The advent of 5G technology promises a paradigm shift in the realm of
telecommunications, offering unprecedented speeds and connectivity. However,
the efficient management of traffic in 5G networks remains a critical
challenge. It is due to the dynamic and heterogeneous nature of network
traffic, varying user behaviors, extended network size, and diverse
applications, all of which demand highly accurate and adaptable prediction
models to optimize network resource allocation and management. This paper
investigates the efficacy of live prediction algorithms for forecasting
cellular network traffic in real-time scenarios. We apply two live prediction
algorithms on machine learning models, one of which is recently proposed Fast
LiveStream Prediction (FLSP) algorithm. We examine the performance of these
algorithms under two distinct data gathering methodologies: synchronous, where
all network cells report statistics simultaneously, and asynchronous, where
reporting occurs across consecutive time slots. Our study delves into the
impact of these gathering scenarios on the predictive performance of traffic
models. Our study reveals that the FLSP algorithm can halve the required
bandwidth for asynchronous data reporting compared to conventional online
prediction algorithms, while simultaneously enhancing prediction accuracy and
reducing processing load. Additionally, we conduct a thorough analysis of
algorithmic complexity and memory requirements across various machine learning
models. Through empirical evaluation, we provide insights into the trade-offs
inherent in different prediction strategies, offering valuable guidance for
network optimization and resource allocation in dynamic environments.

我们研究了实时场景下应用于移动网络流量预测的两种实时预测算法的有效性，发现 Fast LiveStream Prediction（FLSP）算法相对于传统的在线预测算法，在异步数据汇报情况下，能够减少所需带宽一半，并提高预测准确性和降低处理负载。我们还通过对各种机器学习模型的算法复杂度和内存需求进行了细致分析，从而提供有关不同预测策略的权衡，为动态环境下的网络优化和资源分配提供了有价值的指导。