Energy-aware intelligent scheduling for deadline-constrained workflows in sustainable cloud computing

It is challenging to handle the non-linear power consumption model, complex workflow structures, and diverse user-defined deadlines for energy-efficient workflow scheduling in sustainable cloud computing. Although metaheuristics are very attractive to solve this problem, most of the existing work regards the problem as a black-box and ignores the use of domain knowledge. To make up for their shortcomings, this paper tailors an energy-aware intelligent scheduling algorithm (EIS) with three new mechanisms. First, we derive the optimal execution time that minimizes energy consumption for each task on a given resource. Second, based on the optimal execution time of each workflow task, the EIS distributes the workflow slack time (difference between its completion time and deadline) to reduce the voltages and frequencies of task executions for energy saving. Third, the EIS mines the idle time gaps caused by task precedence constraints to further reduce dynamic energy consumption whilst satisfying workflows' deadline constraints. To measure the performance of the EIS, we conduct extensive comparison experi-ments based on actual workflow applications. The results demonstrate that the energy consumption of the EIS is much lower than that of the competitors under different deadlines, and has a faster descend rate with the evolution process.(c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Computers and Artificial Intel-ligence, Cairo University. This is an open access article under the CC BY-NC-ND license (http://creative-commons.org/licenses/by-nc-nd/4.0/).