# Modeling Data Sovereignty in Public Cloud--A Comparison of Existing Solutions
## 4 Models and Computational Experiments
### Experiments  
The purpose of the experiment was to understand the impact of running Dataspace Connector software (version 8.0.2) on various public cloud platforms hosted in different geographic locations.  
As discovered during the systematic literature review, designing and implementing data sovereignty in public cloud environments requires additional software layers, which increase computational complexity and cause additional performance impacts. This paper presents research results related to the performance of accessing data spaces designed for data sharing and exchange. We decided to perform an experiment comparing the performance of the Dataspace Connector.  
The Dataspace Connector is an open-source implementation of the IDS connector [26]. A comprehensive list of IDS connector implementations is published monthly by the IDSA organization and is available on their website [27]. Currently, this list includes 38 implementations. The Dataspace Connector was selected for experimentation due to its high maturity level and open-source licensing. In the subsequent phase of experiments, we plan to include additional implementations of IDS connector components.  
In this phase, we hosted the Dataspace Connector in two different public cloud environments: the Google Cloud Platform and Microsoft Azure. We selected two Dataspace Connector API endpoints (out of a total of 187) that create or retrieve catalog entities:  
* GET/api/catalogs Get a list of base resources with pagination.
* POST/api/catalogs Create a base resource.  
All available Dataspace Connector API endpoints are listed in Appendix B.  
The Dataspace Connector has been executed on Docker runtime and HTTPS requests were sent to different endpoints hosted on virtual servers and response time was measured.  
We used three virtual machines with similar configuration, hosted in Europe and US regions.  
Detailed configuration was as follows:  
* VM 1 \(\odot\) Platform: GCP Compute Engine \(\odot\) VM size: 2 vCPU, 1 core, 8 GB memory \(\odot\) Operating System: Debian GNU/Linux 12 (bookworm) \(\odot\) Region: Europe Central 2--Warsaw, Poland
* VM 2 \(\odot\) Platform: GCP Compute Engine \(\odot\) VM size: 2 vCPU, 1 core, 8 GB memory \(\odot\) Operating System: Debian GNU/Linux 12 (bookworm) \(\odot\) Region: US Central 1--Council Bluffs, Iowa \(\bullet\) VM 3 \(\odot\) Platform: Azure Virtual Machine  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Environment** & **GET** * & **POST** * \\ \hline Azure EU & 211 [ms] & 187 [ms] \\ Azure EU-local test (public IP) & 105 [ms] & 104 [ms] \\ Azure EU-local test (local IP) & 99 [ms] & 98 [ms] \\ GCP EU & 190 [ms] & 207 [ms] \\ GCP EU-local test (public IP) & 154 [ms] & 134 [ms] \\ GCP EU-local test (local IP) & 133 [ms] & 115 [ms] \\ GCP US & 757 [ms] & 635 [ms] \\ GCP US-local test (public IP) & 125 [ms] & 116 [ms] \\ GCP US-local test (local IP) & 113 [ms] & 103 [ms] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataspace Connector response time, (*) average time for 100 requests.  
Figure 5: Response times for Dataspace Connector GET requests.