This paper presents an energy-aware method to schedule multiple real-time tasks in multiprocessor systems that support dynamic voltage scaling (DVS). The key difference from existing approaches is that we consider the probabilistic distributions of the tasks' execution time to partition the workload for better energy reduction. We analyze the problem of energy-aware scheduling for multiprocessor with probabilistic workload information and derive its mathematical formulation. As the problem is NP-hard, we present a polynomial-time heuristic method to transform the problem into a probability-based load balancing problem that is then solved with worst-fit decreasing bin-packing heuristic. Simulation results with synthetic, multimedia, and stereovision tasks show that our method saves significantly more energy than existing methods.
In REF , the authors studied energy-aware real-time tasks scheduling with uncertain execution time.
Energy-aware scheduling for real-time multiprocessor systems with uncertain task execution time
DAC '07
Computer Science