Name
Abstract | ||
|---|---|---|
The microservice architecture is a Service-Oriented Architecture (SOA) where a service-based application can be composed of a number of smaller but independently concurrent running units, called microservices, to improve the performance and maintainability of the application. In an application with microservice architecture, an unexpected artifact state(s) inside a microservice may be exchanged to another microservice or other service units and corrupt the whole system of the application. On the other hand, the abnormal artifact operation pairs can be categorized into continuous and concurrent artifact anomalies which indicate that two sequential and parallel operations working on the same artifact resulting in abnormal behavior semantically. The recent studies showed that an SP-tree structure adopted in the detections of both anomalies inside a structured workflow can reduce the computation complexity of detection as linear. In this paper, we present a series of methods based on SP-tree to detect the artifact anomalies inside each microservice of the application during the design phase. Different from the design of applications with traditional services, where each service is assumed to contain all possible types of anomalies and cannot be modified directly, the designer of microservice is concerned with the limited scope and can modify each microservice based on the anomaly detection results. Our contribution includes identifying the artifact properties in a microservice architecture and the methods to detect the anomalies based on these properties which can simplify the detection of artifact anomaly in service-based applications. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICPADS51040.2020.00021 | 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) |
Keywords | DocType | ISSN |
microservices architecture,artifact anomaly,workflow | Conference | 1521-9097 |
ISBN | Citations | PageRank |
978-1-7281-8382-4 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Faisal Fahmi | 1 | 0 | 1.35 |
| Pei Shu Huang | 2 | 1 | 2.06 |
| Feng-jian Wang | 3 | 242 | 39.75 |