Controlling Energy Profile of RT Multiprocessor Systems by Anticipating Workload at Runtime

Emerging trends in applications with the requirement of considerable computational performance and decreasing timeto-market have urged the need of multiprocessor systems. With the increase in number of processors there is an increased demand to efficiently control the energy and power budget of such embedded systems as well. Power management in embedded computing systems is achieved by actively changing the power consumption profile of the system by putting its components into such power/energy states which are sufficient to meet functionality requirements. Dynamic Power Management (DPM) strategies attempt to make decisions related to the choice of such states based on the available information at runtime. These strategies exploit the inherently present idleness (if any) in the application’s behavior which is a priori unknown or non-stationary. This paper presents a novel DPM strategy called Assertive Dynamic Power Management (AsDPM) for real time applications. It is based on the idle time extraction from application’s behavior and clustering to make appropriate decision for state-transition of processors in a multiprocessor real time system. Experimental results show that conventional DPM approaches often yield suboptimal, if not incorrect, performance in the presence of real time constraints whereas, the AsDPM strategy gives better energy consumption performance under the same constraints by 10.40%. Also, it reduces the number of state transitions by 74.85% and 59.76% for EDF and LLF scheduling policies respectively.