Stochastic DVS-based dynamic power management for soft real-time systems
This paper introduces a stochastic dynamic power management policy for soft real-time systems. Such a system comprises a single processor with the capability of dynamic voltage scaling (DVS). The policy uses DVS to consume less power in the processor while satisfying some performance constraints. The idea is based on a Markovian model of the system, which presents an analytical technique for tuning the system parameters and evaluating the effectiveness of the policy. Real-time jobs arrive according to a Poisson process and have exponentially distributed service times and relative deadlines. The power management policy is designed to reduce the long-run power consumption of the system while guaranteeing an upper bound on the fraction of jobs missing their deadlines. It is also shown that the method can be applied to non-Markovian systems using a hybrid analytical-simulation method. Moreover, the method can improve the tradeoff between power and performance when jitters are introduced in the traffic intensity or on the average service time. To satisfy the desired performance constraints of the system, a number of population-dependent speeds are required. Such speeds are extracted from the model of an ideal system with continuous speeds and no speed switching overhead. Since the speed levels in real processors are not continuous, each required speed is approximated in a probabilistic manner by switching between its two immediate neighbors. The time and energy overheads of the switchings are also taken into account. The analytical and simulation results illustrate that the long-run power consumption of the resulting system is close to that of the ideal system, while the performance constraints are completely satisfied.
dynamic power management soft real-time systems dynamic voltage scaling (dvs) stochastic power management real-time scheduling