Abstract
With the development of cloud computing technology, the microservice architecture (MSA) has become a prevailing application architecture in cloud-native applications. Many user-oriented services are supported by many microservices, and the dependencies between services are more complicated than those of a traditional monolithic architecture application. In such a situation, if an anomalous change happens in the performance metric of a microservice, it will cause other related services to be downgraded or even to fail, which would probably cause large losses to dependent businesses. Therefore, in the operation and maintenance job of cloud applications, it is critical to mine the causality of the problem and find its root cause as soon as possible. In this paper, we propose an approach for mining causality and diagnosing the root cause that uses knowledge graph technology and a causal search algorithm. We verified the proposed method on a classic cloud-native application and found that the method is effective. After applying our method on most of the services of a cloud-native application, both precision and recall were over 80%.
Highlights
With the emergence of enterprise digital transformation, it has become practical for enterprise applications to migrate to a cloud platform
We have found no relevant work on combining a knowledge graph and a causal search algorithm to find the root cause of a defect or failure in a cloud platform
On the basis of the above definition, the root cause finding problem is formulated as follows: Given M N, assuming an anomaly is observed from metric Mi at a certain timestamp ti, that is, Yti = 1, our goal is to find a set of subjects Src and their metrics Mrc as the root cause of the anomaly
Summary
With the emergence of enterprise digital transformation, it has become practical for enterprise applications to migrate to a cloud platform. As cloud computing technology has developed, microservice architecture (MSA) has become a prevailing web application architecture. MSA divides complex software systems into single-function service components and can be independently developed and deployed. MSA is similar to the earlier service-oriented architecture (SOA). It further refines the concept of servicing but does not emphasize the heavy-duty service bus in the SOA framework. Due to the numerous components of microservices, the complex dependencies between services and the frequent updates of system versions based on microservices inevitably increase the probability of failure and the difficulty of problem diagnosis. In the operation and maintenance (O&M) task in MSA applications, it is important to find the root cause as soon as possible
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