Modern parallel architectures have heterogeneous processors and complex memory hierarchies, offering up to billion-way parallelism at multiple hierarchical levels. Their exploitation of HPC applications greatly boosts scientific discoveries and advances, but it is still hard to utilize them maximally, engendering pro rata high energy consumption. The task-based programming model has demonstrated promising potential in developing scientific applications on modern high-performance platforms. In this work, a new framework for managing the concurrent execution of task-based applications, RSCHED, is introduced. RSCHED aims at minimizing the overall makespan while executing a set of applications, and maximizing resource utilization. We implemented our proposal on StarPU and evaluated it on real applications. RSCHED demonstrated the potential to speed up the overall makespan of ran applications compared to consecutive execution with an average factor of 10x and the potential to increase resource utilization.