| 1. |
- Coelho, Frederico, et al.
(författare)
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Forecasting power load curves from spatial and temporal mobile data
- 2020
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Ingår i: World Review of Science, Technology and Sustainable Development. - 1741-2234 .- 1741-2242. ; 16:1, s. 4-21
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Tidskriftsartikel (refereegranskat)abstract
- This work aims at applying computational intelligence approaches to telecommunication data, in order to associate mobile data to energy consumption load curves. Clustering methods are applied in order to allow the telecommunication network to infer about its topology and consumption load forecasting. Through an extensive analysis of Telecom Italia dataset and power distribution lines data available for the city of Trento, it was possible to confirm the high correlation between them, mainly when voice data is considered. To a great extent, this correlation can be explained by the fact that cellular communication devices are physically present in the service area of the distribution lines and when people are communicating, they are also consuming energy. Based on the aforementioned dataset, load curves for the city of Trento were constructed having as inputs data from telecommunication transactions. Results show that it is possible to use the telecommunication load as the input to predict the energy load, with the proposed model performing better than the naive predictor in 82% of the tested distribution lines.
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| 2. |
- Natalino, Carlos, Dr. 1987-, et al.
(författare)
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A Proactive Restoration Strategy for Optical Cloud Networks Based on Failure Predictions
- 2018
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Ingår i: 20th International Conference on Transparent Optical Networks, ICTON 2018. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538666050
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Konferensbidrag (refereegranskat)abstract
- Failure prediction based on the anomaly detection/forecasting is becoming a reality thanks to the introduction of machine learning techniques. The orchestration layer can leverage on this new feature to proactively reconfigure cloud services that might find themselves traversing an element that is about to fail. As a result, the number of cloud service interruptions can be reduced with beneficial effects in terms of cloud service availability. Based on the above intuition, this paper presents an orchestration strategy for optical cloud networks able to reconfigure vulnerable cloud services (i.e., the ones that would be disrupted if a predicted failure happens) before an actual failure takes place. Simulation results demonstrate that, with a single link failure scenario, proactive restoration can lead to up to 97% less cloud services having to be relocated. This result brings considerable benefits in terms of cloud service availability, especially in low load conditions. It is also shown that these improvements come with almost no increase in the cloud service blocking probability performance,i.e., resource efficiency is not impacted.
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