Most packet processing applications receive and process multiple types of packets. Today, the processors available within packet processing systems are allocated to packet types at design time. In this paper, we explore the benefits and challenges of adapting allocations of processors to packet types in packet processing systems. We demonstrate that, for all the applications and traces considered, run-time adaptation can reduce energy consumption by 70-80% and processor provisioning level by 40-50%. The adaptation benefits are maximized if processor allocations can be adapted at fine timescales and if the total available processing power can be allocated to packet types in small granularities. We show that, of these two factors, allocating processing power to packet types in small granularity is more important-if the allocation granularity is large, then even a very fine adaptation time-scale yields meager benefits.

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, profiledriven 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.

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