# Text Semantics-Driven Data Classification Storage Optimization
## 5 Experiments and Evaluation
### Results and Discussions
#### 5.2.1 Comparison across Multiple Years  
Energy ConsumptionIn this comprehensive analysis, we delve into the nuanced landscape of energy consumption across various data classification strategies. The experimental workloads are meticulously configured for the years 2018, 2019, and 2020, providing a robust foundation for evaluating the strategies' performance over time. Figure 4 elucidates the dynamic trends in energy consumption associated with different data classification methods.  
Figure 4 unveils that the semantic similarity method outperforms other classification strategies regarding energy consumption. Across the time quantum from January 2018 to December 2020, compared with AS-H and K-ear, the semantic similarity method decreases energy consumption by 8.33-12.24%, 7.35-11.42%, and 9.51-13.35%, respectively. Furthermore, we can observe that the semantic similarity method not only outperforms its counterparts but also showcases a resilient and enduring advantage over three consecutive years. This sustained superiority highlights the robustness and stability of the semantic similarity approach in minimizing energy consumption in data classification tasks. Compared with the method that relies solely on access frequency for classification, this is mainly attributed to the advantages of the text semantic method in extracting semantics, because there is a large amount of semantic data text with seasonal features and access frequency  
without seasonal feature. Text semantics have greater dimensional information in reflecting seasonal characteristics than access frequency.  
Operating CostIn our comprehensive analysis, we delve into the operating costs associated with diverse data classification approaches from 2018 to 2020. The operating costs for each classification strategy are visually presented in Figure 5.  
Figure 5 illuminates the operating cost of the semantic similarity method, which consistently exhibits a lower operating cost compared to alternative strategies. Specifically, when juxtaposed with the AS-H and K-ear classification strategies, the semantic similarity method showcases substantial operating cost savings, ranging from 13.19% to 22.00% in 2018, 13.20% to 22.17% in 2019, and 13.20% to 22.05% in 2020. This notable cost advantage underscores the economic viability and efficiency of the semantic similarity method, positioning it as a choice for seeking to optimize the operational cost of data processing and storage. The sustained and substantial cost savings over the three years further accentuate the method's reliability and cost-effectiveness in real-world applications. This is mainly attributed to the improvement in the accuracy of seasonal feature recognition using text-semantic methods. The improvement in accuracy allows limited data to be placed on high-performance disks. This also means that more data are placed on low-cost disks, reducing the entire storage system's cost.  
Figure 4: Annual total energy consumption of different methods (2018–2020). We use the energy consumption module in CloudSimDisk to compare the energy consumption of all approaches by replaying the workloads. Compared to AH-S and K-ear, the semantic similarity method reduces energy consumption by up to 9.51–13.35%.