Performance characterization of hadoop workloads on SR-IOV-enabled virtualized InfiniBand clusters

Abstract

Big Data Systems are becoming increasingly complex and generally have very high operational costs. Cloud computing offers attractive solutions for managing large scale systems. However, one of the major bottlenecks in VM performance is virtualized I/O. Since Big Data applications and middleware rely heavily on high performance interconnects such as InfiniBand, the performance of virtualized InfiniBand interfaces is vital. Single Root I/O Virtualization (SR-IOV) is a hardware based approach which offers significant performance benefits as compared to software based I/O virtualization. With the increasing adoption of InfiniBand network for cloud computing, it is important to evaluate the performance benefits of SR-IOV for InfiniBand networks; especially to see the performance characteristics of Big Data applications and middleware under different scenarios. We characterize the main performance factors for different workloads through this study (such as map task scheduling, I/O, data replication, etc.). Our experimental evaluations show that the performance difference for a wide set of Big Data benchmarks and applications over SR-IOV with InfiniBand using RDMA-enabled Hadoop as compared to native InfiniBand network is just 5 -- 15%. In addition, with RDMA-enabled Hadoop, we see 20.9 -- 81.6% performance improvement for RDMA as compared to IPoIB.