Energy efficient multiprocessor task scheduling under input-dependent variation

In this paper, we propose a novel, energy aware scheduling algorithm for applications running on DVS-enabled multiprocessor systems, which exploits variation in execution times of individual tasks. In particular, our algorithm takes into account latency and resource constraints, precedence constraints among tasks and input-dependent variation in execution times of tasks to produce a scheduling solution and voltage assignment such that the average energy consumption is minimized. Our algorithm is based on a mathematical programming formulation of the scheduling and voltage assignment problem and runs in polynomial time. Experiments with randomly generated task graphs show that up to 30% savings in energy can be obtained by using our algorithm over existing techniques. We perform experiments on two real-world applications - MPEG-4 decoder and MJPEG encoder. Simulations show that the scheduling solution generated by our algorithm can provide up to 25% reduction in energy consumption over greedy dynamic slack reclamation algorithms.