Traffic and cloud resource prediction methodologies have been recently used in Network Function Virtualization environment for cloud and bandwidth resource allocation purposes. Both traditional and innovative prediction methodologies have been proposed for the application of allocation procedures. For instance Long Short Term Memory-based prediction techniques have been shown to be very effectiveness to allocate the resources. All of these techniques are based on the minimization of a symmetric cost function as the Root Mean Square Error that equally weights positive and negative prediction errors. However the error sign can differently impact the cost increase due to prediction errors. For instance when the Quality of Service degradation cost due to traffic loss is prevalent with respect to the cloud resource allocation cost, an algorithm is preferable that overestimates the offered traffic; conversely the traffic underestimation is preferable in the opposite case when the cloud allocation cost is higher than the QoS degradation one. For this reason we propose an Asymmetric LSTM traffic prediction procedure in which the cost function is defined so as to take into account both the QoS degradation and cloud resource allocation costs. In a typical network and traffic scenario, we show how the proposed solution allows for cost decrease by 40% with respect to classical LSTM prediction methodology based on the Root Mean Square Error.

流量和云资源预测方法已在网络功能虚拟化环境中最近用于云和带宽资源分配目的。对于分配程序的应用，已经提出了传统的和创新的预测方法。例如，基于长期短期记忆的预测技术已显示出非常有效的资源分配方式。所有这些技术都基于最小化对称成本函数（即均方根误差），该均方根权重正负预测误差。然而，由于预测误差，误差符号会不同地影响成本增加。例如，当因流量损耗而导致的服务质量降低成本相对于云资源分配成本而言非常普遍时，最好使用一种算法来高估提供的流量；相反，在云分配成本高于QoS降级成本的情况下，流量低估是优选的。因此，我们提出了一种非对称LSTM流量预测程序，其中定义了成本函数，以便同时考虑QoS降级和云资源分配成本。在典型的网络和流量场景中，我们展示了相对于基于均方根误差的经典LSTM预测方法，所提出的解决方案如何使成本降低40％。因此，我们提出了一种非对称LSTM流量预测程序，其中定义了成本函数，以便同时考虑QoS降级和云资源分配成本。在典型的网络和流量场景中，我们展示了相对于基于均方根误差的经典LSTM预测方法，所提出的解决方案如何使成本降低40％。因此，我们提出了一种非对称LSTM流量预测程序，其中定义了成本函数，以便同时考虑QoS降级和云资源分配成本。在典型的网络和流量场景中，我们展示了相对于基于均方根误差的经典LSTM预测方法，所提出的解决方案如何使成本降低40％。