Resource management for running HPC applications in container clouds

Abstract

Innovations in operating-system-level virtualization technologies such as resource control groups, isolated namespaces, and layered file systems have driven a new breed of virtualization solutions called containers. Applications running in containers depend on the host operating system (OS) for resource allocation, throttling, and prioritization. However, the OS is designed to provide only best-effort/fair-share resource allocation. Lack of resource management, as in virtual machine managers, constrains the use of containers and container-based clusters to a subset of workloads other than traditional high-performance computing (HPC) workflows. In this paper, we describe problems with the fair-share resource management of CPUs, network bandwidth, and I/O bandwidth on HPC workloads and present mechanisms to allocate, throttle, and prioritize each of these three critical resources in containerized HPC environments. These mechanisms enable container-based HPC clusters to host applications with different resource requirements and enforce effective resource use so that a large collection of HPC applications can benefit from the flexibility, portability, and agile characteristics of containers.