An Empirical Evaluation of Multi-Resource Scheduling for Moldable Workflows

Resource scheduling plays a vital role in High-Performance Computing (HPC) systems. However, most scheduling research in HPC has focused on only a single type of resource (e.g., computing cores or I/O resources). With the advancement in hardware architectures and the increase in data-intensive HPC applications, there is a need to simultaneously embrace a diverse set of resources (e.g., computing cores, cache, memory, I/O, and network resources) in the design of runtime schedulers for improving the overall application performance. This thesis performs an empirical evaluation of a recently proposed multi-resource scheduling algorithm for minimizing the overall completion time (or makespan) of computational workflows comprised of moldable parallel jobs. Moldable parallel jobs allow the scheduler to select the resource allocations at launch time and thus can adapt to the available system resources (as compared to rigid jobs) while staying easy to design and implement (as compared to malleable jobs). The algorithm was proven to have a worst-case approximation ratio that grows linearly with the number of resource types for moldable workflows. In this thesis, a comprehensive set of simulations is conducted to empirically evaluate the performance of the algorithm using synthetic workflows generated by DAGGEN and moldable jobs that exhibit different speedup profiles. The results show that the algorithm fares better than the theoretical bound predicts, and it consistently outperforms two baseline heuristics under a variety of parameter settings, illustrating its robust practical performance.