A static power model for architects
Proceedings of the 33rd annual ACM/IEEE international symposium on Microarchitecture
Energy efficient fixed-priority scheduling for real-time systems on variable voltage processors
Proceedings of the 38th annual Design Automation Conference
Real-time dynamic voltage scaling for low-power embedded operating systems
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Maximizing the System Value while Satisfying Time and Energy Constraints
RTSS '02 Proceedings of the 23rd IEEE Real-Time Systems Symposium
On energy-optimal voltage scheduling for fixed-priority hard real-time systems
ACM Transactions on Embedded Computing Systems (TECS)
Maximizing rewards for real-time applications with energy constraints
ACM Transactions on Embedded Computing Systems (TECS)
Adaptive scheduling server for power-aware real-time tasks
ACM Transactions on Embedded Computing Systems (TECS)
Leakage aware dynamic voltage scaling for real-time embedded systems
Proceedings of the 41st annual Design Automation Conference
Dual ceiling protocol for real-time synchronization under preemption threshold scheduling
Journal of Computer and System Sciences
| Hi-index | 14.98 |
Slowdown factors determine the extent of slowdown a computing system can experience based on functional and performance requirements. Dynamic Voltage Scaling (DVS) of a processor based on slowdown factors can lead to considerable energy savings. We address the problem of computing slowdown factors for dynamically scheduled tasks with specified deadlines. We present an algorithm to compute a near optimal constant slowdown factor based on the bisection method. As a further generalization, for the case of tasks with varying power characteristics, we present the computation of near optimal slowdown factors as a solution to convex optimization problem using the ellipsoid method. The algorithms are practically fast and have the same time complexity as the algorithms to compute the feasibility of a task set. Our simulation results show an average 20 percent energy gain over known slowdown techniques using static slowdown factors and 40 percent gain with dynamic slowdown.