一句话：提出面向 Serverless 的图计算方案 FaaSGraph，通过框架与 Serverless 系统协同设计大幅提升性能并降低成本。
图处理在云服务中广泛应用，但基于 IaaS 的现有框架受弹性限制，在部署时效率与成本效益不佳。本文提出 FaaSGraph，一种 Serverless 原生的图计算方案，通过图处理框架与 Serverless 计算系统协同设计来兼顾效率与经济性。具体地，设计以数据为中心的 Serverless 执行模型以支撑重型计算任务，并设计与之无缝配合的图处理范式。实验表明，相比最先进的 IaaS 方法，FaaSGraph 端到端性能最高提升 8.3 倍，内存使用最多降低 52.4%；在负载高度波动时仍保持稳定的 99 百分位性能，并将金钱成本降低 85.7%。
Graph processing is widely used in cloud services; however, current frameworks face challenges in efficiency and cost-effectiveness when deployed under the Infrastructure-as-a-Service model due to its limited elasticity. In this paper, we present FaaSGraph, a serverless-native graph computing scheme that enables efficient and economical graph processing through the co-design of graph processing frameworks and serverless computing systems. Specifically, we design a data-centric serverless execution model to efficiently power heavy computing tasks. Furthermore, we carefully design a graph processing paradigm to seamlessly cooperate with the data-centric model. Our experiments show that FaaS-Graph improves end-to-end performance by up to 8.3X and reduces memory usage by up to 52.4% compared to state-of-the-art IaaS-based methods. Moreover, FaaSGraph delivers steady 99%-ile performance in highly fluctuated workloads and reduces monetary cost by 85.7%.
@inproceedings{LiuSL0GHLG24,
title = {FaaSGraph: Enabling Scalable, Efficient, and Cost-Effective Graph Processing with Serverless Computing},
author = {Yushi Liu and Shixuan Sun and Zijun Li and Quan Chen and Sen Gao and Bingsheng He and Chao Li and Minyi Guo},
booktitle = {ASPLOS},
year = {2024},
doi = {10.1145/3620665.3640361}
}