| 1. |
- ter Steege, Hans, et al.
(författare)
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Mapping density, diversity and species-richness of the Amazon tree flora
- 2023
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Ingår i: COMMUNICATIONS BIOLOGY. - 2399-3642. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution. A study mapping the tree species richness in Amazonian forests shows that soil type exerts a strong effect on species richness, probably caused by the areas of these forest types. Cumulative water deficit, tree density and temperature seasonality affect species richness at a regional scale.
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| 2. |
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| 3. |
- Etezadi, Ehsan, 1993, et al.
(författare)
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Deep reinforcement learning for proactive spectrum defragmentation in elastic optical networks [Invited]
- 2023
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Ingår i: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 15:10, s. E86-E96
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Tidskriftsartikel (refereegranskat)abstract
- The immense growth of Internet traffic calls for advanced techniques to enable the dynamic operation of optical networks, efficient use of spectral resources, and automation. In this paper, we investigate the proactive spectrum defragmentation (SD ) problem in elastic optical networks and propose a novel deep reinforcement learning-based framework DeepDefrag to increase spectral usage efficiency. Unlike the conventional, often threshold-based heuristic algorithms that address a subset of the defragmentation related tasks and have limited automation capabilities, DeepDefrag jointly addresses the three main aspects of the SD process: determining when to perform defragmentation, which connections to reconfigure, and which part of the spectrum to reallocate them to. By considering services attributes, spectrum occupancy state expressed by several different fragmentation metrics, as well as reconfiguration cost, DeepDefragmis able to consistently select appropriate reconfiguration actions over the network lifetime and adapt to changing conditions. Extensive simulation results reveal superior performance of the proposed scheme over a scenario with exhaustive defragmentation and a well-known benchmark heuristic from the literature, achieving lower blocking probability at a smaller defragmentation overhead.
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| 4. |
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| 5. |
- Etezadi, Ehsan, 1993, et al.
(författare)
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Proactive Spectrum Defragmentation Leveraging Spectrum Occupancy State Information
- 2023
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Ingår i: International Conference on Transparent Optical Networks. - 2162-7339. ; 2023-July
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Konferensbidrag (refereegranskat)abstract
- One of the main obstacles to efficient resource usage under dynamic traffic in elastic optical networks (EONs) is spectrum fragmentation (SF), leading to blocking of incoming service requests. Proactive spectrum defragmentation (SD) approaches periodically reallocate services to ensure better alignment of available spectrum slots across different links and alleviate blocking. The services for reallocation are commonly selected based on their properties, e.g., age, without detailed consideration of prior or posterior spectrum occupancy states. In this paper, we propose a heuristic algorithm for proactive SD that considers different spectrum fragmentation metrics to select services for reallocation. We analyze the relationship between these metrics and the resulting service blocking probability. Simulation results show that the proposed heuristic outperforms the benchmarking proactive SD algorithms from the literature in reducing blocking probability.
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| 6. |
- Famelis, Panagiotis, et al.
(författare)
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P5: Event-driven Policy Framework for P4-based Traffic Engineering
- 2023
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Ingår i: Proceedings of the 24th International Conference on High Performance Switching and Routing. - 9781665476409
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Konferensbidrag (refereegranskat)abstract
- We present P5, an event-driven policy framework that allows network operators to realize end-to-end policies on top of P4-based data planes in an intuitive and effective manner. We demonstrate how P5 adheres to a service-level agreement (SLA) by applying P4-based traffic engineering with latency constraints.
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| 7. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
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A Flexible and Scalable ML-Based Diagnosis Module for Optical Networks: A Security Use Case
- 2023
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Ingår i: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 15:8, s. C155-C165
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Tidskriftsartikel (refereegranskat)abstract
- To support the pervasive digital evolution, optical network infrastructures must be able to quickly and effectively adapt to the changes arising from traffic dynamicity or external factors such as faults and attacks. Network automation is crucial for enabling dynamic, scalable, resource-efficient, and trustworthy network operations. Novel telemetry solutions enable optical network management systems to obtain fine-grained monitoring data from devices and channels as the first step towards the near-real-time diagnosis of anomalies such as security threats and soft failures. However, the collection of large amounts of data creates a scalability challenge related to processing the data within the desired monitoring cycle regardless of the number of optical services being analyzed. This paper proposes a module that leverages the cloud native software deployment approach to achieve near-real-time \ac{ML}-assisted diagnosis of optical channels. The results obtained over an emulated physical-layer security scenario demonstrate that the architecture successfully scales the necessary components according to the computational load, and consistently achieves the desired monitoring cycle duration over a varying number of monitored optical channels.
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| 8. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
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Machine-Learning-as-a-Service for Optical Network Automation
- 2023
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Ingår i: Optical Fiber Communication Conference and Exhibition. - 9781957171180
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Konferensbidrag (refereegranskat)abstract
- MLaaS is introduced in the context of optical networks, and an architecture to take advantage of its potential is proposed. A use case of QoT classification using MLaaS techniques is benchmarked against state-of-the-art methods.
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| 9. |
- Natalino Da Silva, Carlos, 1987
(författare)
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Optical Network Automation and Programmability for 6G: State-of-the-Art, Vision, and Challenges
- 2023
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The current 6G vision foresees a massive increase in connected devices and more widespread adoption of local/distributed intelligence. To support this paradigm shift, optical networks will need to operate in a more dynamic and flexible fashion, and the control and management will need to be highly automated, programmable, and scalable. In this tutorial, we will analyze which of the 6G requirements can be supported by network automation and programmability, and what are the current developments in these areas. We will conclude by discussing the challenges that need to be addressed in the near future.
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| 10. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
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Scalable and Efficient Pipeline for ML-based Optical Network Monitoring
- 2023
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Ingår i: 2023 Optical Fiber Communications Conference and Exhibition, OFC 2023 - Proceedings.
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Konferensbidrag (refereegranskat)abstract
- We demonstrate a scalable processing of OPM data using ML to detect anomalies in optical services at run time. A dashboard will show operational SDN controller metrics, raw OPM data, and the ML assessment results.
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