This paper focuses on the problem of time‐efficient traffic prediction. The prediction enables the proactive and globally scoped optimisation in software‐defined networks (SDNs). We propose the shrinkage and selection heuristic method for the trigonometric Fourier‐based traffic models in SDNs. The proposed solution allows us to optimise the network for an upcoming time window by installing flow entries in SDN nodes before the first packet of a new flow arrives. As the mechanism is designed to be a part of a sophisticated routing‐support system, several critical constraints are considered and taken into account. Specifically, the system is traffic‐ and topology‐agnostic, thus the prediction mechanism must be applicable to the networks with highly variable traffic loads (e.g., observed inside intra‐DCNs: datacentre networks). Furthermore, the system must effectively optimise routing in large‐scale SDNs comprised of numerous nodes and handling millions of flows of a dynamic nature. Therefore, the prediction must be simultaneously accurate as well as being time efficient and scalable. These requirements are met by our Fourier‐based solution, which subtracts consecutive harmonics from the original signal and compares the result with an adaptive threshold adjusted to the signal's standard deviation. The evaluation is performed by comparing the proposed heuristic with the well‐known Lasso method of proven accuracy. The results show that our solution is able to retain prediction accuracy at a comparable level. Moreover, in accordance with our main aim, we operate in a manner which is always significantly faster. In some cases, computation times are reduced by as much as 50 times.

中文翻译：

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一种基于傅里叶预测的省时收缩算法，可在软件定义的网络中进行主动优化

本文关注于时间高效交通预测的问题。该预测可以在软件定义的网络（SDN）中进行主动的全局范围内的优化。我们为SDN中基于三角傅立叶的流量模型提出了收缩和选择启发式方法。所提出的解决方案允许我们通过在新流的第一个数据包到达之前在SDN节点中安装流条目来为即将到来的时间窗口优化网络。由于该机制被设计为复杂的路由支持系统的一部分，因此考虑并考虑了一些关键约束。具体而言，该系统与流量和拓扑无关，因此，预测机制必须适用于流量负载高度可变的网络（例如，在DCN内部：数据中心网络内部观察到）。此外，该系统必须有效地优化大型SDN中的路由，该SDN包含多个节点并处理数百万个动态流。因此，预测必须同时准确，时间高效且可扩展。我们基于傅立叶的解决方案满足了这些要求，该解决方案从原始信号中减去连续的谐波，并将结果与​​调整到信号标准偏差的自适应阈值进行比较。通过将提议的启发式方法与公认的准确性的套索方法进行比较来进行评估。结果表明，我们的解决方案能够将预测精度保持在可比的水平。而且，根据我们的主要目标，我们以始终明显更快的方式运行。在某些情况下，计算时间最多减少50倍。