Today’s datacenter workloads increasingly comprise distributed data-intensive applications, including data analytics, graph processing, and machine-learning training. These applications are bandwidth-hungry and often congest the datacenter network, resulting in poor network performance, which hurts application completion time. Efforts made to address this problem generally aim to achieve max-min fairness at the flow or application level.
\nWe observe that splitting the bandwidth equally among workloads is sub-optimal for aggregate application-level performance because various workloads exhibit different sensitivity to network bandwidth: for some workloads, even a small reduction in the available bandwidth yields a significant increase in completion time; for others, the completion time is largely insensitive to the available bandwidth.<\/p>\n
Building on this insight, we propose Saba, an application-aware bandwidth allocation framework that distributes network bandwidth based on application-level sensitivity. Saba combines ahead-of-time application profiling to determine bandwidth sensitivity with runtime bandwidth allocation using lightweight software support with no modifications to network hardware or protocols. Experiments with a 32-server hardware testbed show that Saba improves average completion time by 1.88x (and by 1.27x in a simulated 1,944-server cluster).<\/p>\n","protected":false},"excerpt":{"rendered":"
Today’s datacenter workloads increasingly comprise distributed data-intensive applications, including data analytics, graph processing, and machine-learning training. These applications are bandwidth-hungry and often congest the datacenter network, resulting in poor network performance, which hurts application completion time. Efforts made to address this problem generally aim to achieve max-min fairness at the flow or application level. 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