Software network functions promise to simplify the deployment of network services and reduce network operation cost. However, they face the challenge of unpredictable performance. Given this performance variability, it is imperative that during deployment, network operators consider the performance of the NF not only for typical but also adversarial workloads. We contribute a tool that helps solve this challenge: it takes as input the LLVM code of a network function and outputs packet sequences that trigger slow execution paths. Under the covers, it combines directed symbolic execution with a sophisticated cache model to look for execution paths that incur many CPU cycles and involve adversarial memory-access patterns. We used our tool on 11 network functions that implement a variety of data structures and discovered workloads that can in some cases triple latency and cut throughput by 19% relative to typical testing workloads.

Cloud providers typically implement abstractions for network virtualization on the server, within the operating system that hosts the tenant virtual machines or containers. Despite being flexible and convenient, this approach has fundamental problems: incompatibility with bare-metal support, unnecessary performance overhead, and susceptibility to hypervisor breakouts. To solve these, we propose to offload the implementation of network-virtualization abstractions to the top-of-rack switch (ToR). To show that this is feasible and beneficial, we present VNToR, a ToR that takes over the implementation of the security-group abstraction. Our prototype combines commodity switching hardware with a custom software stack and is integrated in OpenStack Neutron. We show that VNToR can store tens of thousands of access rules, adapts to traffic-pattern changes in less than a millisecond, and significantly outperforms the state of the art.