HyperPlane: A Scalable Low-Latency Notification Accelerator for Software Data Planes

摘要

I/O software stacks have evolved rapidly due to the growing speed of I/O devices—including network adapters, storage devices, and accelerators—and the emergence of microservice-based programming models. Datacenters rely on fast, efficient Software Data Planes (SDPs), which orchestrate data transfer between applications and I/O devices. Modern data planes are user-level software stacks, wherein cores spin-poll a large number of queues to avoid the attendant overheads of kernel-based I/O. Cores often poll empty queues before finding work in non-empty ones. Interrogating empty queues hurts peak throughput, tail latency, and energy efficiency as it often entails fruitless cache misses. In this work, we propose HyperPlane, an efficient accelerator for the notification mechanism of SDPs. The key features of HyperPlane are (1) avoiding iteration over empty I/O queues, unlike software-only designs, resulting in queue scalability, (2) halting execution when I/O queues are idle, leading to work proportionality and energy efficiency, and (3) efficiently sharing queues across cores to enjoy strong theoretical properties of scale-up queuing. HyperPlane is realized through a hardware subsystem associated with a familiar programming model. HyperPlane’s microarchitecture consists of a monitoring set that watches for work arrival from I/O, and a ready set, which tracks ready queues and distributes work to cores based on various service policies and priority levels. We show that HyperPlane improves peak throughput by 4.1× and tail latency by 16.4× compared to a state-of-the-art SDP.