Function as a Service for the Storage of Large Geospatial Data

Applications grow over time. While they are usually small at the beginning, more and more features are added over the years. At some point, this leads to a heavy monolithic system. On the other hand, there is a trend towards deploying applications in smaller units. The most recent stage of this development is Function as a Service (FaaS). It uses isolated, short-running and stateless functions. This reduces the complexity of individual functions compared to the entire monolithic application. Furthermore, a framework can scale the functions up and down as needed. To run an existing monolithic application as FaaS, it has to be adjusted. This thesis presents a strategy for the migration process. It analyzes the processing flow in the monolithic application and defines criteria for a division into functions. The suitability of the process is demonstrated based on an existing application. For this purpose two functional concepts with different focuses are developed. The first one preserves the backwards compatibility to the monolithic application and thus allows a flexible change between a monolithic operation and a FaaS execution. The second concept focuses on a fine division of functions. Here the compatibility to the monolithic application is lost, but the implementation becomes more flexible and can be extended more easily. To execute the designed functions, an improved scaling metric is presented. It is based on the number of outstanding function requests and integrates into the underlying FaaS-Framework. The evaluation shows that the presented functional concepts are suitable for processing real world data. Both concepts lead to an speed up in processing compared to the monolithic application. However, this performance gain is accompanied by an increased resource consumption, so that the use of a FaaS-based solution must be weighed up depending on the situation.