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The microservices architecture enables independent development and maintenance of application components through its fine-grained and modular design. This has enabled rapid adoption of microservices architecture to build latency-sensitive online applications. In such online applications, it is critical to detect and mitigate sources of performance degradation (bottlenecks). However, the modular design of microservices architecture leads to a large graph of interacting microservices whose influence on each other is non-trivial. In this preliminary work, we explore the effectiveness of Graph Neural Network models in detecting bottlenecks. Preliminary analysis shows that our framework, B-MEG, produces promising results, especially for applications with complex call graphs. B-MEG shows up to 15% and 14% improvements in accuracy and precision, respectively, and close to 10X increase in recall for detecting bottlenecks compared to the technique used in existing work for bottleneck detection in microservices.
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Informer: Irregular Traffic Detection for Containerized Microservices RPC in the Real World
Containerized microservices have been widely deployed in industry. Meanwhile, security issues also arise. Many security enhancement mechanisms for containerized microservices require predefined rules and policies. However, it is challenging when it comes to thousands of microservices and a massive amount of real-time unstructured data. Hence, automatic policy generation becomes indispensable. In this paper, we focus on the automatic solution for the security problem: irregular traffic detection for RPCs. We propose Informer, which is a two-phase machine learning framework to track the traffic of each RPC and report anomalous points automatically. Firstly, we identify RPC chain patterns by density-based clustering techniques and build a graph for each critical pattern. Next, we solve the irregular RPC traffic detection problem as a prediction problem for time-series of attributed graphs by leveraging spatial-temporal graph convolution networks. Since the framework builds multiple models and makes individual predictions for each RPC chain pattern, it can be efficiently updated upon legitimate changes in any of the graphs. In evaluations, we applied Informer to a dataset containing more than 7 billion lines of raw RPC logs sampled from an large Kubernetes system for two weeks. We provide two case studies of detected real-world threats. As a result, our framework found fine-grained RPC chain patterns and accurately captured the anomalies in a dynamic and complicated microservice production scenario, which demonstrates the effectiveness of Informer.
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- Award ID(s):
- 1801751
- PAR ID:
- 10156939
- Date Published:
- Journal Name:
- ACM/IEEE Workshop on Security and Privacy in Edge Computing (Edge S&P)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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