Battery Aware Dynamic Scheduling for Periodic Task Graphs
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1. The document discusses battery-aware dynamic scheduling for periodic task graphs to maximize battery lifetime in embedded systems. 2. It proposes a methodology that combines a good dynamic voltage scaling algorithm with a priority function to schedule task graphs in a battery-efficient manner. 3. Simulations show the methodology performs up to 47% better than existing approaches in terms of battery lifetime.
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Battery Aware Dynamic Scheduling for Periodic Task Graphs
- 1. Battery Aware Dynamic Scheduling for Periodic Task Graphs Venkat Rao # , Nicolas Navet # , Gaurav Singhal *, Anshul Kumar , GS Visweswaran # TRIO Group, INRIA-Lorraine /LORIA. * Dept of ECE, UT Austin, Dept of CSE, IIT Delhi Dept of EE, IIT Delhi
- 9. Problem Definition To find a battery efficient schedule for a given a set of periodic tasks graphs (T1, T2, ....Tn) which have corresponding deadlines (D1,D2, .....Dn) equal to their periods, where a taskgraph Ti comprises of any m interdependent nodes, each of which are in themselves tasks with given worst case computations (wci1, wci2, ......wcim). T1 D1 T3D3 T2 D2 wci Precendence constraint
- 24. Thank You
- 25. Battery Models Still Too computationally intensive for use at runtime Not accurate, elements change value depending conditions Use capacitor and resistors to represent battery Circuit Still in the process of development. Relatively accurate and fast. Stochastic Slow, involves a large number of parameters Accurate PDE (higher forms of KiBaM) Disadvantages Advantages
- 28. Simulation Results: Effect of ready list on energy consumption Energy consumption (normalized w.r.t optimal schedule) by various scheduling policies for different number of tasks in a taskgraph At Utilization 70% and actual computation times varying from 20% to 70%
- 29. Simulation Results: Effect of priority function on energy consumption Energy consumption (normalized w.r.t optimal schedule) by various scheduling policies for different number of tasks in a taskgraph At Utilization 70% and actual computation times varying from 20% to 70%. Ready list comprises of most imminent.
Notes de l'éditeur
- Based solely on the electro-chemistry. Sometime rely on empirically established 2. PDE: finite element models, divide each cell into a number of finite elements interacting with each other, models current flow and potential distribution in the cell. Quite complex and slow while accurate, not suitable for mobile OS. 3. 4. Stochastic is a promising modeling method. Represent the battery behavior as A discrete time transient stochastic process, that tracks the the cell state of the charge.
- Factors that may affect the battery performance include: When a battery stands idle after a discharge, certain chemical and physical changes take place which can result in voltage recovery. So, the voltage will rise after a rest period, giving a saw-tooth-shaped discharge. Shelf Life: even during storage, the battery is still discharge itself. Depending on the storage temp and humidity, the short shelf life can be a problem on long-term discharges. Around room temp, alkaline lose about 3% capacity per year, however zinc-carbon can lose up to 15% of the capacity
- Factors that may affect the battery performance include: When a battery stands idle after a discharge, certain chemical and physical changes take place which can result in voltage recovery. So, the voltage will rise after a rest period, giving a saw-tooth-shaped discharge. Shelf Life: even during storage, the battery is still discharge itself. Depending on the storage temp and humidity, the short shelf life can be a problem on long-term discharges. Around room temp, alkaline lose about 3% capacity per year, however zinc-carbon can lose up to 15% of the capacity