Assessing the Use of Genetic Algorithms to Schedule Independent Tasks Under Power Constraints

Green data and computing centers, centers using renewable energy sources, can be a valid solution to the over growing energy consumption of data or computing centers and their corresponding carbon foot print. Powering these centers with energy solely provided by renewable energy sources is however a challenge because renewable sources (like solar panels and wind turbines) cannot guarantee a continuous feeding due to their intermittent energy production. The high computation demand of HPC applications requires high power levels to be provided from the power supply. On the other hand, one advantage is that unlike online applications, HPC applications can tolerate delaying the execution of some tasks. Since the users however want their results as early as possible, minimum makespan is usually the main objective when scheduling this kind of jobs. The optimization problem of scheduling a set of tasks under power constraints is however proven to be NPComplete. Designing and assessing heuristics is hence the only way to propose efficient solutions. In this paper, we present genetic algorithms for scheduling sets of independent tasks in parallel, with the objective of minimizing the makespan under power availability constraints. Extensive simulations show that genetic algorithms can compute good schedules for this problem.