With the advancement of cloud computing, there has been a growing interest in exploiting demand-based cloud resources for parallel scientific applications. To satisfy different needs for computing resources, cloud providers provide many different types of virtual machines (VMs) with various numbers of computing cores and amounts of memory. The cost and execution time of a scientific application vary depending on the types of VMs, number of VMs, and current status of the cloud due to interference among VMs. However, currently, cloud users are solely responsible for selecting the most effective VM configuration for their needs, but often end up with sub-optimal selections. In this paper, using molecular dynamics simulations as a case study, we propose a framework to guide users to select the optimal VM configurations that satisfy their requirements for scientific parallel computing in virtualized clusters. For molecular dynamics computation on a cluster of VMs, the guidance framework uses artificial neural networks which are trained to predict its execution times for various inputs, VM configurations, and status of interference among VMs. Using our performance prediction mechanisms, the guidance framework helps users choose an optimal or near-optimal VM cluster configuration under cost and runtime constraints.

Consolidating multiple applications on a system can improve the overall resource utilization of data center systems. However, such consolidation can adversely affect the performance of some applications due to interference caused by resource contention. Despite many prior studies on the interference effects in single-node systems, the interference behaviors of distributed parallel applications have not been investigated thoroughly. With distributed applications, a local interference in a node can affect the whole execution of an application spanning many nodes. This paper studies an interference modeling methodology for distributed applications to predict their performance under interference effects in consolidated clusters. This study first characterizes the effects of interference for various distributed applications over different interference settings, and analyzes how diverse interference intensities on multiple nodes affect the overall performance. Based on the characterization, this study proposes a static profiling-based model for interference propagation and heterogeneity behaviors. In addition, this paper presents use case studies of the modeling method, two interference-aware placement techniques for consolidated virtual clusters, which attempt to maximize the overall throughput or to guarantee the quality-of-service.