Shelf schedules for independent moldable tasks to minimize the energy consumption

Abstract

Scheduling independent tasks on a parallel platform is a widely-studied problem, in particular when the goal is to minimize the total execution time, or makespan (<tex>$P\Vert C_{max}$</tex> problem in Graham&#x0027;s notations). Also, many applications do not consist of sequential tasks, but rather parallel moldable tasks that can decide their degree of parallelism at execution (i.e., on how many processors they are executed). Furthermore, since the energy consumption of data centers is a growing concern, both from an environmental and economical point of view, minimizing the energy consumption of a schedule is a main challenge to be addressed. One can then decide, for each task, on how many processors it is executed, and at which speed the processors are operated, with the goal to minimize the total energy consumption. We further focus on co-schedules, where tasks are partitioned into shelves, and we prove that the problem of minimizing the energy consumption remains NP-complete when static energy is consumed during the whole duration of the application. We are however able to provide an optimal algorithm for the schedule within one shelf, i.e., for a set of tasks that start at the same time. Several approximation results are derived, and simulations are performed to show the performance of the proposed algorithms.