Towards power consumption in optical networks: a cognitive prediction-based technique

Panagiotis G. Sarigiannidis, Georgios Papadimitriou, Petros Nicopolitidis, Emmanouel Varvarigos

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)


Summary: Modern backbone, optical networks have developed into large, massive networks, which consist of numerous intermediate and terminal nodes as well as final users. These networks serve uninterruptedly multitude of users at the expense of considerable power consumption. Many research efforts are aimed at reducing energy consumption in large-scale optical networks; however, this objective is deemed laborious: the operation of such networks has to continuously remain in good levels without disruptions. One of the most compelling techniques to remedy this situation is to switch off redundant links and devices at specific (short) periods. These links and devices remain idle as long as the network can cope with the underlying traffic demands. Hence, a power mechanism is required to manage how and when underutilized network elements may be silent during network operation. Nevertheless, this management entails fast processing and efficient decision making. While many research efforts neglect this serious factor, the problem of reducing the power consumption still threats the development of today's backbone network. In this work, an effective, cognitive power management technique is proposed by enhancing the decision making with traffic prediction. Traffic capacity is estimated in each link within the network supporting, thus, more efficient decisions on switching off underutilized or even idle network elements a priori. The technique introduced succeeds high accuracy levels, while it offers energy savings up to 30% lower than other energy-aware schemes.

Original languageEnglish
Article numbere2981
Number of pages23
JournalInternational Journal of Communication Systems
Issue number7
Publication statusPublished - 10 May 2017
Externally publishedYes


  • Backbone networks
  • Energy efficiency
  • Optical networks
  • Power consumption
  • Prediction

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