Live Webcast 15th Annual Charm++ Workshop

Cloud offerings are increasingly serving workloads with a large variability in terms of compute, storage and net- working resources. Computing requirements (all the way to High Performance Computing or HPC), criticality, communication intensity, memory requirements, and scale can vary widely. Virtual Machine (VM) placement and consolidation for effective utilization of a common pool of resources for efficient execution of such diverse class of applications in the cloud is challenging, resulting in higher cost and missed Service Level Agreements (SLAs). For HPC, current cloud providers either offer dedicated cloud with dedicated nodes, losing out on consolidation benefits of virtualization, or use HPC-agnostic cloud scheduling resulting in poor HPC performance. In this work, we address application-aware allocation of n VM instances (comprising a single job request) to physical hosts from a single pool. We design and implement an HPC-aware scheduler on top of OpenStack Compute (Nova) and also incorporate it in a simulator (CloudSim). Through various optimizations, specifi- cally topology- and hardware-awareness, cross-VM interference accounting and application-aware consolidation, we demonstrate enhanced VM placements which achieve up to 45% improvement in HPC performance and/or 32% increase in job throughput while limiting the effect of jitter (or noise) to 8%.