Performance tuning has never been more critical in modern datacenters, where rapid advances in networking and I/O speeds expose potential bottlenecks in CPUs, memory, and operating systems. Yet, tuning remains notoriously complex, brittle, and opaque, especially at the microsecond-scale latencies demanded by modern dataplanes. We argue that there is a pressing need to unleash the power of machine learning in datacenter operating systems. We propose ML-native Dataplane Operating Systems, a radical design for ML-based performance tuning. Our design embraces real-time, microsecond-scale ML-based adaptation as a foundational principle, treating ML not as a byproduct but as the backbone, to continuously optimize dataplane performance under dynamic workloads and operating conditions. Our preliminary experiments show that a traditional static dataplane OS incurs severe queuing delays (over milliseconds) under dynamic and parallel workloads, whereas our adaptive approach sustains microsecond-scale tail latencies by dynamically tuning OS parameters in response to runtime signals.
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