GeoLayer: Towards Low-Latency and Cost-Efficient Geo-Distributed Graph Stores with Layered Graph

2025-09-02

AI Summary

To address high latency and cost in geo-distributed cloud environments caused by graph data’s topological dependencies and localized access patterns, this paper proposes a hierarchical graph storage framework that jointly optimizes replica placement and request routing. Our approach introduces: (1) a latency-aware hierarchical graph structure to reduce decision complexity and mitigate network heterogeneity; (2) an overlap-aware replica placement strategy to improve coverage efficiency for critical subgraphs; (3) a directed hot-diffusion model for dynamic data allocation; and (4) a layer-wise expansion routing algorithm tailored to graph-pattern access characteristics. Experimental results demonstrate that the framework achieves 1.34×–3.67× speedup in online graph query response time and 1.28×–3.56× acceleration in offline graph analytics performance, significantly outperforming state-of-the-art baselines.

Technology Category

• Search and Optimization: Distributed Search
• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

The inherent connectivity and dependency of graph-structured data, combined with its unique topology-driven access patterns, pose fundamental challenges to conventional data replication and request routing strategies in geo-distributed cloud storage systems. In this paper, we propose GeoLayer, a geo-distributed graph storage framework that jointly optimizes graph replica placement and pattern request routing. We first construct a latency-aware layered graph architecture that decomposes the graph topology into multiple layers, aiming to reduce the decision space and computational complexity of the optimization problem, while mitigating the impact of network heterogeneity in geo-distributed environments. Building on the layered graph, we introduce an overlap-centric replica placement scheme to accommodate the diversity of graph pattern accesses, along with a directed heat diffusion model that captures heat conduction and superposition effects to guide data allocation. For request routing, we develop a stepwise layered routing strategy that performs progressive expansion over the layered graph to efficiently retrieve the required data. Experimental results show that, compared to state-of-the-art replica placement and routing schemes, GeoLayer achieves a 1.34x - 3.67x improvement in response times for online graph pattern requests and a 1.28x - 3.56x speedup in offline graph analysis performance.

Problem

• Optimizing geo-distributed graph storage for low latency
• Reducing computational complexity in graph replication strategies
• Improving request routing efficiency for pattern-based accesses

Innovation

• Layered graph architecture reduces complexity
• Overlap-centric replica placement for access diversity
• Stepwise layered routing for efficient data retrieval