The complexity of packet-processing applications continues to grow, with encryption, compression, and XML processing becoming common on packet-processing devices at the edge of enterprise and service provider networks. While performance remains a key differentiator for these devices, the complexity and rate of change in the supported applications has made general-purpose platforms an attractive alternative to ASICs and network processors. General-purpose platforms offer excellent programmability and a wealth of existing software, in the form of operating systems, libraries, and applications that can be used to build a packet-processing system; however, the performance of general-purpose operating systems is unacceptable for many environments. This has driven developers to either make derivative versions of existing operating systems or to use special-purpose operating systems with a less comprehensive and familiar library of existing software.As part of the Symerton project, we propose using virtualization to address these issues. We have designed a system that has a virtual machine dedicated to high-performance networking, and a virtual machine dedicated to hosting non-performance critical tasks in a general-purpose operating system. Using a proof-of-concept implementation, we show that the resulting system outperforms a general-purpose operating system by an average of 22% for a real networking application. We also discuss tradeoffs that will need to be considered in further development of systems using this design.

Network edge packet-processing systems, as are commonly implemented on network processor platforms, are increasingly required to support a rich set of services. These multi-service systems are also subjected to widely varying and unpredictable traffic. Current network processor systems do not simultaneously deal well with a variety of services and fluctuating workloads. For example, current methods of worst-case, static provisioning can meet performance requirements for any workload, but provisioning each service for its worst case reduces the total number of services that can be supported. Alternately, profile-driven automatic-partitioning compilers create efficient binaries for multi-service applications for specific workloads but they are sensitive to workload fluctuations.Run-time adaptation is a potential solution to this problem. With run-time adaptation, the mapping of services to system resources can be dynamically adjusted based on the workload. We have implemented an adaptive system that automatically changes the mapping of services to processors, and handles migration of services between different processor core types to match the current workload. In this paper we explain our adaptive system built on the Intel® IXP2400 network processor. We demonstrate that it outperforms multiple different profile-driven compiled solutions for most workloads and performs within 20% of the optimal compiled solution for the remaining workloads.

This paper presents a qualitative spatial model for manufactured components capable of supporting systematic spatial inferencing about the component. The model incorporates the features of the component as well as mathematical spatial relationships between the features. With the incorporation of these spatial relationships, the model is capable of supporting spatial inferencing tasks of benefit to the planning phases of manufacturing. As one example, spatial inferencing task, this paper describes how to find the multiple interpretations of a component.