Usage Trends Aware VM Placement in Academic Research Computing Clouds

Academic Research Computing Clouds are widely deployed worldwide by research institutions to support researcher's computations. While the nature of the hosted use-cases is diverse, literature and institutions' published guides point to highly parallel HPC workloads and state-heavy workloads as two popular use-cases in research-computing clouds. Additionally, our prior investigation has uncovered unique patterns in the users' VM-provisioning behaviors in four of Canada's research-computing clouds. These patterns, i.e., usage-trends, were generated by examining nearly 1 million VMs created by researchers over four and half years. The usage trends mimicked behaviors of provisioning highly parallel HPC workloads and state-heavy portals. In this paper, we exploit the knowledge of these usage trends to guide VM placement decisions in research-computing IaaS clouds. We propose a delayed-provisioning algorithm that postpones resource allocations for incoming VM requests in anticipation of running VMs termination, minimizing the number of active PMs and PM underutilization. We examine the performance of the proposed algorithm compared to other placement algorithms using a real-life validation dataset of nearly 850 thousand VMs. The results show significant improvements ranging from 7% to 33% reduction in the number of active PMs and up to 61% reduction in the number of hourly underutilized PMs.