A Microservice Graph Generator with Production Characteristics

• URL: http://arxiv.org/abs/2412.19083v1
• Date: Thu, 26 Dec 2024 06:51:35 GMT
• Title: A Microservice Graph Generator with Production Characteristics
• Authors: Fanrong Du, Jiuchen Shi, Quan Chen, Li Li, Minyi Guo,
• Abstract summary: We propose a Service Dependency Graph Generator (DGG) that comprises a Data Handler and a Graph Generator. DGG generates the service dependency graphs of benchmarks that incorporate production-level characteristics from traces. Case studies show that DGG's generated graphs similar to real traces in terms of topologies.
• Score: 14.487102827568856
• License:
• Abstract: A production microservice application may provide multiple services, queries of a service may have different call graphs, and a microservice may be shared across call graphs. It is challenging to improve the resource efficiency of such complex applications without proper benchmarks, while production traces are too large to be used in experiments. To this end, we propose a Service Dependency Graph Generator (DGG) that comprises a Data Handler and a Graph Generator, for generating the service dependency graphs of benchmarks that incorporate production-level characteristics from traces. The data handler first constructs fine-grained call graphs with dynamic interface and repeated calling features from the trace and merges them into dependency graphs, and then clusters them into different categories based on the topological and invocation types. Taking the organized data and the selected category, the graph generator simulates the process of real microservices invoking downstream microservices using a random graph model, generates multiple call graphs, and merges the call graphs to form the small-scale service dependency graph with production-level characteristics. Case studies show that DGG's generated graphs are similar to real traces in terms of topologies. Moreover, the resource scaling based on DGG's fine-grained call graph constructing increases the resource efficiency by up to 44.8% while ensuring the required QoS.

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