Simmer: Rate proportional scheduling to reduce packet drops in vGPU based NF chains

Network Function Virtualization (NFV) paradigm offers flexibility, cost benefits, and ease of deployment by decoupling network function from hardware middleboxes. The service function chains (SFC) deployed using the NFV platform require efficient sharing of resources among various network functions in the chain. Graphics Processing Units (GPUs) have been used to improve various network functions’ performance. However, sharing a single GPU among multiple virtualized network functions (virtual machines) in a service function chain has been challenging due to their proprietary hardware and software stack. Earlier GPU architectures had a limitation: a single physical GPU can only be allocated to one virtual machine (VM) and cannot be shared among multiple VMs. The newer GPUs are virtualization-aware (hardware-assisted virtualization) and allow multiple virtual machines to share a single physical GPU. Although virtualization-aware, these GPUs still lack support for custom scheduling policies and do not expose the preemption control to users. When network functions (hosted within virtual machines) with different processing requirements share the same GPU, virtualization-aware GPUs’ default round-robin scheduling mechanism proves to be inefficient, resulting in packet drops and lower throughput. This paper presents Simmer, an efficient mechanism for scheduling a network function service chain on virtualization-aware GPUs. Our scheduling solution considers the processing requirement of NFs in a GPU-based SFC, thus improving overall throughput by up to 29% and reducing the packet drop to zero compared to vanilla setup.