts > output buffer -> Considerable energy consumption when buffer is full or when it polls a synchronization variable (similar to [74])

16. Distributed/Multiprocessor real- time systems Reminder  Tightly coupled multiprocessor system Multiple processors are connected at the bus level and share main memory Extreme: multicore with multiple processors on a chip  Loosely coupled multiprocessor system (e.g., cluster) Multiple, stand-alone processors connected via, e.g., Gigabit Ethernet

17. Distributed/Multiprocessor real- time systems Classic RT scheduling ≠ Power-aware one Example: a tightly coupled RT system with 2 processors, 2 memory banks, 1 ready queue  Apply EDF Assign a task to the first available processor  Put memory bank(s) into sleep mode if not used

18. Distributed/Multiprocessor real- time systems Task set Assume memory utilization is linearly dependent on exec time

19. Distributed/Multiprocessor real- time systems Classic Load Balancing (LB)  Try to balance memory bank utilization  Assign T 1 & T 2 to bank 1 (bank utilization 70%) & T 3 & T 4 to bank 2 (BU 54.1%) Power-Aware (PA)  Assign harmonically related tasks to the same bank  Tasks are simultaneously active more often  Assign T 1 & T 3 (BU 36.6%) to bank 1 & T 2 & T 4 to bank 2 (BU 87.5%)

20. Distributed/Multiprocessor real- time systems PA vs. LB Several variations possible: Another project idea!

21. Soft real-time systems [77] Hand-held, pocket computes – audio, video, GPS,... Power profile of a pocket computer shows more dynamic power profile than a laptop Using a single JVM is 25% more energy efficient than using one JVM per application Just-in-time compilation is power efficient

22. Soft real-time systems: Battery lifetime Energy-aware QoS tradeoff [79]  Energy-aware scheduling favoring low energy & critical tasks  Tunable toward extended battery life at the cost of perf  Battery life can be extended up to 100% with perf degradation of 40% Open & closed-loop approaches [80] Tight cooperation btwn OS and applications for energy savings [81]

23. Soft real-time systems: Web server Web workloads are bursty  1998 Nagano Winter Olympic  1840 hits/sec during peak time  Only 459 hits/sec in average Voltage scheduling & frequency scheduling during the predicted low activity time -> 23% - 36% energy savings Heat is also a critical factor in this kind of systems

24. Network Level Much less work has been done compared to RT scheduling & RTOS  QoS support for audio & video streaming, e.g., RSVP (Resource Reseravation Protocol), RTP, RTSP, is a different story  ATM: Constant Bit Ratio & Variable Bit Ratio Research issues  Limited hard RT support, e.g., CAN (Control Area Network)  Overlay networks?  Wireless networks?

25. Network Level Wireless communications: Relatively new technologies  WiFi (IEEE 802.11)  Energy-efficient MAC (Medium Access Control)  Example: S-MAC for Wireless Sensor Networks In WSNs, duty cycle ≤ 1%; 802.11 too expensive Efficient sleep scheduling: If a node loses contention for the medium, it goes to sleep for the duration specified in each packet RTS/CTS for an entire message that can be multiple packets  Fairness in WSNs is not as important as other wireless ad hoc networks S-MAC consumes 2-6 times less energy than 802.11 [96]

26. Above the MAC layer LEACH (Low-Energy Adaptive Clustering Hierarchy)  Cluster head/cluster can aggregate data  Randomly elect a cluster head RAP & SPEED  Real-time protocols in WSNs  To be discussed later in this class

27. Shortest path vs. max power reduction Balance timing & power constraints!

28. Questions?