Receive side scaling (RSS) is an NIC technology that provides the benefits of parallel receive processing in multiprocessing environments. However, RSS lacks a critical data steering mechanism that would automatically steer incoming network data to the same core on which its application thread resides. This absence causes inefficient cache usage if an application thread is not running on the core on which RSS has scheduled the received traffic to be processed and results in degraded performance. To remedy the RSS limitation, Intel's Ethernet Flow Director technology has been introduced. However, our analysis shows that Flow Director can cause significant packet reordering. Packet reordering causes various negative impacts in high-speed networks. We propose an NIC data steering mechanism to remedy the RSS and Flow Director limitations. This data steering mechanism is mainly targeted at TCP. We term an NIC with such a data steering mechanism “A Transport-Friendly NIC” (A-TFN). Experimental results have proven the effectiveness of A-TFN in accelerating TCP/IP performance.

Computing is now shifting towards multiprocessing. The fundamental goal of multiprocessing is improved performance through the introduction of additional hardware threads or cores (referred to as “cores” for simplicity). Modern network stacks can exploit parallel cores to allow either message-based parallelism or connection-based parallelism as a means to enhance performance. OpenSolaris has redesigned and parallelized to better utilize additional cores. Three special technologies, named Softring Set, Soft ring and Squeue are introduced in OpenSolaris for stack parallelization. In this paper, we study the OpenSolaris packet receiving process and its core parallelism optimization techniques. Experiment results show that these techniques allow OpenSolaris to achieve better network I/O performance in multiprocessing environments; however, network stack parallelization has also brought extra overheads for system. An effective and efficient network I/O optimization in multiprocessing environments is required to cross all levers of the network stack from network interface to application.

Lambda Station is an ongoing project of Fermi National Accelerator Laboratory and the California Institute of Technology. The goal of this project is to design, develop and deploy network services for path selection, admission control and flow based forwarding of traffic among data- intensive Grid applications such as are used in High Energy Physics and other communities. Lambda Station deals with the last-mile problem in local area networks, connecting production clusters through a rich array of wide area networks. Selective forwarding of traffic is controlled dynamically at the demand of applications. This paper introduces the motivation of this project, design principles and current status. Integration of Lambda Station client API with the essential Grid middleware such as the dCache/SRM Storage Resource Manager is also described. Finally, the results of applying Lambda Station services to development and production clusters at Fermilab and Caltech over, advanced networks such as DOE's UltraScience Net and NSF's UltraLight is covered.