Power-Aware real-time Scheduling: Models, Open Problems, & Practical considerations

Power-Aware real-time Scheduling: Models, Open Problems, & Practical considerations Department of Computer Science Wayne State University Nathan Fisher

Motivation: Temperature & Energy Management • Platform-Design-Time Techniques: • Packaging: Fans, heat sinks, low-speed CPUs, etc.

Some (Extreme) Examples of Cooling Packaging

Motivation: Temperature & Energy Management • Platform-Design-Time Techniques: • Packaging: Fans, heat sinks, low-speed CPUs, etc. • Advantage: can be very effective. • Disadvantages: • Inflexible. • Expensive: $1 - $3/watt dissipated [Skandron et al., 2003]. • Dynamic Power Management (DPM) Techniques: • Dynamic Voltage Scaling (DVS) processors. • Advantages: flexible & inexpensive. • Disadvantage: modeling energy & thermal behavior is non-trivial.

Overview • Models: Workload/Scheduling, Power, & Cooling. • Open Problems: • Peak Temperature Minimization & • Energy Minimization (with Temperature Constraints). • Practical Considerations. • Beyond Uniprocessor Platforms: DPM in Multicore Platforms.

Models: Workload/Scheduling • Each component is a sporadic task system, τ= {τ1, τ2 …, τn} • Legal arrival sequence for τ1=(2,3,5): Sporadic Tasks • Characterized by the tupleτi=(Ci, Ti, Di) • Worst case execution requirement, Ci • Period, Ti • Relative deadline, Di (2) (2) (2) (2) (2) (2) 0 5 10 15 20 25 t

Models: Workload/Scheduling • Common Real-time Scheduling Algorithms: • Earliest-Deadline-First (EDF). • Fixed Priority (e.g., deadline-monotonic) • Initial Assumption: Uniprocessor Platform -Schedulable A task system τ is -schedulable if it always meets deadlines when scheduled according to scheduling algorithm .

Models: CPU-Power Consumption • Notation for power from Wang et al. [RTCSA, 2009]. • Power Function: • Speed-Dependent Power: dep ind Time CPU-Speed Function Effective Switch Capacitance Supplied Voltage and are positive constants with .

Models: CPU-Power Consumption • Notation for power from Wang et al. [RTCSA, 2009]. • Power Function: • Speed-Dependent Power: • Speed-Independent Power (Leakage): dep ind Absolute CPU-Temp Function ind CPU-Specific Constants

Models: CPU-Power Consumption • Notation for power from Wang et al. [RTCSA, 2009]. • Power Function: Energy Consumption over [t1, t2] dep ind 1

Models: CPU Cooling • Modeled by Fourier’s First-Order Approximation: • Positive Constants dependent upon: • Heating & Cooling Coefficients • Fixed Ambient Temperature

Models: CPU Cooling • Modeled by Fourier’s First-Order Approximation: • Easier to work with adjusted temperature: Temperature at giveninitial temperature

Open Problem 1 Peak-Temperature Minimization • Given: • Sporadic Task System , • Scheduling Algorithm . • Find: Determine that minimizes peak CPU temperature such that is -schedulable.

Open Problem 2 Energy Minimization with Temperature Constraints • Given: • Sporadic Task System , • Scheduling Algorithm , • Temperature Threshold Tthresh. • Find: Determine that minimizes CPU energy consumption such that is -schedulable and the CPU temp never exceeds Tthresh.

Practical Considerations Problem: Some published techniques claiming to be “energy-efficient” actually increase energy-consumption over non-power-aware approaches! [Schmidt & Wehn, White paper, 2009]

Practical Considerations: Constraints on Speed Function • Speed function must be efficiently computable: • Online, or • Fit in table in main memory. • Piecewise, periodic function. • Lower bound on interval between speed changes: • Hardware limitations • Clock granularity.

Practical Considerations: Constraints on Achievable Speeds • Some prior results assume a continuous range of speed . • In practice • Bounded Range: • Finite Set: min max 1

Practical Considerations: Nonlinear Program Runtimes • Job execution times do not necessarily decrease linearly (or even continuously) with increased CPU frequency. • Example • Two CPU Speeds: and . • Two Jobs: • JA requires units of time at speed . • JB requires units of time at speed . • At speed , JA executes for while JB for . • Reason: I/O or Cache Misses. 1 2 1 1 2 1

Practical Considerations: Speed-Transition Overhead • CPU may be unavailable for job execution when transitioning between speeds: • units of overhead for going from to .

Practical Considerations: Non-CPU Power Consumption • Non-CPU resources have DPM capabilities – e.g., memory controllers. • Careful consideration is required: • E.g., low-power state may require memory refresh  energy-expensive operation.

Open Problems Previous Results: Obtained for simpler task models only (e.g., frame-based tasks).

Beyond Uniprocessors: Multicore • Thermal Challenges: • Heat transfer between elements (e.g., Core-to-Sink). Core-to-Sink Core 3 Core 4 Sink 1 Sink 2 Core 1 Core 2

Beyond Uniprocessors: Multicore • Thermal Challenges: • Heat transfer between elements (e.g., Core-to-Sink). Sink-to-Core Core 3 Core 4 Sink 1 Sink 2 Core 1 Core 2

Beyond Uniprocessors: Multicore • Thermal Challenges: • Heat transfer between elements (e.g., Core-to-Sink). Sink-to-Sink Core 3 Core 4 Sink 1 Sink 2 Core 1 Core 2

Beyond Uniprocessors: Multicore • Thermal Challenges: • Heat transfer between elements (e.g., Core-to-Sink). Core-to-Core Core 3 Core 4 Sink 1 Sink 2 Core 1 Core 2

Beyond Uniprocessors: Multicore • Thermal Challenges: • Heat transfer between elements (e.g., Core-to-Sink). Sink-to-Environment Core 3 Core 4 Sink 1 Sink 2 Core 1 Core 2 Previous Results: Static-speed determination to minimize temperature in the steady state.

Conclusions • Energy & Thermal considerations are increasingly prevalent/important in real-time system design. • DPM provides flexible & inexpensive solution. • (Theoretic) Power Model: • Thermal/Energy. • Interdependent & Leakage-Aware. • Open Questions for Sporadic Task Model. • Effectiveness of solution constrained by practical considerations. • Multicore offers challenging “twist”.

Thank You Questions, Comments, Corrections? fishern@cs.wayne.edu

Power-Aware real-time Scheduling: Models, Open Problems, & Practical considerations