# FANT: Flexible Attention-Shifting Network Telemetry
## 5 Attention-Switching Strategy
### Implementation
**Performance with Different \(a_{1}\) and \(a_{2}\)**: By configuring acceleration factors, we have successfully accelerated the algorithm's operation. However, when the acceleration factor is nonzero, the algorithm cannot guarantee achieving the theoretical optimum for two reasons: (i.) The estimation function may not ensure values are less than the actual values, leading to excessive pruning and premature termination of the search for the optimal solution. (ii.) The allowable cost is updated to \(Max(T_{min},T+1)\) rather than \(T+1\) during the search and thus might cause an excessive increase in allowable costs, leading to the output of non-theoretical optimal solutions. As shown in Figure 11a,b, when \(a_{1}\) is 0.1 and \(a_{2}\) is 0.07, the algorithm still maintains the theoretical optimum. However, as \(a_{1}\) and \(a_{2}\) gradually increase, the algorithm begins to deviate from this optimum. This deviation becomes more pronounced in larger topologies. That is because a larger topology has a large search tree, thereby increasing the likelihood of the algorithm finding non-optimal solutions when cost limits are excessively updated. Nevertheless, these non-optimal solutions are still sufficiently effective. Even when \(a_{1}\) and \(a_{2}\) are set to higher values, compared to the theoretical optimum, their bandwidth only increases by 20.1% (\(a_{1}\) = 0.9, bandwidth usage is 3.4 Mbps) and 6.4% (\(a_{2}\) = 0.15, bandwidth usage is 3.0 Mbps). Figure 11: Bandwidth usage of FANT-H across different \(a_{1}\) and \(a_{2}\) settings. (**a**) Performance across various \(a_{1}\) settings. (**b**) Performance across various \(a_{2}\) settings. By analyzing Figures 10 and 11 together, we conclude that configuring \(a_{1}\) and \(a_{2}\) enables us to achieve a speed improvement of over \(10^{5}\times\). However, this comes at the cost of a 6.4% reduction in bandwidth-savings ability. Based on all the experimental results presented in this section, we can conclude that compared to state-of-the-art solutions, the FANT system:

1. Achieves measurement accuracy that is nearly equivalent. 2. Significantly reduces overhead, particularly in stable network conditions, where it can save up to 65%, and even in highly unstable network conditions, it still manages to save 30%. 3. The reduction in overhead with the FANT system leads to networks using this system having more available bandwidth, thereby increasing the throughput of users' traffic. ## 7 Limitations and Future Work

### Limitations

* FANT is tailored for any AINT-based full-network measurement system, effectively mitigating the increased overhead as metrics rise. However, FANT is not suitable for PINT-based full-network measurement systems. * FANT relies on probe generators/collectors to detect anomalous metrics based on local information. This approach, which does not use global information, requires a cautious strategy to avoid missing measurements. As a result, more metrics that are not actually anomalous might be incorrectly identified as suspicious, thereby increasing the system's overhead. ### Future Work

FANT optimizes probe-switching decisions in each cycle. Nonetheless, during the probe-switching process, the controller must issue switching instructions to multiple probe generators/collectors every cycle. Consequently, (i.) as the network size expands, the number of probe generators/collectors increases, causing the control costs to rise; (ii.) similarly, when the cycle frequency is high, the control costs also increase. Thus, we plan to evolve the existing model from a weighted set cover problem focused solely on measurement costs to a dynamic weighted set cover problem that balances both control and measurement costs in the future. ## 8 Conclusions

In this paper, we introduce FANT, an AINT monitoring system that employs an attention-shifting strategy to reduce overhead as metrics increase. This reduction is achieved by concentrating more on links that were suspicious in the previous measurement cycle and less on those that performed normally.