The evolving nature of network traffic challenges existing learning-based models, as frequently re-training the hyper-parameters is required to adaptively learn and predict its behavior. Benefiting from discovering the sparsity and self-similarity of network traffic, it becomes possible to develop compact algorithms with high accuracy and low computational complexity. However, how to properly make use of these properties still remains to be explored. In this article, we establish a light-weight learning framework for network traffic prediction based on sparse representation, and try to take the full advantage of these properties to enhance the capability of tracking its highly evolving characteristics. Specifically, 1). The strict causality constraint makes it difficult to equip the conventional sparse representation with predictive capability. To solve this issue, we make use of the self-similarity and train the representative/predictive dictionaries in a joint manner, such that the query point is embedded in terms of a sparse combination of dictionary atoms, and jointly coded with its T + 1 time slot behind counterpart, which proves to be optimal in the concatenated representative/predictive feature space. Then, the query point can be estimated through iterative projection method. 2). The consideration of the sparsity constraint loosens the upper bound of the time averaged prediction error. To address this problem, we slightly modify the sparse representation-based prediction by adopting Lyapunov optimization, and try to minimize the time averaged prediction error. Finally, the simulation results verify the performance improvements.

中文翻译：

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通过轻量级机器学习预测网络流量


网络流量的不断发展的性质对现有的基于学习的模型提出了挑战，因为需要频繁地重新训练超参数才能自适应地学习和预测其行为。受益于发现网络流量的稀疏性和自相似性，开发具有高精度和低计算复杂度的紧凑算法成为可能。然而，如何正确利用这些特性仍有待探索。在本文中，我们建立了一个基于稀疏表示的轻量级网络流量预测学习框架，并试图充分利用这些特性来增强跟踪其高度演化特征的能力。具体来说，1）。严格的因果关系约束使得传统的稀疏表示很难具备预测能力。为了解决这个问题，我们利用自相似性，以联合的方式训练代表/预测字典，使得查询点以字典原子的稀疏组合嵌入，并与其T + 1联合编码对应的时隙，这被证明在串联的代表性/预测特征空间中是最佳的。然后，可以通过迭代投影方法来估计查询点。 2）。稀疏约束的考虑放宽了时间平均预测误差的上限。为了解决这个问题，我们通过采用李亚普诺夫优化稍微修改基于稀疏表示的预测，并尝试最小化时间平均预测误差。最后，仿真结果验证了性能的改进。