MrgRecmp4: Efficient Stateful SFC Recompilation in PDP Switches with Flexible Table Merging
Tingyu Li, Zhen Wei, Xiaoliang Chen, Zuqing Zhu
Abstract
The fast development of P4-based programmable data plane (PDP) has motivated the offloading of service function chains (SFCs) to switches. To adapt to dynamic service demands, PDP switches will need to be reprogrammed occasionally to update their packet processing pipelines, especially for stateful SFCs. In this work, we propose a novel stateful SFC recompilation system, namely, MrgRecmp4. It jointly considers the existing and new SFCs to optimize the P4 program recompilation for deploying new SFCs as reconstructed pipelines, such that the complexity of SFC reconfiguration can be minimized together with the hardware resource utilization in PDP switches. We first lay out the system design of MrgRecmp4, and explain how to leverage a flexible and fine-grained table merging scheme to optimize the P4 programs of stateful SFCs. Then, we formulate a bi-level integer linear programming (BILP) model to accomplish network-wide optimization for MrgRecmp4, which jointly considers the existing and new SFCs in a PDP network to determine the PDP switches that need to be reprogrammed and to generate reconstructed pipelines for them with table merging. We also propose a sliding-window-based heuristic to solve the network-wide optimization quickly. Both simulations and real-world experiments confirm that MrgRecmp4 outperforms existing benchmarks.
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