Approximation Algorithms for Multiprocessor Energy-Efficient Scheduling of Periodic Real-Time Tasks with Uncertain Task Execution Time

摘要

Energy-efficiency has been an important system issue in hardware and software designs for both real-time embedded systems and server systems. This research explores systems with probabilistic distribution on the execution time of real-time tasks on homogeneous multiprocessor platforms with the capability of dynamic voltage scaling (DVS). The objective is to derive a task partition which minimizes the expected energy consumption for completing all the given tasks in time. We give an efficient 1.13-approximation algorithm and a polynomial-time approximation scheme (PTAS) to provide worst-case guarantees for the strongly NP-hard problem. Experimental results show that the algorithms can effectively minimize the expected energy consumption.