eNetSTL: Towards an In-kernel Library for High-Performance eBPF-based Network Functions
Bin Yang, Dian Shen, Junxue Zhang, Hanlin Yang, Lunqi Zhao, Beilun Wang, Guyue Liu, Kai Chen
Abstract
Using extended Berkeley Packet Filter (eBPF) to implement networking functions (NFs) has been a promising trend for modern network infrastructure. In this paper, we endeavor to implement 35 representative NFs with eBPF, but encounter inherent problems of either incomplete functionality or performance degradation of up to 49.2%. Conventional solutions like modifying the eBPF infrastructure or implementing functions directly in the kernel can lead to intrusive and unstable modifications.
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