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Please draw the graphs in the same format as in Figure 4 . I drew some pictures, please modify them. Thank you so much. project.

Please draw the graphs in the same format as in Figure 4. I drew some pictures, please modify them. Thank you so much. project. For an extended analysis, Figure 4 shows the key performance results of the voting algorithms.
The performance parameters are the time to deliver a data to the end-user (TTC). the number of distinct
data proposals before effecting a data delivery, and the control message overhead expended to deliver
a data. One of the influential parameters is the fault severity of a mis-behaving voter: denoted as a
parameter r, where r is the probability that a faulty device does in fact behave incorrectly. For e.g.,
r=0.1 means that a faulty device x behaves incorrectly only for 10% of the times x participates in
the algorithm operations - which means that x behaves correctly for 90% of the times. The results
are shown for a case of r=0.5. Empirically plot the results for two other cases: r=0.1 and r=0.8.
Show the plots on top of the results given for r=0.5.
Figure 4: Experimental performance results of replica voting algorithm
I use a software to draw the graphs, r=0.1 : shown by the yellow line of each graph, r=0.8 : shown by the red line
of each graph.
The explanation for r=0.1 suggests that with a lower fault severity, the algorithm will perform more efficiently,
resulting in a lower Time to Consistency (TTC), fewer voting steps needed before successful data delivery, and
lower control message overhead. Therefore, in the chart, we would expect the performance line for r=0.1 to
be below the line for r=0.5, indicating better performance.
For r=0.8, the explanation indicates that a higher fault severity leads to a worse performance. This is due to
the higher probability of failure, which increases the TTC, the number of voting steps required, and the control
message overhead. Consequently, the performance line for r=0.8 would be above the line for r=0.5 on the
chart.
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