# Latency Analysis of Blockchain-Based SSI Applications +
## 4 Evaluation Results
### Four-Node Measurements in a Private Cloud  
For the experiments, we used our local, private cloud deployed on a Synergy 480 Gen10 Server, which has two CPUs; both of them are Intel(R) Xeon(R) Gold 5118 with 12 cores running on 2.30 GHz. The server has 384 GB RAM. The Indy ledger consisted \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**TX** & \multicolumn{5}{c}{**Arrival Rate (TXs/s)**} \\ \cline{2-7}
**Type** & **1** & _50_ & _100_ & _150_ & _200_ & _250_ \\ \hline
**Min (W)** & 1.1014 & 1.1014 & 1.1014 & 1.1014 & 1.1014 & 1.1014 \\ \hline
**Max (W)** & 2.9832 & 2.9138 & 2.9490 & 5.8131 & 6.8542 & 13.3811 \\ \hline
**Avg (W)** & 2.7388 & 2.5284 & 2.2197 & 4.4763 & 5.3511 & 6.3534 \\ \hline
**TX** & \multicolumn{5}{c}{**Arrival Rate (TXs/s)**} \\ \cline{2-7}
**Type** & **1** & _50_ & _100_ & _150_ & _200_ & _250_ \\ \hline
**Min (R)** & 0.0108 & 0.0671 & 0.1058 & 0.0841 & 0.2469 & 0.0800 \\ \hline
**Max (R)** & 0.2160 & 1.1387 & 1.4655 & 2.2378 & 2.5302 & 4.7086 \\ \hline
**Avg (R)** & 0.0881 & 0.3961 & 0.5815 & 0.8964 & 1.1408 & 1.5562 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Latency measures, in seconds, for read (R) and write (W) TXs where network size = 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**TX** & \multicolumn{5}{c}{**Arrival Rate (TXs/s)**} \\ \cline{2-7}
**Type** & **1** & _50_ & _100_ & _150_ & _200_ & _250_ \\ \hline
**Min (W)** & 0.2614 & 1.7064 & 1.5032 & 1.3114 & 1.2914 & 1.5289 \\ \hline
**Max (W)** & 2.9949 & 2.9607 & 5.2011 & 8.5050 & 7.4365 & 16.9094 \\ \hline
**Avg (W)** & 2.6118 & 2.5993 & 2.5347 & 4.8506 & 5.5883 & 10.4323 \\ \hline
**TX** & \multicolumn{5}{c}{**Arrival Rate (TXs/s)**} \\ \cline{2-7}
**Type** & **1** & _50_ & _100_ & _150_ & _200_ & **250** \\ \hline
**Min (R)** & 0.0092 & 0.0567 & 0.0927 & 0.0667 & 0.0350 & 0.0219 \\ \hline
**Max (R)** & 0.7147 & 1.1744 & 1.4469 & 1.9189 & 2.4340 & 5.6863 \\ \hline
**Avg (R)** & 0.1203 & 0.3718 & 0.5430 & 0.8163 & 1.0652 & 2.4928 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Latency measures, in seconds, for read (R) and write (W) TXs where network size = 8.  
of four Docker containers, created from the official Indy-SDK images, using the official Docker Compose description file. The test script was written in Python and executed in a Jupyter notebook, just like the official Indy demo commands. Table 3 provides the detailed read and write latency measurements with a four-node network. We also evaluated an eight-node local network; the results can be found in Table 4. We found once again that the size of the dataset stored on-chain does not affect the latency. We can also see that increasing the arrival rate increases the average latency for both types of requests. It is observable in the table that the average write latency fluctuates from 1.8 to 7.1 s per TX in the four-node network, and from 2.0 to 7.7 s per TX in the eight-node network, depending on the arrival rate, as Figure 4 shows. On the other hand, the average read latency fluctuates from 0.03 to 1.4 s per TX in the four-node network, and from 0.03 to 2.4 s per TX in the eight-node network, as shown in Figure 5.