Adaptive and Scalable Optimizations for High Performance SR-IOV

Abstract

High performance networking interfaces, such as 10-Gigabit Ethernet (10GE), are now widely deployed in commercial Cloud computing environments. Virtualization is a standard technique for these environments, one of whose key challenges is to achieve highly efficient and scalable I/O virtualization. Single Root I/O Virtualization (SR-IOV) eliminates the overhead of redundant data copies and the virtual network switch through direct I/O, but needs more work on performance and scalability. In this paper, we first study the defects of SR-IOV with 10GE networking and find two major challenges. Due to multiplexing of traffic from different virtual machines, SR-IOV may generate redundant interrupts unexpectedly and thus result in high CPU overhead. SR-IOV also suffers from single-threaded NAPI which prevents it from fully utilizing multi-core machines. Then we propose two optimizations for enhancing the SR-IOV performance. The first uses adaptive interrupt rate control (AIRC) to reduce CPU overhead caused by excessive interrupts. The second is a multi-threaded network driver (MTND) which allows SR-IOV to make full use of multi-core resources. We implement these optimizations and carry out a detailed performance evaluation. The results show that AIRC can reduce CPU overhead by up to 143% and MTND can improve SR-IOV performance by up to 38%.