边缘-云连续体中的函数与数据复制感知放置
Replication-Aware Placement of Functions and Data in the Edge-Cloud Continuum
- Politecnico di Milano（米兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对边缘-云连续体中的FaaS，提出BLP模型与拓扑感知贪心启发式，联合调度函数与数据放置并考虑复制一致性，以最小化客户端延迟，实现近最优且计算高效。
AI中文摘要：
函数即服务（FaaS）已成为边缘-云连续体的主流编程模型。FaaS 本质上将无状态函数与其持久状态解耦。我们研究如何在异构一致性要求下考虑数据复制，联合调度函数并放置数据以最小化客户端延迟。我们引入一个二元线性规划（BLP）模型来计算最优放置，建立了严格的理论基线。由于 BLP 随基础设施节点数量呈三次方扩展，我们提出一种拓扑感知的贪心启发式算法，该算法能高效逼近最优解。我们的评估表明，该启发式算法以极低的计算成本实现接近最优的放置质量，使其适用于周期性系统重构。
英文摘要：
Function-as-a-Service (FaaS) has emerged as the prominent programming model for the edge-cloud continuum. FaaS inherently decouples stateless functions from their persistent state. We study how to jointly schedule functions and place data to minimize client latency, considering data replication under heterogeneous consistency requirements. We introduce a Binary Linear Programming (BLP) model to compute optimal placements, establishing a rigorous theoretical baseline. Since the BLP scales cubically with the infrastructure nodes, we propose a topology-aware greedy heuristic that efficiently approximates the optimal solution. Our evaluation shows that the heuristic achieves near-optimal placement quality at a fraction of the computational cost, making it suitable for periodic system reconfigurations.
