Overload Control for Scaling WeChat Microservices
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Abstract (arXiv)
Effective overload control for large-scale online service system is crucial for protecting the system backend from overload. Conventionally, the design of overload control is ad-hoc for individual service. However, service-specific overload control could be detrimental to the overall system due to intricate service dependencies or flawed implementation of service. Service developers usually have difficulty to accurately estimate the dynamics of actual workload during the development of service. Therefore, it is essential to decouple the overload control from service logic. In this paper, we propose DAGOR, an overload control scheme designed for the account-oriented microservice architecture. DAGOR is service agnostic and system-centric. It manages overload at the microservice granule such that each microservice monitors its load status in real time and triggers load shedding in a collaborative manner among its relevant services when overload is detected. DAGOR has been used in the WeChat backend for five years. Experimental results show that DAGOR can benefit high success rate of service even when the system is experiencing overload, while ensuring fairness in the overload control.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 2; 2025: 0; 2026: 1.
6 papers.
- 2022Autothrottle: A Practical Bi-Level Approach to Resource Management for SLO-Targeted Microservicescited by 12
- 2023IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiencycited by 2
- 2021Sinan: Data-Driven, QoS-Aware Cluster Management for Microservicescited by 0
- 2024Analytically-Driven Resource Management for Cloud-Native Microservicescited by 0
- 2024Why does Prediction Accuracy Decrease over Time? Uncertain Positive Learning for Cloud Failure Predictioncited by 0
- 2026Denial of Deadline: Network-Driven Accuracy Collapse in Distributed Inference Pipelinescited by 0
- 2026Denial of Deadline: Network-Driven Accuracy Collapse in Distributed Inference Pipelines27 Jul 2026
- 2024Why does Prediction Accuracy Decrease over Time? Uncertain Positive Learning for Cloud Failure Prediction8 Jan 2024
- 2024Analytically-Driven Resource Management for Cloud-Native Microservices5 Jan 2024
- 2023IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency24 Aug 2023
- 2022Autothrottle: A Practical Bi-Level Approach to Resource Management for SLO-Targeted Microservices23 Dec 2022
- 2021Sinan: Data-Driven, QoS-Aware Cluster Management for Microservices27 May 2021