time and energy optimization algorithms for the static scheduling of multiple workflows in heterogeneous computing system

The heterogeneous computing system (hcs), which is used to deal with complex and enormous business or scientific workflows, is playing a very important role as cloud computing rapidly develops. for multiple workflows computing in hcs, one of challenging issues is how to make a reasonable tradeoff between the schedule length and energy consumption. in this paper, we focus on a workflow that can be represented by a directed acyclic graph (dag). we propose the corresponding algorithms which cooperate with dynamic voltage and frequency scaling (dvfs) technique to address the aforementioned concern and evaluate the algorithms in terms of randomly generated dags, real application dags and their hybrids under dvfs-enabled hcs. from the experimental results, we draw the conclusion that interleaving workflows lead to a better average tradeoff when scheduling multiple workflows in hcs.