Location
Grand Wailea, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2020 12:00 AM
End Date
10-1-2020 12:00 AM
Description
The documentation of IT landscapes is a challenging task which is still performed mainly manually. Technology and software development trends like agile practices and microservice-based architectures exacerbate the endeavours to keep documentation up-to-date. Recent research efforts for automating this task have not addressed runtime data for gathering the architecture and remain unclear regarding proper algorithms and visualization support. In this paper, we want to close this research gap by presenting two algorithms that 1) discover the IT landscape based on historical data and 2) create continuously architecture snapshots based on new incoming runtime data. We especially consider scenarios in which runtime artifacts or communications paths were removed from the architecture as those cases are challenging to unveil from runtime data. We evaluate our prototype by analyzing the monitoring data from 79 days of a big automotive company. The algorithms provided promising results. The implemented prototype allows stakeholders to explore the snapshots in order to analyze the emerging behavior of the microservice-based IT landscape.
Discovery of Microservice-based IT Landscapes at Runtime: Algorithms and Visualizations
Grand Wailea, Hawaii
The documentation of IT landscapes is a challenging task which is still performed mainly manually. Technology and software development trends like agile practices and microservice-based architectures exacerbate the endeavours to keep documentation up-to-date. Recent research efforts for automating this task have not addressed runtime data for gathering the architecture and remain unclear regarding proper algorithms and visualization support. In this paper, we want to close this research gap by presenting two algorithms that 1) discover the IT landscape based on historical data and 2) create continuously architecture snapshots based on new incoming runtime data. We especially consider scenarios in which runtime artifacts or communications paths were removed from the architecture as those cases are challenging to unveil from runtime data. We evaluate our prototype by analyzing the monitoring data from 79 days of a big automotive company. The algorithms provided promising results. The implemented prototype allows stakeholders to explore the snapshots in order to analyze the emerging behavior of the microservice-based IT landscape.
https://aisel.aisnet.org/hicss-53/os/it_governance/6