AS-RAM: Automated Search with Rich Attention Modules for Network Traffic Prediction

By effectively forecasting network traffic patterns, future cellular networks have the potential to manage themselves and incorporate intelligent, streamlined operations autonomously. Existing methods have relied on handcrafted architectures to predict network traffic, which still have two main limitations. First, human-designed models may have sub-optimality in modeling. Second, existing handcrafted designs did not take spatio-temporal long-term dependencies into account explicitly even though they play crucial roles in network traffic prediction. To address these issues, we propose a method using NAS (Neural Architecture Search) to find more optimized architectures than handcrafted ones for network traffic prediction. Specifically, we introduce a new search space that integrates attention modules enabling better extraction and reflection of the unique characteristics of network traffic data. These attention modules are seamlessly incorporated into the architecture from an optimization perspective. To verify the effectiveness of the proposed method, we validate the performance of the proposed framework using the Italia Telecom dataset. Experimental results show that our framework significantly improves the performance compared to the baseline in terms of root mean squared error between predicted value and ground-truth value. Specifically, our method improves the performance by 9.5%, 9.8%, and 10.2% on SMS, CALL, and INTERNET traffic datasets, respectively.