We develop a holistic framework for adaptively scheduling asynchronous requests in distributed file systems. The system is holistic in that it manages all resources, including network bandwidth, server I/O, server CPU, and client and server memory utilization. It accelerates, defers, or cancels asynchronous requests in order to improve application-perceived performance directly. We employ congestion pricing via online auctions to coordinate the use of system resources by the file system clients so that they can detect shortages and adapt their resource usage. We implement our modifications in the Congestion-Aware Network File System (CA-NFS), an extension to the ubiquitous network file system (NFS). Our experimental result shows that CA-NFS results in a 20% improvement in execution times when compared with NFS for a variety of workloads.

NFS has traditionally used TCP or UDP as the underlying transport. However, the overhead of these stacks has limited both the performance and scalability of NFS. Recently, high-performance network such as InfiniBand have been deployed. These networks provide low latency of a few microseconds and high bandwidth for large messages up to 20 Gbps. Because of the unique characteristics of NFS protocols, previous designs of NFS with RDMA were unable to exploit the improved bandwidth of networks such as InfiniBand. Also, they leave the server open to attacks from malicious clients. In this paper, we discuss the design principles for implementing NFS/RDMA protocols. We propose, implement and evaluate an alternate design for NFS/RDMA on InfiniBand, which can significantly improve the security of the server, compared to the previous design. In addition, we evaluate the performance bottlenecks of using RDMA operations in NFS protocols and propose strategies and designs that tackle these overheads. With the best of these strategies and designs, we demonstrate throughput of 700 MB/s on the OpenSolaris NFS/RDMA design and 900 MB/s on the Linux design and an application level improvement in performance of up to 50%. We also evaluate the scalability of the RDMA transport in a multi-client setting, with a RAID array of disks. Our design has been integrated into the OpenSolaris kernel.