𝜆Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing

Haichuan Hu, Fangming Liu, Qiangyu Pei, Yongjie Yuan, Zichen Xu, Lin Wang

Keywords: Serverless Computing, Graph Neural Networks, Model Serving

Abstract:

Graph Neural Networks (GNNs) have been increasingly adopted for graph analysis in web applications such as social networks. Yet, efficient GNN serving remains a critical challenge due to high workload fluctuations and intricate GNN operations. Serverless computing, thanks to its flexibility and agility, offers on-demand serving of GNN inference requests. Alas, the request-centric serverless model is still too coarse-grained to avoid resource waste.

Observing the significant data locality in computation graphs of requests, we propose 𝜆Grapher, a serverless system for GNN serving that achieves resource efficiency through graph sharing and fine-grained resource allocation. 𝜆Grapher features the following designs: (1) adaptive timeout for request buffering to balance resource efficiency and inference latency, (2) graph-centric scheduling to minimize computation and memory redundancy, and (3) resource-centric function management with fine-grained resource allocation catered to the resource sensitivities of GNN operations and function orchestration optimized to hide communication latency. We implement a prototype of 𝜆Grapher based on the representative open-source serverless platform Knative and evaluate it with real-world traces from various web applications. Our results show that 𝜆Grapher can achieve savings of up to 54.2% in memory resource and 45.3% in computing resource compared with the state-of-the-art while ensuring GNN inference latency.

Track: Systems and Infrastructure for Web, Mobile, and WoT

Submission Guidelines Scope: Yes

Submission Guidelines Blind: Yes

Submission Guidelines Format: Yes

Submission Guidelines Limit: Yes

Submission Guidelines Authorship: Yes

Student Author: Yes

Submission Number: 414