The paper studies efficient modeling and prediction of daily traffic patterns in transport telecommunication networks. The investigation is carried out using two historical datasets, namely WASK and SIX, which collect flows from edge nodes of two networks of different size. WASK is a novel dataset introduced and analyzed for the first time in this paper, while SIX is a well-known source of network flows. For the considered datasets, the paper proposes traffic modeling and prediction methods. For traffic modeling, the Fourier Transform is applied. For traffic prediction, two approaches are proposed—modeling-based (the forecasting model is generated based on historical traffic models) and machine learning-based (network traffic is handled as a data stream where chunk-based regression methods are applied for forecasting). Then, extensive simulations are performed to verify efficiency of the approaches and their comparison. The proposed modeling method revealed high efficiency especially for the SIX dataset, where the average error was lower than 0.1%. The efficiency of two forecasting approaches differs with datasets–modeling-based methods achieved lower errors for SIX while machine learning-based for WASK. The average prediction error for SIX reached 3.36% while forecasting for WASK turned out extremely challenging.

The overall network volume in backbone optical networks is constantly growing, composed of many smaller network services with increasing trends and various seasonality. Due to the recent advances in machine learning algorithms, short- and long-term traffic fluctuations can be forecasted. Consequently, the backbone optical network can be adapted to traffic changes aiming to improve its performance. However, an important challenge lies in developing effective optimization methods capable of adapting to traffic changes to leverage the knowledge about the traffic. To this end, this paper addresses the time-varying traffic in spectrally-spatially flexible optical networks (SS-FONs), which are a promising technology to mitigate backbone network requirements of vast traffic volume transmission. The main contribution of this paper is twofold. Firstly, we introduce a new traffic prediction method using multioutput regression and temporal features to forecast traffic between all node pairs and integrate this prediction method into an optimization framework developed for dynamic resource allocation in translucent SS-FONs with time-varying traffic. Secondly, we evaluate potential network performance improvements from periodic lightpath reallocation through extensive numerical experiments. According to the results of experiments run on two representative optical network topologies, the proposed approach with periodic resource allocation allows achieving up to 7.8 percentage points reduction of bandwidth blocking compared to the reference scenario without reallocation. Consequently, the network requires up to 23.4% fewer transceivers to deliver the same traffic in considered scenarios and thus the power consumption savings are provided.

Knowledge about the future link load is an essential issue for optical network operators, especially in the case of network failure and the restoration of affected traffic. In this paper, we study the dynamic operation of an optical network in its normal non-failure state and the situation of a link failure followed by the restoration process. Data obtained during the simulation are used for link load prediction. We propose dynamic methods for multistep link load prediction. The analyzed link load is expressed in two ways: the bitrate of the overall traffic allocated to the link and the number of frequency slots occupied. The proposed prediction methods are expanded to include additional mechanisms that improve the forecasting quality expressed by the mean absolute percentage error metric. We evaluate developed methods on a dataset collected using a representative European network topology with realistic traffic containing diverse types of network transmissions. In broad numerical experiments, we prove the high prediction quality of regression algorithms aided by the proposed additional features.

Traffic in current networks is constantly increasing due to the growing popularity of various network services. The currently deployed backbone optical networks apply wavelength division multiplexing (WDM) techniques in single-core single-mode fibers (SMFs) to transmit the light. However, the capacity of SMFs is limited due to physical constraints, and new technologies are required in the near future. Spectrally–spatially-flexible optical networks (SS-FONs) are proposed to provide a substantial capacity increase by exploring the spatial dimension. However, before this technology will reach maturity, various aspects need to be addressed. In particular, during traffic grooming, multiple small requests are aggregated into large-capacity optical corridors in an optical layer to increase the spectral efficiency. As the summary traffic volume is dynamically changing, it may be required to set up and tear down optical channels, which results in network fragmentation. As a consequence, in a congested network, part of the requests can be blocked due to the lack of spectrum resources. Thus, the grooming of traffic and the creation of lightpaths should be carefully designed to minimize network fragmentation. In this study, we present several fragmentation metrics and develop a fragmentation-aware traffic grooming algorithm that reduces the bandwidth blocking probability.