Abstract
Nowadays, there is an increasing number of workloads, i.e. data serving, analytics, AI, HPC workloads, etc., executed on the Cloud. Although multi-tenancy has gained a lot of attention to optimize resource efficiency, current state-of-the-art resource orchestrators rely on typical metrics, such as CPU or memory utilization, for placing incoming workloads on the available pool of resources, thus, neglecting the interference effects from workload co-location. In this paper, we design an interference-aware cloud orchestrator, based on micro-architectural event monitoring. We integrate our solution with Kubernetes, one of the most widely used and commercially adopted cloud orchestration frameworks nowadays, and we show that we achieve higher performance, up to 32% compared to its default scheduler, for a variety of cloud representative workloads.
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