This paper introduces the Axon, an Ethernet-compatible device for creating large-scale datacenter networks. Axons are inexpensive, practical devices that are demonstrated using prototype hardware. Functionally, Axons replace Ethernet switches and maintain full compatibility with existing Ethernet hosts. Between themselves, however, Axons transparently use source-routed Ethernet. This unlocks many benefits, such as improved network scalability, performance, and flexibility.In an Axon network, all state required to route a host's packets is placed in the local Axon---the Axon to which the host is directly connected. Therefore, regardless of the scale of the network, the route computation and storage needs of a single Axon device only need to scale with the demands of its locally-connected hosts. This is in stark contrast to conventional switched Ethernet, which requires routing resources proportional to the traffic that flows through the device. Scalability is also increased by eliminating the use of packet flooding for automatic location and address discovery. Further, source-routed Ethernet increases network flexibility by supporting different route selection strategies. For example, shortest-path routing could be employed, or longer paths selected to minimize congestion by balancing traffic across redundant links.

Hadoop is a popular open-source implementation of MapReduce for the analysis of large datasets. To manage storage resources across the cluster, Hadoop uses a distributed user-level filesystem. This filesystem - HDFS - is written in Java and designed for portability across heterogeneous hardware and software platforms. This paper analyzes the performance of HDFS and uncovers several performance issues. First, architectural bottlenecks exist in the Hadoop implementation that result in inefficient HDFS usage due to delays in scheduling new MapReduce tasks. Second, portability limitations prevent the Java implementation from exploiting features of the native platform. Third, HDFS implicitly makes portability assumptions about how the native platform manages storage resources, even though native filesystems and I/O schedulers vary widely in design and behavior. This paper investigates the root causes of these performance bottlenecks in order to evaluate tradeoffs between portability and performance in the Hadoop distributed filesystem.

This paper presents hardware and software mechanisms to enable concurrent direct network access (CDNA) by operating systems running within a virtual machine monitor. In a conventional virtual machine monitor, each operating system running within a virtual machine must access the network through a software-virtualized network interface. These virtual network interfaces are multiplexed in software onto a physical network interface, incurring significant performance overheads. The CDNA architecture improves networking efficiency and performance by dividing the tasks of traffic multiplexing, interrupt delivery, and memory protection between hardware and software in a novel way. The virtual machine monitor delivers interrupts and provides protection between virtual machines, while the network interface performs multiplexing of the network data. In effect, the CDNA architecture provides the abstraction that each virtual machine is connected directly to its own network interface. Through the use of CDNA, many of the bottlenecks imposed by software multiplexing can be eliminated without sacrificing protection, producing substantial efficiency improvements