RAGraph: A Region-Aware Framework for Geo-Distributed Graph Processing

• Published in: Proceedings of the VLDB Endowment 2023-11, Vol.17 (3), p.264-277
• Main Authors: Yao, Feng, Tao, Qian, Yu, Wenyuan, Zhang, Yanfeng, Gong, Shufeng, Wang, Qiange, Yu, Ge, Zhou, Jingren
• Format: Article
• Language: English
• ISSN: 2150-8097; 2150-8097
• DOI: 10.14778/3632093.3632094

In many global businesses of multinational enterprises, graph-structure data is usually geographically distributed in different regions to support low-latency services. Geo-distributed graph processing suffers from the Wide Area Networks (WANs) with scarce and heterogeneous bandwidth, thus essentially differs from traditional distributed graph processing. In this paper, we propose RAGraph, a Region-Aware framework for geo-distributed graph processing. At the core of RAGraph, we design a region-aware graph processing framework that allows advancing inefficient global updates locally and enables sensible coordination-free message interactions. RAGraph also contains an adaptive hierarchical message interaction engine to switch interaction modes adaptively based on network heterogeneity and fluctuation, and a discrepancy-aware message filtering strategy to filter important messages. Finally, the experiments show that RAGraph can achieve 1.69X - 40.53X speedup and 20.9% - 97% WAN cost reduction compared with state-of-the-art systems.