ScholarWorks@성균관대학교

초록

The evolution of cloud datacenter infrastructure, driven by ever-increasing bandwidth demands, has shifted performance bottlenecks from network hardware to server-side protocol processing. To mitigate per-CPU processing bottlenecks, load-balancing schemes such as Receive Side Scaling (RSS) and accelerated Receive Flow Steering (aRFS) have been introduced to distribute network flows across multiple CPU cores for incoming packet processing. While improving overall core utilization and cache locality, these schemes do not consider recent advancements such as non-uniform memory access (NUMA)-based architectures and direct cache access (DCA), leading to suboptimal performance, particularly when large and small flows are mixed. In this paper, we introduce a new receive flow steering scheme, mixed Flow Steering (mFS), which disaggregates large and small flows to optimize network flow steering in NUMA-based architectures. Our approach incorporates a flow monitoring module to classify flows based on cumulative data volume, a flow core migration mechanism that aligns application processing with the appropriate NUMA node, and adaptive handling of multi-large flow contention. Our experimental evaluations demonstrate that, by leveraging DCA, mFS significantly improves total throughput for large flows by 42.12% compared to existing schemes while maintaining comparable throughput for small flows across various mixed traffic scenarios and real-world applications.

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