Hybrid Resource Management for HPC and Data Intensive Workloads

Abstract

High Performance Computing (HPC) and Data Intensive (DI) workloads have been executed on separate clusters using different tools for resource and application management. With increasing convergence, where modern applications are composed of both types of jobs in complex workflows, this separation becomes a growing overhead and the need for a common platform increases. Executing both workload classes on the same clusters not only enables hybrid workflows, but can also increase system efficiency, as available hardware often is not fully utilized by applications. While HPC systems are typically managed in a coarse grained fashion, with exclusive resource allocations, DI systems employ a finer grained regime, enabling dynamic allocation and control based on application needs. On the path to full convergence, a useful and less intrusive step is a hybrid resource management system allowing the execution of DI applications on top of standard HPC scheduling systems. In this paper we present the architecture of a hybrid system enabling dual-level scheduling for DI jobs in HPC infrastructures. Our system takes advantage of real-time resource profiling to efficiently co-schedule HPC and DI applications. The architecture is easily extensible to current and new types of distributed applications, allowing efficient combination of hybrid workloads on HPC resources with increased job throughput and higher overall resource utilization. The implementation is based on the Slurm and Mesos resource managers for HPC and DI jobs. Experimental evaluations in a real cluster based on a set of representative HPC and DI applications demonstrate that our hybrid architecture improves resource utilization by 20%, with 12% decrease on queue makespan while still meeting all deadlines for HPC jobs.