# FANT: Flexible Attention-Shifting Network Telemetry
## 5 Attention-Switching Strategy
### Implementation
Set acceleration factors: Higher values of \(a_{1}\) and \(a_{2}\) can effectively accelerate the algorithm's operation, as demonstrated in the following experiments. Figure 8: Basic performance of three methods in different sizes of spine-leaf topology. (**a**) Bandwidth consumption. (**b**) Normalized bandwidth consumption. **Performance Across Different Anomaly Rate**: The anomaly rate in the experiment is a predefined value that is influenced by two factors: (i.) Conditions for identifying suspicious links in the current cycle: If we use strict criteria for identification, the anomaly rate will be higher; if the criteria are more lenient, the anomaly rate will be lower. (ii.) Network stability or rapid changes: A network that changes slowly tends to have a lower anomaly rate. As illustrated in Figure 9, regardless of the anomaly rate, FANT-H consumes the least bandwidth. As the anomaly rate increases from 0% to 30%, the bandwidth consumed by FANT-G and FANT-H increases from 1.3 MBPs to 2 MBPs and from 1.2 MBPs to 6 MBPs, respectively. The growth rate of bandwidth usage in FANT-H is slower than that of FANT-G. The increase in bandwidth usage by FANT as the anomaly rate rises is due to the need for more detailed measurements at higher numbers of anomaly points, which diminishes the bandwidth-saving effect of FANT. At an anomaly rate below 10%, the bandwidth consumed by FANT grows rapidly. However, as the anomaly rate exceeds 10%, the rate of bandwidth increase gradually slows down. This trend occurs because it is challenging for a single probe to cover multiple suspicious links when there are few random anomalies. However, as the number of anomalies increases, it becomes easier for a probe to cover multiple links. #### 6.4.3 Impact of Parameters

In this section, we explore the impact of various parameters on algorithm performance and execution time. To more clearly demonstrate these effects, we set the anomaly rate at 10%, which is higher than in previous experiments. Figure 9: Attention-shifting strategy performance of different abnormal rates. (**a**) Bandwidth consumption. (**b**) Normalized bandwidth consumption. **Time Consumption with Different \(a_{1}\) and \(a_{2}\)**: In previous experiments, we observe that the algorithm's execution time exceeds the measurement cycle interval. This means that the INT controller cannot set correct INT instructions for the probe generator/collector before the next measurement cycle. Consequently, in the actual FANT system, we accelerate measurements by setting acceleration factors. As illustrated in Figure 10, when (\(a_{1}\) = 0), the execution time is 85.7 s; when (\(a_{1}\) = 0.9), the execution time is 0.22 ms. The results indicate that increasing \(a_{1}\) can effectively accelerate the algorithm, enhancing efficiency by \(3.85*10^{5}\times\). Similarly, when we set \(a_{1}\) to 0 and adjust \(a_{2}\), we find that with (\(a_{2}\) = 0), the execution time remains 85.7 s; when (\(a_{2}\) = 0.15), the execution time changes to 0.69 ms. The results demonstrate that increasing \(a_{2}\) also significantly accelerates the algorithm, with an efficiency increase of \(1.24*10^{5}\times\). We also note that when \(a_{1}\) and \(a_{2}\) are already high, further increases in \(a_{1}\) and \(a_{2}\) result in diminishing reductions in execution time. Additionally, for the same configuration parameters, larger topologies do not always result in longer execution times. This is primarily because the FANT takes probe traces as input. And the probe traces generated by INT-Probes may vary in different sizes and thus influence the execution time more than the topology size itself. Therefore, we recommend that users conduct tests with the same topology to determine the optimal configuration of acceleration factors when using FANT. Figure 10: Execution time of FANT-H across different \(a_{1}\) and \(a_{2}\) settings. (**a**) Execution time across various \(a_{1}\) settings. (**b**) Execution time across various \(a_{2}\) settings.