Service Function Chaining (SFC) establishes efficient communication paths by ensuring that traffic traverses a predefined sequence of network functions in a specified order to meet particular service requirements. Inspired by this concept, we have proposed an SFC-based architecture for multi-hop split learning (MSL) and split inference (MSI), facilitating distributed AI applications to effectively route smashed data across multi-hop networks. However, the multi-hop environment presents new challenges, including (1) determining optimal cut points, (2) deploying split sub-models on appropriate computing nodes, and (3) routing smashed data through the underlying communication networks while adhering to service requirements. To address these challenges, we formulate an Integer Linear Programming (ILP) model to jointly optimize model splitting, placement, and chaining (data routing) in the SFC-based MSL/MSI architecture, aiming to minimize end-to-end inference or training latency. Additionally, we propose a Block Coordinate Descent (BCD)-based heuristic algorithm to efficiently solve the problem. Comprehensive evaluations demonstrate the effectiveness and characteristics of the proposed formulation and algorithm.

翻译：服务功能链（SFC）通过确保流量按指定顺序依次经过预定义的网络功能集以满足特定服务需求，从而建立高效通信路径。受此概念启发，我们提出了一种基于SFC的多跳分割学习（MSL）与分割推理（MSI）架构，使分布式人工智能应用能够在多跳网络中有效路由压缩数据。然而，多跳环境带来了新挑战，包括：（1）确定最优切分点；（2）将分割子模型部署至合适的计算节点；（3）在满足服务需求的前提下，通过底层通信网络对压缩数据进行路由。为解决上述问题，我们构建了一个整数线性规划（ILP）模型，在基于SFC的MSL/MSI架构中联合优化模型切分、部署与链式编排（数据路由），以最小化端到端推理或训练时延。此外，我们提出一种基于块坐标下降（BCD）的启发式算法以高效求解该问题。综合实验评估验证了所提建模与算法的有效性与特性。