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Communication Dans Un Congrès Année : 2021

Adaptive Batching for Fast Packet Processing in Software Routers using Machine Learning

Résumé

Processing packets in batches is a common technique in high-speed software routers to improve routing efficiency and increase throughput. With the growing popularity of novel paradigms such as Network Function Virtualization, advocating for the replacement of hardware-based networking modules towards software-based network functions deployed on commodity servers, we observe that batching techniques have been successfully implemented to reduce the HW/SW performance gap. As batch creation and management is at the very core of high-speed packet processors, it provides a significant impact to the overall packet processing capabilities of the system, affecting latency, throughput, CPU utilization and power consumption. It is commonly accepted to adopt a fixed maximum batching size (usually in the range between 32 and 512) to optimize for the worst case scenario (i.e. minimum-size packets at full bandwidth capacity). Such approach may result in a loss of efficiency despite a 100% utilization of the CPU. In this work we explore the possibilities of enhancing the runtime batch creation in VPP, a popular software router based on the Intel DPDK framework. Instead of relying on the automatic batch creation, we apply machine learning techniques to optimize the batching size for lower CPU-time and higher power efficiency in average scenarios, while maintaining its high performance in the worst case.
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Dates et versions

hal-04322392 , version 1 (08-12-2023)

Identifiants

Citer

Peter Okelmann, Leonardo Linguaglossa, Fabien Geyer, Paul Emmerich, Georg Carle. Adaptive Batching for Fast Packet Processing in Software Routers using Machine Learning. 2021 IEEE 7th International Conference on Network Softwarization (NetSoft), Jun 2021, Tokyo, Japan. pp.206-210, ⟨10.1109/NetSoft51509.2021.9492668⟩. ⟨hal-04322392⟩
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