## 7 Experimental Evaluation
### Evaluation with Other Workloads  
To demonstrate the generalizability of our proposed solution, we repeated our experimental evaluation with open-source workload traces provided by Google's Thes project [74] and available in the SNIA IOTTA repository [75]. The The Thesions dataset contains I/O traces from January to March 2024 from three clusters of storage servers in Google's distributed storage system with different types of workload traffic. Table 7 shows some key average statistics per trace per cluster in the Thesios dataset, revealing that each trace contains tens of thousands of read I/O requests, processing 20-42 GB of data. For these experiments, we evaluated the four streaming ML models against the rule-based approaches (recall Section 7.3) as well as the three scenarios of the batch-based ML approaches with bucket size 5000 (recall Section 7.2). Figure 17a shows the F1 score for _Next Offset Class_ for the three clusters from the Thesios dataset. The rule-based approach yields the lowest performance, with only a 59% F1 score on average across the clusters due to the complex non-sequential access patterns present in the Thesios workload. The batch-based ML approaches achieve better performances of around 81% F1 score, with the 3 rd scenario (incremental retraining) offering marginal benefits (of around 3%) over the other two scenarios. Finally, the streaming ML approach consistently achieves the highest F1 score of up to 93% across all traces and clusters, showcasing the strong predictive power and adaptability of the streaming models. The results are very similar for the regression models, as illustrated in Figure 17b, which shows MAE for the _Next File Hotness_ case for the Thesios dataset. Once again, the rule-based method leads to the lowest performance \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Statistic** & **Cluster 1** & **Cluster 2** & **Cluster 3** \\ \hline \# Applications & 95 & 193 & 278 \\ \# Read Requests & 60,098 & 42,513 & 54,542 \\ Data Read (GB) & 25 & 37 & 30 \\ \# Write Requests & 85,923 & 47,791 & 110,099 \\ Data Written (GB) & 16 & 16 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average statistics per trace per cluster in the Thesios dataset. Figure 16: The (**a**) Mean Absolute Error (MAE), (**b**) model size, and (**c**) runtime for the _Next Offset_ case using all the training instances from the 17 traces consecutively. The red points show when a trace ends and the next one begins. (i.e., highest MAE), followed by the three batch-based ML approaches, while streaming ML achieves the best performance. Figure 18 shows a more in-depth view of how the F1 score changes over time for _Next Offset Class_ for a representative trace from Cluster 1 of the Thesios dataset. For most of the trace, the rule-based approach shows a steady but low performance of around 62% F1 score because it can only detect specific sequential access patterns. The 1 st batch scenario (trained once) performs well at the beginning (87% F1 score) for some time, but its performance gradually declines to 66% F1 score because new access patterns appear in the trace that the model had not seen before. Periodically retraining the model (2 nd scenario) reverses the declining trend and manages to achieve up to 83% F1 score toward the end of the trace. Incrementally retraining the model (3 rd scenario) yields steady performance with 86% F1 score but at the expense of collecting training data and performing costly retraining cycles. Once again, streaming ML yields the most robust and highest performance of 92% F1 score throughout most of the trace, even when new access patterns appear over time. The results for the runtime and memory comparison of the approaches are very similar to those presented in the previous sections using the Huawei production traces and, hence, are omitted to avoid repetition.