| 1. |
- Kalsson, Stefan, et al.
(författare)
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Eavesdropping G.652 vs. G.657 fibres: a performance comparison
- 2022
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Ingår i: 2022 International Conference on Optical Network Design and Modeling, ONDM 2022.
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Konferensbidrag (refereegranskat)abstract
- With increasing dependence on secure access to digital services and the ultra-high traffic volumes running on the optical fibre communication infrastructure, the protection of this infrastructure from eavesdropping is extremely important, especially in defense and military applications. The G.657 fibre is recommended to be deployed in in-building installations for its improved bending performance compared to the G.652 fibre. However, the easiness to be eavesdropped, which reflects the security level of those two types of fibres has not yet been investigated. In this paper, we study the eavesdropping of fibre from a system perspective and compare the bending property of G.652 and G.657 fibres. The measurement results show that G.657 can be bent sharper than G.652 without causing any additional power attenuation at the receiver. This indicates that the so-called bending-insensitive G.657 fibre can potentially be eavesdropped more easily than their G.652 counterparts. The paper also shows that the power level measurement at the receiver may not be sufficient for unambiguous eavesdrop detection.
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| 2. |
- Song, Haokun, et al.
(författare)
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Machine-learning-based method for fiber-bending eavesdropping detection
- 2023
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Ingår i: Optics Letters. - 0146-9592 .- 1539-4794. ; 48:12, s. 3183-3186
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Tidskriftsartikel (refereegranskat)abstract
- In this Letter, we present a scheme for detecting fiber-bending eavesdropping based on feature extraction and machine learning (ML). First, 5-dimensional features from the time-domain signal are extracted from the optical signal, and then a long short-term memory (LSTM) network is applied for eavesdropping and normal event classification. Experimental data are collected from a 60km single-mode fiber transmission link with eavesdropping implemented by a clip-on coupler. Results show that the proposed scheme achieves a 95.83% detection accuracy. Furthermore, since the scheme focuses on the time-domain waveform of the received optical signal, additional devices and a special link design are not required.
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| 3. |
- Lin, Rui, et al.
(författare)
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To overcome the scalability limitation of passive optical interconnects in datacentres
- 2016
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Ingår i: Optics InfoBase Conference Papers. - : OSA - The Optical Society. - 2162-2701. - 9780960038008
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Konferensbidrag (refereegranskat)abstract
- We propose to add optical amplifier(s) to passive optical interconnect (POI) at top-of-rack in datacentres and validate this approach by introducing impairment constraints into POIs design. It is shown that one amplifier can improve scalability by a factor of 16.
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| 4. |
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| 5. |
- Yan, Li, 1988, et al.
(författare)
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Network performance trade-off in optical spatial division multiplexing data centers
- 2017
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Ingår i: 2017 Optical Fiber Communications Conference and Exhibition, OFC 2017 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781943580231 ; Part F40-OFC 2017, s. W3D.5-
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Konferensbidrag (refereegranskat)abstract
- We propose close-to-optimal network resource allocation algorithms for modular data centers using optical spatial division multiplexing. A trade-off between the number of established connections and throughput is identified and quantified.
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| 6. |
- 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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| 7. |
- Li, Jun, et al.
(författare)
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Service Migration in Fog Computing Enabled Cellular Networks to Support Real-Time Vehicular Communications
- 2019
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 7, s. 13704-13714
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Tidskriftsartikel (refereegranskat)abstract
- Driven by the increasing number of connected vehicles and related services, powerful communication and computation capabilities are needed for vehicular communications, especially for real-time and safety-related applications. A cellular network consists of radio access technologies, including the current long-term evolution (LTE), the LTE advanced, and the forthcoming 5th generation mobile communication systems. It covers large areas and has the ability to provide high data rate and low latency communication services to mobile users. It is considered the most promising access technology to support real-time vehicular communications. Meanwhile, fog is an emerging architecture for computing, storage, and networking, in which fog nodes can be deployed at base stations to deliver cloud services close to vehicular users. In fog computing-enabled cellular networks, mobility is one of the most critical challenges for vehicular communications to maintain the service continuity and to satisfy the stringent service requirements, especially when the computing and storage resources are limited at the fog nodes. Service migration, relocating services from one fog server to another in a dynamic manner, has been proposed as an effective solution to the mobility problem. To support service migration, both computation and communication techniques need to be considered. Given the importance of protocol design to support the mobility of the vehicles and maintain high network performance, in this paper, we investigate the service migration in the fog computing-enabled cellular networks. We propose a quality-of-service aware scheme based on the existing handover procedures to support the real-time vehicular services. A case study based on a realistic vehicle mobility pattern for Luxembourg scenario is carried out, where the proposed scheme, as well as the benchmarks, are compared by analyzing latency and reliability as well as migration cost.
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| 8. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
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Spectrum Anomaly Detection for Optical Network Monitoring using Deep Unsupervised Learning
- 2021
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Ingår i: IEEE Communications Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 1089-7798 .- 1558-2558. ; 25:5, s. 1583-1586
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Tidskriftsartikel (refereegranskat)abstract
- Accurate and efficient anomaly detection is a key enabler for the cognitive management of optical networks, but traditional anomaly detection algorithms are computationally complex and do not scale well with the amount of monitoring data. Therefore, we propose an optical spectrum anomaly detection scheme that exploits computer vision and deep unsupervised learning to perform optical network monitoring relying only on constellation diagrams of received signals. The proposed scheme achieves 100% detection accuracy even without prior knowledge of the anomalies. Furthermore, operation with encoded images of constellation diagrams reduces the runtime by up to 200 times.
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| 9. |
- Song, Haokun, et al.
(författare)
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Eavesdropping Detection and Localization in WDM Optical System
- 2023
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Ingår i: Proceedings - 2023 IEEE Future Networks World Forum: Future Networks: Imagining the Network of the Future, FNWF 2023.
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Konferensbidrag (refereegranskat)abstract
- Leveraging our initial work on detecting eavesdropping events in WDM optical systems [1], we propose a mechanism that utilizes bisecting k-means on dynamic optical performance monitoring (OPM) data to initialize the detection. We develop a method to detect and localize single and multiple eavesdropping events in WDM optical systems. Very small losses caused by eavesdropping can be detected using OPM data collected at the receiver, while the in-line OPM data enables localizing single and multiple eavesdropping events.
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| 10. |
- Tonini, Federico, 1990, et al.
(författare)
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Demonstrating the Benefits of Service-Aware Pod Autoscaling with Shared Resources
- 2023
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Ingår i: 2023 IEEE 9th International Conference on Network Softwarization: Boosting Future Networks through Advanced Softwarization, NetSoft 2023 - Proceedings. - 9798350399806 ; , s. 305-307
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Konferensbidrag (refereegranskat)abstract
- Service providers can leverage shared resources to reduce the overall amount of required resources while keeping acceptable Quality of Service (QoS) levels. Kubernetes (K8s) provides a Horizontal Pod Autoscaling (HPA) mechanism that allows to automatically adjust the number of Pods to closely follow the user demand variations over time. To properly leverage shared resources with HPA, service providers need to limit the use of dedicated resources and overprovisioning. However, in the case of traffic spikes, there may not be enough resources to satisfy the demand. The HPA, which relies on resource usage to drive the scaling, is unaware of how many requests could not be served with the required QoS. This might result in an underestimation of the number of required Pods to be added, leading to additional QoS degradation. This demonstration showcases the effectiveness of a new Pod autoscaling mechanism (i.e., Service Aware Pod Autoscaling (SAPA)) that relies on user request measurements from the service load balancer to better estimate the number of required Pods. SAPA allows selecting the amount of Pod resources (dedicated and shared) in a simple way. We demonstrate the benefits of SAPA by comparing it to a K8s cluster based on the traditional HPA in terms of resource usage and service latency.
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