DLA: Detecting and Localizing Anomalies in Containerized Microservice Architectures Using Markov Models Containerbased microservice architectures are emerging as a new approach for building distributed applications as a collection of independent services that works together. As a result, with microservices, we are able to scale and update their applications based on the load attributed to each service. Monitoring and managing the load in a distributed system is a complex task as the degradation of performance within a single service will cascade reducing the performance of other dependent services. Such performance degradations may result in anomalous behaviour observed for instance for the response time of a service. This paper presents a Detection and Localization system for Anomalies (DLA) that monitors and analyzes performancerelated anomalies in containerbased microservice architectures. To evaluate the DLA, an experiment is done using R, Docker and Kubernetes, and different performance metrics are considered. The results show that DLA is able to accurately detect and localize anomalous behaviour.

ADGS: Anomaly Detection and Localization Based on Graph Similarity in ContainerBased Clouds Docker container is experiencing rapid development with the support from the industry like Google and Alibaba and is being widely used in large scale production cloud environment. For example, Alibaba has deployed millions of containers for its internal business, and most of the online services are already migrated to the containers. Those services are usually very complex, spanning multiple containers with complex interaction and dependency relationship. Detecting potential anomalies in such a large containerbased cloud platform is very challenging. Traditional detection models usually use system resource metrics like CPU and memory usage, but rarely consider the relationship among components, causing high false positive rate. In this paper, we present a novel Anomaly Detection and root cause localization method based on Graph Similarity (ADGS) in the containerbased cloud environment. We first monitor the response time and resource usage of each component in the application to determine whether the system status is normal or not. Then, we propose a new mechanism to locate the root cause of the anomalies based on graph similarity, investigating the anomaly propagation rules among cluster components. We implement and evaluate our method in a containerbased environment. The results show that the proposed method can detect and determine the root cause of anomalies efficiently and accurately.