Scalable Hierarchical Scheduling for Multiprocessor Systems Using Adaptive Feedback-Driven Policies

This work addresses the problem of allocating resource-intensive parallel jobs on multicore- and multiprocessor-based systems, where the performance gains largely depend on effectively exploiting application parallelization across the available parallel computing resources. The objective is to find efficient allocation approaches that minimize the parallel jobs' completion time, i.e. makespan. Integrating feedback-driven adaptive strategies, we present a general hierarchical scheduling framework and show that two hierarchical scheduling algorithms: ABG-DS and AG-DS achieve scalable performance in term of makespan regardless of the number of hierarchical levels. Specifically, we prove that both ABG-DS and AG-DS have O(1)-competitive ratio for batched parallel jobs. Extending an existing tool, called Malleable-Lab, we evaluate the performance and scalability of our proposed algorithms and compare with that of well-known EQUI-based strategies. The simulation results demonstrate that both ABG-DS and AG-DS generally outperforms EQUI-EQUI for a wide range of parallel workloads. Moreover, feedback-driven adaptive scheduling algorithms show better scalability when the number of levels increases in the scheduling hierarchy.