## 5 Results
### Performance Evaluation
#### 5.3.2 Dynamic Checkpointing for CiC Algorithms  
We ran different scenarios with diverse values for QoS parameters, and followed an exponential distribution, simulating real-world conditions, see Figures 6-8. Therefore, the crisp values for the FCSE varied from 0 to 5 after evaluated by the FIS system; this was named the FCSE window. This window can be very rigorous; for instance, we took forced checkpoints when the output computed crisp value of the FCSE satisfied that it was equal to 0.625 or lower. We referred to a relax window to that one allowing a more degraded system, having an FCSE crisp output value of 3.75. Also, we considered crisp intermediate values between the rigorous window and the relaxed window and evaluate windows with the following values: \(0.625,1.25,1.875,2.5,3.125,\) y 3.75. In order to show that our proposal can be adapted, to simulate several system requirements, we also ran different scenarios where we include four QoS non-functional parameters, such as CPU (Good Medium, Bad), Memory (Good, Medium, Bad), Response Time (Good, Average, Bad) and Reliability(Good, Average, Bad), see Figures 9-11. We compared the performance of three different CiC algorithms, namely DCFI, FI and FINE. These were chosen because they are recent algorithms and the most efficient reported in the literature. We simulated and analyzed these three algorithms using the distributed checkpointing simulator ChkSim [16] and JFuzzyLogic. ChkSim simulator, models distributed systems in a deterministic manner reproducing the same behavior for two or more algorithms, and allows running simulation as often as necessary. We took the number of forced checkpoints as key performance indicator to compare each checkpointing algorithm regarding the overhead each one of them produce. Figure 8: The number of forced checkpoints for 5000 messages sent. Evaluating three Quality of Service (QoS) non-functional parameters  
Figure 9: The number of forced checkpoints for 1000 messages sent. Evaluating four QoS non-functional parameters. In Figures 6-8, we analyzed the performance of three different communication-induced checkpointing mechanisms (DFCI, FINE, and FI) in terms of forced checkpoints. We used the next configuration; scenario one consisted of 0 to 160 concurrent clients each sending 1000 messages; scenario two consisted of 0 to 160 concurrent clients each sending of 2500; scenario three consisted 0 to 160 concurrent clients each sending 5000 messages; for each scenario, we varied the QoS window. Again to have a significant statistical sample we executed each point of each scenario for 100 iterations with different checkpointing patterns as well as their communication or messages exchanged. Figures 6-8 shows that the DCFI, for all checkpointing mechanisms, generated the lowest number of forced checkpoints, this has to do with the QoS window. For example, for a rigorous QoS the number of forced checkpoints was similar to the one presented observed for DCFI without using our approach. Yet, as the window relaxed the number of forced checkpoints reduced drastically. For all cases the best performing checkpointing mechanism was the DCFI, because it generated fewer checkpoints  
Figure 11: The number of forced checkpoints for 5000 messages sent. Evaluating four QoS non-functional parameters. Figure 10: The number of forced checkpoints for 2500 messages sent. Evaluating four QoS non-functional parameters. compared to FI and FINE. FI was the second-best checkpointing mechanism, and FINE also reduced its checkpointing generation but compared to the other two mechanisms it presented the worst cases. Experiments presented in Figures 9-11 shows that if the number of non-functional parameters increased to evaluate the system performance, then this allowed relaxing and detecting smaller changes in the system behavior; as a consequence, fewer forced checkpoints were generated. For all the cases presented in these Figs. the number of checkpoints generated was lower by improving the granularity that monitors the system behavior.