Lower-bound time-complexity greening mechanism for duplication-based scheduling on large-scale computing platforms

Large-scale computing platforms become essential in nowadays business and scientific activities. The electrical energy consumed by such platforms increases dramatically due to the increase in both the computation power of these platforms and the required cooling energy. Task scheduling is one of the key issues to achieve high performance in large-scale computing platforms. One approach to the compile-time task scheduling, in these platforms, is duplication-based list scheduling heuristics. This approach enhances the performance from the application completion-time point of view, while it decreases the performance from the energy consumption point of view. This paper presents a mechanism that can be applied to any schedule produced by a duplication-based task scheduling algorithm to reduce the consumed energy while keeping the same application completion time. The mechanism is called toward green task duplication (TGTD). TGTD is examined in reducing the energy consumed by four of the most-recent and well-known duplication-based list scheduling algorithms. The experimental results based on a computer simulation utilizing large sets of both randomly generated and two real-world applications graphs show that TGTD significantly enhances the energy consumed by each algorithm.