RDMAbox : Optimizing RDMA for Memory Intensive Workloads

TL;DR

RDMAbox introduces low-level RDMA optimizations with flexible libraries, significantly improving performance for memory-intensive workloads like remote paging and file systems in data centers.

Contribution

The paper presents novel RDMA request merging, chaining, and adaptive polling techniques that enhance throughput and latency, outperforming existing solutions.

Findings

Up to 4× throughput improvement in remote paging

Up to 83% reduction in tail latency for big data workloads

Up to 5.9× higher throughput in user space file systems

Abstract

We present RDMAbox, a set of low level RDMA optimizations that provide better performance than previous approaches. The optimizations are packaged in easy-to-use kernel and user space libraries for applications and systems in data center. We demonstrate the flexibility and effectiveness of RDMAbox by implementing a kernel remote paging system and a user space file system using RDMAbox. RDMAbox employs two optimization techniques. First, we suggest RDMA request merging and chaining to further reduce the total number of I/O operations to the RDMA NIC. The I/O merge queue at the same time functions as a traffic regulator to enforce admission control and avoid overloading the NIC. Second, we propose Adaptive Polling to achieve higher efficiency of polling Work Completion than existing busy polling while maintaining the low CPU overhead of event trigger. Our implementation of a remote paging system with RDMAbox outperforms existing representative solutions with up to 4? throughput improvement and up to 83% decrease in average tail latency in bigdata workloads, and up to 83% reduction in completion time in machine learning workloads. Our implementation of a user space file system based on RDMAbox achieves up to 5.9? higher throughput over existing representative solutions.