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• W3134353086 endingPage "153615" @default.
• W3134353086 startingPage "153615" @default.
• W3134353086 abstract "The rapidly increasing data demand from current Internet services, such as cloud computing and high-quality video streaming, is raising the pressure on network operators to provide reliable high-speed connections while keeping costs low. Without having to rely on the bandwidth expansion of optical waveguides or modulation level efficiency, one of the ways to increase spectral efficiency of a network is by the optimal utilization of already existing resources. For elastic optical networks (EON) this can be solved by the Routing, Modulation and Spectrum Assignment (RMSA). However, another problem arises, due to the nature of computer network traffic, that makes it difficult to assess the longevity of physical components and to plan a network to serve a maximum amount of bandwidth demand for predetermined time span. To help alleviating these problems, this paper presents an investigation of a novel technique for resource planning and consume estimation based on simulations. First, traffic matrix prediction is made using recurrent neural networks (RNN), and then simulations are ran to estimate the consumed bandwidth for a point in time. For the traffic forecasting, real traffic data has been used to train the RNN, obtained from an anonymous dataset containing the traffic history of the ABILENE and GEANT networks from the years 2004–2005, which is a relatively recent available dataset. Furthermore, heuristic algorithms are proposed to apply the RMSA to the predicted traffic, in a quasi-optimal manner, in order to minimize the use of network resources for future traffic, by releasing available capacity in the existing optical fiber links in an incremental traffic approach. The results show that the proposed RNN models are indeed capable of predicting the traffic matrices a month ahead, with low mean squared error. RMSA simulations were also performed using heuristics in order to estimate the consumed bandwidth in the context of EON networks, revealing that, for the proposed heuristics, the results favor the shortest path routing heuristic over the load balancing heuristic for a long-term bandwidth open capacity maximization and better resource savings." @default.
• W3134353086 created "2021-03-15" @default.
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• W3134353086 date "2021-05-01" @default.
• W3134353086 modified "2023-10-14" @default.
• W3134353086 title "Resource planning on elastic optical networks using traffic matrix prediction" @default.
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• W3134353086 doi "https://doi.org/10.1016/j.aeue.2021.153615" @default.
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