Energy Aware Real-Time Systems

1. Energy Aware Real-Time Systems G. Sudha Anil Kumar Real Time Computing and Networking Laboratory Department of Electrical and Computer Engineering Iowa State University CprE 545 class presentation

2. Real-Time System: Characteristics • Real-Time Guarantees • Meeting deadlines • Fault Tolerance • Tolerating faults • Quality of Service • Acceptable quality of service • Energy Consumption • Minimize overall energy consumption anil@iastate.edu

3. Real Time system Energy Quality Fault tolerance anil@iastate.edu

4. Fault Tolerance vs. Quality • Imprecise Computation technique • Trading off quality for fault tolerance • (m, k)-firm deadline task model • Trading off quality for scheduling flexibility anil@iastate.edu

5. Imprecise Computation (IC) Normal Task Ci Mandatory Optional Mi Oi Imprecise Computation task anil@iastate.edu

6. IC: Relevant Applications • Image Processing: Fuzzy image in time are better than too late perfect image • Tracking: Rough estimate of target location in time is better than too late accurate location data. anil@iastate.edu

7. (m, k)-firm deadline tasks Task (T): C = 1; P = 2; 0 2 4 6 8 10 Time M = 2; K = 3; 0 2 4 6 8 10 Time anil@iastate.edu

8. (m, k): Relevant Applications • Radar tracking: A few well spaced deadlines can be tolerated • Automobile control, multi-media streaming, etc.. anil@iastate.edu

9. Real Time system Energy Quality Fault tolerance anil@iastate.edu

10. Energy vs. Quality • Conflicting Design Objectives • Energy savings • Quality of Service anil@iastate.edu

11. Organization of the presentation • Energy issues in RT-Embedded systems • Dynamic Voltage Scaling (DVS) • RT-DVS schemes • Energy aware RT-DVS for IC and (m, k) tasks anil@iastate.edu

12. Energy consumption in RT-ES • Energy consumption is an important issue in RT-embedded systems like: • Laptops, PDAs. • Digital camcorders, cellular phones • portable medical devices. anil@iastate.edu

13. Important Facts (1) • The peak computing rate needed is much higher than the average throughput that must be sustained • High performance is needed only for a small fraction of time, while for the rest of time, a low-performance, a low-power processor would suffice anil@iastate.edu

14. Workload Profile Work load Peak Computing Rate is needed Average rate would suffice Time anil@iastate.edu

15. Important Facts (2) CMOS based processors Varying voltage and frequency we can reduce the energy consumption Power (P) αV2 .f V αf Energy (Ei) αcci .f2 anil@iastate.edu

16. Variable Voltage Processors • Modern processors operate at multiple frequency (and voltage) levels. • Crusoe Processor: Transmeta Corporation • PowerNow! Technology: AMD • Intel XScale: Intel • Higher the frequency level higher the energy consumption anil@iastate.edu

17. Dynamic Voltage Scaling (DVS) • DVS scales the operating voltage of the processor along with the frequency. • Since the energy consumption is proportional to V2 , DVS can potentially provide a very large energy savings. anil@iastate.edu

18. DVS-example • Consider a task with a computation time 20 units. • Energy of Ti without DVS • E1 = K * 20 * F2. • Energy of Ti with DVS • E2 = K * 20 * (F/2)2. • Clearly, E2 = (E1)/4. anil@iastate.edu

19. Energy-Time Tradeoffs 60 40 Energy Savings 20 10 Time anil@iastate.edu

20. Energy aware RT-scheduling: objectives • Minimizing energy consumption • Maximize the quality • Meeting the deadlines anil@iastate.edu

21. Energy aware RTS Techniques • OS Level Energy Management • Inter-task DVS • Compiler Level Energy Management • Intra-task DVS anil@iastate.edu

22. Intra vs. Inter-task DVS • Inter-task DVS scheme: Voltage scheduling is done on a task by task basis. T3 T1 T2 • Intra-task DVS scheme: Voltage scheduling is done within a task boundary. • Each task is modeled as a control flow graph. Voltage scheduling points T3 T1… …T1 T2… …T2 anil@iastate.edu

23. Quality Oi Energy Aware RT-Scheduling IC tasks System Model • OS level DVS • Inter-task DVS Each periodic task is specified by : Ci, Pi, Mi, Oi Energy budget per hyper-period: Eb Mi anil@iastate.edu

24. Energy aware RT-Scheduling of IC tasks [8] • Goal: • To schedule a set of Imprecise Computation tasks • Objective: • maximize the quality • Constraints: • without exceeding the deadlines • Without exceeding the total energy available anil@iastate.edu

25. Optimal Solution [8] Find the Minimum energy frequencies settings of each task Find the Maximum quality solution With the above frequency settings anil@iastate.edu

26. Minimum energy frequency settings • Theorem: All tasks will execute at the same frequency in the minimum-energy solution • Due to the concave nature of the energy function • The above theorem is proved using rigorous mathematical tools. • The intuition follows…… anil@iastate.edu

27. Energy function characteristics anil@iastate.edu

28. Example • Consider two tasks: • T1 = (3, 12) and T2 = (3,12) +ΔE2 Energy T2 @ f = 0.7 -ΔE1 f = 0.5 T1 @ f = 0.4 Time 0 7.5 12 ΔE1 < ΔE2 anil@iastate.edu

29. Example: Minimum energy frequency settings • Consider two tasks: • T1 = (3, 12) and T2 = (3,12) Energy f = 0.5 T1 @ f = 0.5 T2 @ f = 0.5 6.0 0 12 Time anil@iastate.edu

30. Calculating the minimum energy frequency • Given: energy budget, Eb per LCM • We know: power, P = k * f3 • Solve for fop: k * fop3 = Eb / LCM anil@iastate.edu

31. Reduced Problem [8] • Goal: • To schedule a set of Imprecise Computation tasks • Objective: • maximize the quality • Constraints: • without exceeding the deadline • Without exceeding the total energy available anil@iastate.edu

32. Reducing the problem Ti = (Ci, Pi, Mi, Oi) Ti = (Ci/fop, Pi, Mi/fop, Oi/fop) Ti = (C’i, Pi, M’i, O’i) anil@iastate.edu

33. Optimal Solution to the reduced problem • Theorem: There exists an optimal solution to the reduced problem where the optional parts of a task Ti receive the same service time at every instance • The above theorem is proved using rigorous mathematical tools. • The intuition follows…. anil@iastate.edu

34. Optimal solution with equal optional service times M11 O11 M21 O21 Both satisfy constraints Oi1 = (O11 + O21)/2 M11 O11 M21 O21 anil@iastate.edu

35. Algorithm for linear quality functions • Step1: Sort all the tasks in the order of (ki/bi), where bi is the number of instances of Ti in LCM. • Step 2: Allocate maximum possible slack to the task with largest (ki/bi) Mi Quality Qi = ki * ti Oi anil@iastate.edu

36. The entire procedure Find the optimal frequency which isthe same forall tasks Find the Maximum quality solution by determining the optional service times anil@iastate.edu

37. The (m,k) firm guarantee Task Model • Energy aware (m,k) Problem: • (m, k)-firm deadlines • Minimize energy consumption anil@iastate.edu

38. Static algorithm by Gang et al. [1] • Assumptions: • Each task is specified by: (Pi,Di,Ci,mi,ki). • Processor provides two voltage/frequency modes (high and low). anil@iastate.edu

39. Static algorithm (contd..) • Algorithm: • Sort all the tasks as per their utilizations. • Test the task set for the schedulability at the High Frequency Mode. • Considers the next highest utilization task, and checks (with respect to schedulability) if it can be slowed down. anil@iastate.edu

40. Static Algorithm: drawbacks • Algorithms’ run time increases exponentially with the number of voltage levels. • Does not capture the energy-value tradeoffs. anil@iastate.edu

41. Conclusions • Energy-Quality-Time tradeoff is an important issue in Embedded RTS. • There is a lot of scope to work in this area (e.g. better energy aware (m, k)-firm deadline task scheduling) anil@iastate.edu

42. References • [1] Quan, G., L. Niu and J. P. Davis, "Power Aware Scheduling for Real-Time Systems with (m,k)-Guarantee", Proceedings CNDS-04: Communication Networks and Distributed Systems Modeling and Simulation, The Society for Modeling and Simulation International, 2004. • [2] http://www.transmeta.com/crusoe/faq.html#8 • [3] Real-Time Dynamic voltage scaling for Low-Power Embedded Operating Systems, P. Pillai and K. G. Shin, in ACM SOSP, pages 89-201, 2001. anil@iastate.edu

43. References • [4] Intra-task Voltage Scheduling on DVS-Enabled Hard Real-Time Systems, D. Shin and J. kim, IEEE Design and Test of Computers, March 2001. • [5] Maximizing the System Value while Satisfying Time and Energy Constraints, Cosmin Rusu, Rami Melhem, Daniel Mossé; ,IBM Journal of R&D, vol 47, no 5/6, 2003 • [6] Hard Real-Time scheduling for Low-energy using stochastic data and DVS Processors, Flavius Gruian, symposium on low power electronics and design, 2001. anil@iastate.edu

44. References • [7] Scheduling with Dynamic Voltage/Speed Adjustment Using Slack Reclamation in Multi-Processor Real-Time Systems, D. Zhu, R. Melhem, and B. Childers, IEEE Trans. on Parallel & Distributed Systems, vol. 14, no. 7, pp. 686 - 700, 2003. • [8] C. Rusu, R. Melhem and D. Mossé, "Maximizing Rewards for Real-Time Applications with Energy Constraints", Accepted for publication in ACM Transactions on Embedded Computer Systems. • [9] R. Mishra, N. Rastogi, D. Zhu, D. Mosse, R. Melhem, "Energy Aware Scheduling for Distributed Real-Time Systems", Proc. of the International Parallel and Distributed Processing Symposium (IPDPS'03), Nice, France (April 2003). anil@iastate.edu

45. Thank You!! anil@iastate.edu