Mitigating Resource Contention and Heterogeneity in Public Clouds for Scientific Modeling Services

Abstraction of physical hardware using infrastructure-as-a-service (IaaS) clouds leads to the simplistic view that resources are homogeneous and that infinite scaling is possible with linear increases in performance. Hosting scientific modeling services using IaaS clouds requires awareness of application resource requirements and careful management of cloud-based infrastructure. In this paper, we present multiple methods to improve public cloud infrastructure management to support hosting scientific model services. We investigate public cloud VM-host heterogeneity and noisy neighbor detection to inform VM trial-and-better selection to favor worker VMs with better placements in public clouds. We present a cpuSteal noisy neighbor detection method (NN-Detect) which harnesses the cpuSteal CPU metric to identify worker VMs with resource contention from noisy neighbors. We evaluate potential performance improvements provided from leveraging these techniques in support of providing modeling-as-a-service for two environmental science models.