| 1. |
- Gajic, Marija, et al.
(författare)
-
A Framework for Spatial and Temporal Evaluation of Network Disaster Recovery
- 2020
-
Ingår i: Proceedings of the 32nd International Teletraffic Congress, ITC 2020. ; , s. 37-45
-
Konferensbidrag (refereegranskat)abstract
- The support of vital societal functions requires a reliable communication network, especially in the presence of crises and disastrous events. Disasters caused by natural factors including earthquakes, fires, floods or hurricanes can disable network elements such as links and nodes and cause widespread disruption in end users connectivity to network services. Effects of disasters can vary over space and time due to disaster escalation and propagation. Network recovery from disasters requires understanding of both the spatial properties of the hazard at hand, and their temporal evolution. While the former has already been addressed in the literature, existing models and measures are unable to capture the temporal aspects of disaster recovery.This paper proposes a framework for spatial and temporal evaluation of network disaster recovery. It allows for modelling random spatial patterns of disasters in a geographical grid. The temporal aspects captured in our framework include changes due to the progression of a potentially shape-changing disaster across the affected area, as well as to the recovery actions of adaptive network reconfiguration and topology reconstruction undertaken by the network operator. The framework applicability is demonstrated on a content delivery network use case example, where we capture the evolving network performance in terms of the average shortest path length between the peers and the content replicas hosted by servers. By providing insights into the spatial and temporal effects of both disaster escalation and remediation measures, our proposed framework lays down the groundwork for flexible disaster modelling and recovery sequence optimization.
|
|
| 2. |
- Gajic, Marija, et al.
(författare)
-
Survivability Assessment of 5G Network Slicing During Massive Outages
- 2023
-
Ingår i: Proceedings of 2023 13th International Workshop on Resilient Networks Design and Modeling, RNDM 2023.
-
Konferensbidrag (refereegranskat)abstract
- Mobile networks support variety of heterogeneous services, including the emergency and mission-critical ones. The next generation of mobile networks introduces the concept of network slicing where different services can have a dedicated, logically separated virtual network running over a shared physical infrastructure. Each slice may have a specific set of functional and non-functional requirements including performance, security, resilience, and survivability. Given the importance of emergency services during massive outages caused by a natural disaster, the network operators need an efficient way to evaluate the performance of the sliced network in such adverse circumstances. In this paper, we describe how survivability quantification framework can be applied to assess and compare the performance of different slicing configurations during and after massive outages. We demonstrate our proposal in a simplified use-case scenario where the performance metric for each stage of the recovery is represented with delay and throughput of the clients at a sliced, shared bottleneck. The metrics are acquired from OMNeT++ simulations. Survivability is then obtained from an analytical model and the time until the critical services (for the first responders) are recovered is of particular interest. In the scenario we consider 8 application types, 4 priority levels, and 5 approaches to map clients to slices. The results show significant performance variations between different slicing configurations, both for the critical and non-critical applications and thus highlight the importance of having a slicing configuration optimally tailored to the use case.
|
|
| 3. |
- Casellas, Ramon, et al.
(författare)
-
Introduction to the ONDM2020 special issue
- 2021
-
Ingår i: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 13:6, s. ONDM1-ONDM2
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- This JOCN special issue includes extended versions of selected papers that were presented at the 24th International Conference on Optical Network Design and Modeling (ONDM2020), which took place virtually on May 18-21, 2020. The topics covered by the papers represent clear trends in current optical networking research including filterless optical networks and their applicability in metropolitan scenarios; programmable, software-defined-networking-enabled sliceable bandwidth variable transceivers supporting multi-dimensionality; and two applications of machine learning-the cognitive reconfiguration of data-center networks in support of high-performance computing, and quality of transmission estimation for reduced margins.
|
|
| 4. |
- Chan, Vincent, et al.
(författare)
-
Network-wide localization of optical-layer attacks
- 2020
-
Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11616 LNCS, s. 310-322
-
Konferensbidrag (refereegranskat)abstract
- Optical networks are vulnerable to a range of attacks targeting service disruption at the physical layer, such as the insertion of harmful signals that can propagate through the network and affect co-propagating channels. Detection of such attacks and localization of their source, a prerequisite for secure network operation, is a challenging task due to the limitations in optical performance monitoring, as well as the scalability and cost issues. In this paper, we propose an approach for localizing the source of a jamming attack by modeling the worst-case scope of each connection as a potential carrier of a harmful signal. We define binary words called attack syndromes to model the health of each connection at the receiver which, when unique, unambiguously identify the harmful connection. To ensure attack syndrome uniqueness, we propose an optimization approach to design attack monitoring trails such that their number and length is minimal. This allows us to use the optical network as a sensor for physical-layer attacks. Numerical simulation results indicate that our approach obtains network-wide attack source localization at only 5.8% average resource overhead for the attack monitoring trails.
|
|
| 5. |
- de Sousa, Amaro, et al.
(författare)
-
Structural Methods to Improve the Robustness of Anycast Communications to Large-Scale Failures
- 2020
-
Ingår i: Guide to Disaster-Resilient Communication Networks. - Cham : Springer International Publishing. - 9783030446857 ; , s. 401-425
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This chapter is dedicated to the description of structural methods aiming to improve the robustness of anycast communications to large-scale failures, either due to natural disasters or malicious human activities. The chapter considers both software-defined networks (SDNs) where the anycast nodes are the nodes hosting SDN controllers, and content delivery networks (CDNs) where the anycast nodes are the nodes hosting content replicas. Most of the structural methods described in this chapter aim to optimally select the anycast nodes in a given network. The chapter first addresses the robustness of anycast communications to natural disasters based on geodiversity routing. Then, different methods are described to select the SDN controller locations aiming to maximize the SDN control plane robustness to malicious node attacks. Finally, the chapter addresses the robustness of CDNs to malicious link cuts by describing methods for the network upgrade (based either on the addition of new links or new replica locations) and for the optimal selection of content replica locations.
|
|
| 6. |
- Etezadi, Ehsan, 1993, et al.
(författare)
-
Deep reinforcement learning for proactive spectrum defragmentation in elastic optical networks [Invited]
- 2023
-
Ingår i: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 15:10, s. E86-E96
-
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.
|
|
| 7. |
- Etezadi, Ehsan, 1993, et al.
(författare)
-
DeepDefrag: A deep reinforcement learning framework for spectrum defragmentation
- 2022
-
Ingår i: 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings. ; , s. 3694-3699
-
Konferensbidrag (refereegranskat)abstract
- Exponential growth of bandwidth demand, spurred by emerging network services with diverse characteristics and stringent performance requirements, drives the need for dynamic operation of optical networks, efficient use of spectral resources, and automation. One of the main challenges of dynamic, resource-efficient Elastic Optical Networks (EONs) is spectrum fragmentation. Fragmented, stranded spectrum slots lead to poor resource utilization and increase the blocking probability of incoming service requests. Conventional approaches for Spectrum Defragmentation (SD) apply various criteria to decide when, and which portion of the spectrum to defragment. However, these polices often address only a subset of tasks related to defragmentation, are not adaptable, and have limited automation potential. To address these issues, we propose DeepDefrag, a novel framework based on reinforcement learning that addresses the 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. DeepDefrag outperforms the well-known Older-First First-Fit (OF-FF) defragmentation heuristic, achieving lower blocking probability under smaller defragmentation overhead.
|
|
| 8. |
|
|
| 9. |
- Etezadi, Ehsan, 1993, et al.
(författare)
-
Proactive Spectrum Defragmentation Leveraging Spectrum Occupancy State Information
- 2023
-
Ingår i: International Conference on Transparent Optical Networks. - 2162-7339. ; 2023-July
-
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.
|
|
| 10. |
- Etezadi, Ehsan, 1993, et al.
(författare)
-
Programmable Filterless Optical Networks: Architecture, Design and Resource Allocation
- 2024
-
Ingår i: IEEE/ACM Transactions on Networking. - 1558-2566 .- 1063-6692. ; 32:2, s. 1096-1109
-
Tidskriftsartikel (refereegranskat)abstract
- Filterless optical networks (FONs) are a costeffective optical networking technology that replaces reconfigurable optical add-drop multiplexers, used in conventional, wavelength-switched optical networks (WSONs), by passive optical splitters and couplers. FONs follow the drop-and-waste transmission scheme, i.e., broadcast signals without filtering, which generates spectrum waste. Programmable filterless optical networks (PFONs) reduce this waste by equipping network nodes with programmable optical white box switches that support arbitrary interconnections of passive elements. Cost-efficient PFON solutions require optimal routing, modulation format and spectrum assignment (RMSA) to connection requests, as well as optimal design of the node architecture. This paper presents an optimization framework for PFONs. We formulate the RMSA problem in PFONs as a single-step integer linear program (ILP) that jointly minimizes the total spectrum and optical component usage. As RMSA is an NP-complete problem, we propose a two-step ILP formulation that addresses the RMSA sub-problems separately and seeks sub-optimal solutions to larger problem instances in acceptable time. Simulation results indicate a beneficial trade-off between component usage and spectrum consumption in proposed PFON solutions. They use up to 64% less spectrum than FONs, up to 84% fewer active switching elements than WSONs, and up to 81% fewer optical amplifiers at network nodes than FONs or WSONs.
|
|
| 11. |
|
|
| 12. |
|
|
| 13. |
|
|
| 14. |
- Fan, Yuchuan, et al.
(författare)
-
EVM Estimation for Performance Monitoring in Coherent Optical Systems : An Approach of Linear Regression
- 2022
-
Ingår i: Optics InfoBase Conference Papers. - : Optica Publishing Group (formerly OSA). - 9781557528209
-
Konferensbidrag (refereegranskat)abstract
- We experimentally demonstrate the effectiveness of a simple linear regression scheme for optical performance monitoring when applied after modulation format identification. It outperforms the FFNN-based benchmark scheme providing 0.2% mean absolute error for EVM estimation., © 2022 The Author(s)
|
|
| 15. |
|
|
| 16. |
- Fan, Yuchuan, et al.
(författare)
-
Experimental validation of CNNs versus FFNNs for time- and energy-efficient EVM estimation in coherent optical systems
- 2021
-
Ingår i: Journal of Optical Communications and Networking. - : OPTICAL SOC AMER. - 1943-0620 .- 1943-0639. ; 13:10, s. E63-E71
-
Tidskriftsartikel (refereegranskat)abstract
- Error vector magnitude (EVM) has proven to be one of the optical performance monitoring metrics providing the quantitative estimation of error statistics. However, the EVM estimation efficiency has not been fully exploited in terms of complexity and energy consumption. Therefore, in this paper, we explore two deep-learning-based EVM estimation schemes. The first scheme exploits convolutional neural networks (CNNs) to extract EVM information from images of the constellation diagram in the in-phase/quadrature (IQ) complex plane or amplitude histograms (AHs). The second scheme relies on feedforward neural networks (FFNNs) extracting features from a vectorized representation of AHs. In both cases, we use short sequences of 32 Gbaud m-ary quadrature amplitude modulation (mQAM) signals captured before or after a carrier phase recovery. The impacts of the sequence length, neural network structure, and data set representation on the EVM estimation accuracy as well as the model training time are thoroughly studied. Furthermore, we validate the performance of the proposed schemes using the experimental implementation of 28 Gbaud 64QAM signals. We achieve a mean absolute estimation error below 0.15%, with short signals consisting of only 100 symbols per IQ cluster. Considering the estimation accuracy, the implementation complexity, and the potential energy savings, the proposed CNN- and FFNN-based schemes can be used to perform time-sensitive and accurate EVM estimation for mQAM signal quality monitoring purposes.
|
|
| 17. |
- Fan, Yuchuan, et al.
(författare)
-
Fast signal quality monitoring for coherent communications enabled by CNN-based EVM estimation
- 2021
-
Ingår i: Journal of Optical Communications and Networking. - : Institute of Electrical and Electronics Engineers Inc.. - 1943-0620 .- 1943-0639. ; 13:4, s. B12-B20
-
Tidskriftsartikel (refereegranskat)abstract
- We propose a fast and accurate signal quality monitoring scheme that uses convolutional neural networks for error vector magnitude (EVM) estimation in coherent optical communications. We build a regression model to extract EVM information from complex signal constellation diagrams using a small number of received symbols. For the additive-white-Gaussian-noise-impaired channel, the proposed EVM estimation scheme shows a normalized mean absolute estimation error of 3.7% for quadrature phase-shift keying, 2.2% for 16-Ary quadrature amplitude modulation (16QAM), and 1.1% for 64QAM signals, requiring only 100 symbols per constellation cluster in each observation period. Therefore, it can be used as a low-complexity alternative to conventional bit-error-rate estimation, enabling solutions for intelligent optical performance monitoring.
|
|
| 18. |
- Fan, Yuchuan, et al.
(författare)
-
Feedforward Neural Network-Based EVM Estimation : Impairment Tolerance in Coherent Optical Systems
- 2022
-
Ingår i: IEEE Journal of Selected Topics in Quantum Electronics. - : Institute of Electrical and Electronics Engineers Inc.. - 1077-260X .- 1558-4542. ; 28:4
-
Tidskriftsartikel (refereegranskat)abstract
- Error vector magnitude (EVM) is commonly used for evaluating the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques for EVM estimation extend the functionality of conventional optical performance monitoring (OPM). In this article, we evaluate the tolerance of our developed EVM estimation scheme against various impairments in coherent optical systems. In particular, we analyze the signal quality monitoring capabilities in the presence of residual in-phase/quadrature (IQ) imbalance, fiber nonlinearity, and laser phase noise. We use feedforward neural networks (FFNNs) to extract the EVM information from amplitude histograms of 100 symbols per IQ cluster signal sequence captured before carrier phase recovery. We perform simulations of the considered impairments, along with an experimental investigation of the impact of laser phase noise. To investigate the tolerance of the EVM estimation scheme to each impairment type, we compare the accuracy for three training methods: 1) training without impairment, 2) training one model for all impairments, and 3) training an independent model for each impairment. Results indicate a good generalization of the proposed EVM estimation scheme, thus providing a valuable reference for developing next-generation intelligent OPM systems.
|
|
| 19. |
|
|
| 20. |
- Furdek Prekratic, Marija, 1985, et al.
(författare)
-
Design of Programmable Filterless Optical Networks
- 2020
-
Ingår i: 2020 Photonics North, PN 2020.
-
Konferensbidrag (refereegranskat)abstract
- We present the main operating principles and guidelines for the design of programmable filterless networks.
|
|
| 21. |
- Furdek Prekratic, Marija, 1985, et al.
(författare)
-
Introduction to the ONDM 2021 special issue
- 2022
-
Ingår i: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 14:5, s. ONDM1-ONDM2
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- This JOCN special issue contains extended versions of selected papers presented at the 25th International Conference on Optical Network Design and Modeling (ONDM 2021), which took place virtually from 28 June through 1 July 2021. The topics covered by the papers represent clear trends in current optical networking research, including capacity upgrade through transmission parameter optimization in multiband systems, spectral efficiency improvement through probabilistic constellation shaping in Flex Grid/multicore fiber networks, novel point-to-multipoint optical architectures based on digital subcarrier multiplexing that enable a rethink of multilayer network design, and trustworthy inter-operator sharing of passive optical network capacity based on a smart contract.
|
|
| 22. |
- Furdek Prekratic, Marija, 1985, et al.
(författare)
-
Introduction to the Photonic Networks and Devices (NETWORKS) Special Issue
- 2020
-
Ingår i: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 12:4, s. NET1-NET2
-
Tidskriftsartikel (refereegranskat)abstract
- This special issue comprises extended versions of some of the top-scored papers that were presented at the OSA Photonic Networks and Devices (NETWORKS) meeting that was part of the OSA Advanced Photonics Congress held in Burlingame, California, USA, July 29–August 1, 2019. Here, we highlight relevant topics from included papers relating to photonic communication network development.
|
|
| 23. |
- Furdek Prekratic, Marija, 1985, et al.
(författare)
-
Machine Learning for Cognitive Optical Network Security Management
- 2020
-
Ingår i: Optics InfoBase Conference Papers. - 2162-2701. ; Part F185-NETWORKS 2020
-
Konferensbidrag (refereegranskat)abstract
- This talk surveys the security threats pertinent to the optical network and outlines the progress and challenges in developing machine learning approaches for cognitive management of optical network security.
|
|
| 24. |
- Furdek Prekratic, Marija, 1985, et al.
(författare)
-
Machine learning for network security management, attacks, and intrusions detection
- 2022
-
Ingår i: Machine Learning for Future Fiber-Optic Communication Systems. ; , s. 317-336
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This chapter focuses on challenges, progress and pitfalls in applying ML to physical-layer security management. In the context of trustworthy networks, we motivate the need for automation in support of the work of network security professionals. We summarize the characteristics of known attack techniques targeting the physical layer and outline the framework for optical network security management. Supervised, semisupervised and unsupervised learning techniques that can aid automation of network security management are described with a focus on their performance requirements in the context of security. Accuracy, complexity, and interpretability of these techniques are examined on a use case of jamming and polarization scrambling attacks performed experimentally in a telecom operator network testbed. Finally, several open research challenges in the context of optical network security are outlined along with possible avenues to tackle some of them.
|
|
| 25. |
- Furdek Prekratic, Marija, 1985, et al.
(författare)
-
Machine Learning for Optical Network Security Management
- 2020
-
Ingår i: Conference on Optical Fiber Communication, Technical Digest Series. - 9781943580712 ; Part F174-OFC 2020
-
Konferensbidrag (refereegranskat)abstract
- We discuss the role of supervised, unsupervised and semi-supervised learning techniques in identification of optical network security breaches. The applicability, performance and challenges related to practical deployment of these techniques are examined.
|
|
| 26. |
- Furdek Prekratic, Marija, 1985, et al.
(författare)
-
Machine Learning for Optical Network Security Monitoring: A Practical Perspective
- 2020
-
Ingår i: Journal of Lightwave Technology. - 0733-8724 .- 1558-2213. ; 38:11, s. 2860-2871
-
Forskningsöversikt (refereegranskat)abstract
- In order to accomplish cost-efficient management of complex optical communication networks, operators are seeking automation of network diagnosis and management by means of Machine Learning (ML). To support these objectives, new functions are needed to enable cognitive, autonomous management of optical network security. This paper focuses on the challenges related to the performance of ML-based approaches for detection and localization of optical-layer attacks, and to their integration with standard Network Management Systems (NMSs). We propose a framework for cognitive security diagnostics that comprises an attack detection module with Supervised Learning (SL), Semi-Supervised Learning (SSL) and Unsupervised Learning (UL) approaches, and an attack localization module that deduces the location of a harmful connection and/or a breached link. The influence of false positives and false negatives is addressed by a newly proposed Window-based Attack Detection (WAD) approach. We provide practical implementation guidelines for the integration of the framework into the NMS and evaluate its performance in an experimental network testbed subjected to attacks, resulting with the largest optical-layer security experimental dataset reported to date.
|
|
| 27. |
- Furdek Prekratic, Marija, 1985, et al.
(författare)
-
Optical Network Security Management: Requirements, Architecture and Efficient Machine Learning Models for Detection of Evolving Threats [Invited]
- 2021
-
Ingår i: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 13:2, s. A144-A155
-
Tidskriftsartikel (refereegranskat)abstract
- As the communication infrastructure that sustains critical societal services, optical networks need to function in a secure and agile way. Thus, cognitive and automated security management functionalities are needed, fueled by the proliferating machine learning (ML) techniques and compatible with common network control entities and procedures. Automated management of optical network security requires advancements both in terms of performance and efficiency of ML approaches for security diagnostics, as well as novel management architectures and functionalities. This paper tackles these challenges by proposing a novel functional block called Security Operation Center (SOC), describing its architecture, specifying key requirements on the supported functionalities and providing guidelines on its integration with optical layer controller. Moreover, to boost efficiency of ML-based security diagnostic techniques when processing high-dimensional optical performance monitoring data in the presence of previously unseen physical-layer attacks, we combine unsupervised and semi-supervised learning techniques with three different dimensionality reduction methods and analyze the resulting performance and trade-offs between ML accuracy and run time complexity.
|
|
| 28. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
-
A Flexible and Scalable ML-Based Diagnosis Module for Optical Networks: A Security Use Case
- 2023
-
Ingår i: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 15:8, s. C155-C165
-
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.
|
|
| 29. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
-
Autonomous Security Management in Optical Networks
- 2021
-
Ingår i: 2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings.
-
Konferensbidrag (refereegranskat)abstract
- The paper describes the Optical Security Manager module and focuses on the role of Machine Learning (ML) techniques. Issues related to the accuracy, run-time complexity and interpretability of ML outputs are described and coping strategies outlined.
|
|
| 30. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
-
Content placement in 5G‐enabled edge/core datacenter networks resilient to link cut attacks
- 2020
-
Ingår i: Networks. - : Wiley. - 1097-0037 .- 0028-3045. ; 75:4, s. 392-392
-
Tidskriftsartikel (refereegranskat)abstract
- High throughput, resilience, and low latency requirements drive the development of 5G‐enabled content delivery networks (CDNs) which combine core data centers (cDCs) with edge data centers (eDCs) that cache the most popular content closer to the end users for traffic load and latency reduction. Deployed over the existing optical network infrastructure, CDNs are vulnerable to link cut attacks aimed at disrupting the overlay services. Planning a CDN to balance the stringent service requirements and increase resilience to attacks in a cost‐efficient way entails solving the content placement problem (CPP) across the cDCs and eDCs. This article proposes a framework for finding Pareto‐optimal solutions with minimal user‐to‐content distance and maximal robustness to targeted link cuts, under a defined budget. We formulate two optimization problems as integer linear programming (ILP) models. The first, denoted as K‐best CPP with minimal distance (K‐CPP‐minD), identifies the eDC/cDC placement solutions with minimal user‐to‐content distance. The second performs critical link set detection to evaluate the resilience of the K‐CPP‐minD solutions to targeted fiber cuts. Extensive simulations verify that the eDC/cDC selection obtained by our models improves network resilience to link cut attacks without adversely affecting the user‐to‐content distances or the core network traffic mitigation benefits.
|
|
| 31. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
-
Functional Metrics to Evaluate Network Vulnerability to Disasters
- 2020
-
Ingår i: Guide to Disaster-Resilient Communication Networks; Rak, J., Hutchison, D. (eds). - Cham : Springer International Publishing. - 9783030446857 ; , s. 47-62
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Disasters can cause, intentionally or unintentionally, the failure of several network components at the same time. A vast body of literature focuses on understanding the impact of disasters on the network infrastructure to enable the design of more robust networks. However, these multiple failures also affect the applications running over the network infrastructure. Even when the impact of a disaster on the structural performance indicators is insignificant, the functional implications can be substantial. More importantly, a small degradation in network performance can result in severe disruptions of overlay applications, or even completely prevent their proper functioning. Therefore, it is essential to analyze the impact of a disaster on the functional aspects of the network, i.e. the Quality of Service (QoS) offered to the applications and the Quality of Experience (QoE) perceived by the users. In this chapter, we review the functional metrics for evaluating the impact of disasters on applications and users. We specify relevant packet- and network-based functional metrics as well as perceived subjective metrics, and demonstrate the impact of disasters on QoS and QoE metrics in a case study.
|
|
| 32. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
-
Microservice-Based Unsupervised Anomaly Detection Loop for Optical Networks
- 2022
-
Ingår i: 2022 Optical Fiber Communications Conference and Exhibition, OFC 2022 - Proceedings.
-
Konferensbidrag (refereegranskat)abstract
- Unsupervised learning (UL) is a technique to detect previously unseen anomalies without needing labeled datasets. We propose the integration of a scalable UL-based inference component in the monitoring loop of an SDN-controlled optical network.
|
|
| 33. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
-
Root Cause Analysis for Autonomous Optical Network Security Management
- 2022
-
Ingår i: IEEE Transactions on Network and Service Management. - 1932-4537. ; 19:3, s. 2702-2713
-
Tidskriftsartikel (refereegranskat)abstract
- The ongoing evolution of optical networks towards autonomous systems supporting high-performance services beyond 5G requires advanced functionalities for automated security management. To cope with evolving threat landscape, security diagnostic approaches should be able to detect and identify the nature not only of existing attack techniques, but also those hitherto unknown or insufficiently represented. Machine Learning (ML)-based algorithms perform well when identifying known attack types, but cannot guarantee precise identification of unknown attacks. This makes Root Cause Analysis (RCA) crucial for enabling timely attack response when human intervention is unavoidable. We address these challenges by establishing an ML-based framework for security assessment and analyzing RCA alternatives for physical-layer attacks. We first scrutinize different Network Management System (NMS) architectures and the corresponding security assessment capabilities. We then investigate the applicability of supervised and unsupervised learning (SL and UL) approaches for RCA and propose a novel UL-based RCA algorithm called Distance-Based Root Cause Analysis (DB-RCA). The framework’s applicability and performance for autonomous optical network security management is validated on an experimental physical-layer security dataset, assessing the benefits and drawbacks of the SL-and UL-based RCA. Besides confirming that SL-based approaches can provide precise RCA output for known attack types upon training, we show that the proposed UL-based RCA approach offers meaningful insight into the anomalies caused by novel attack types, thus supporting the human security officers in advancing the physical-layer security diagnostics.
|
|
| 34. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
-
Root Cause Analysis for Autonomous Optical Networks: A Physical Layer Security Use Case
- 2020
-
Ingår i: 2020 European Conference on Optical Communications, ECOC 2020.
-
Konferensbidrag (refereegranskat)abstract
- To support secure and reliable operation of optical networks, we propose a framework for autonomous anomaly detection, root cause analysis and visualization of the anomaly impact on optical signal parameters. Verification on experimental physical layer security data reveals important properties of different attack profiles.
|
|
| 35. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
-
Scalable and Efficient Pipeline for ML-based Optical Network Monitoring
- 2023
-
Ingår i: 2023 Optical Fiber Communications Conference and Exhibition, OFC 2023 - Proceedings.
-
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.
|
|
| 36. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
-
Scalable Physical Layer Security Components for Microservice-Based Optical SDN Controllers
- 2021
-
Ingår i: European Conference on Optical Communication, ECOC. ; 2021
-
Konferensbidrag (refereegranskat)abstract
- We propose and demonstrate a set of microservice-based security components able to perform physical layer security assessment and mitigation in optical networks. Results illustrate the scalability of the attack detection mechanism and the agility in mitigating attacks.
|
|
| 37. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
-
Spectrum Anomaly Detection for Optical Network Monitoring using Deep Unsupervised Learning
- 2021
-
Ingår i: IEEE Communications Letters. - : Institute of Electrical and Electronics Engineers Inc.. - 1089-7798 .- 1558-2558. ; 25:5, s. 1583-1586
-
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.
|
|
| 38. |
- Ranaweera, Chathurika, et al.
(författare)
-
Rethinking of Optical Transport Network Design for 5G/6G Mobile Communication
- 2021
-
Ingår i: IEEE Future Networks Tech Focus.
-
Tidskriftsartikel (refereegranskat)abstract
- Driven by the increasing use of emerging smart mobile applications, mobile technology is continuously and rapidly advancing towards the next generation communication systems such as 5G and 6G. However, the transport network, which needs to provide low latency and reliable connectivity between hundreds of thousands of cell sites and the network core, has not advanced at the same pace. This article provides insight into how we can solve the fundamental challenges of implementing cost-optimal transport and 5G and beyond mobile networks simultaneously while satisfying the network and user requirements irrespective of the radio access network's architecture.
|
|
| 39. |
- Rezaei Aghdam, Sina, 1989, et al.
(författare)
-
Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairments
- 2021
-
Ingår i: IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD. - 2378-4873. - 9781665417792 ; 2021-October
-
Konferensbidrag (refereegranskat)abstract
- We consider a wireless federated learning system where multiple data holder edge devices collaborate to train a global model via sharing their parameter updates with an honest-but-curious parameter server. We demonstrate that the inherent hardware-induced distortion perturbing the model updates of the edge devices can be exploited as a privacy-preserving mechanism. In particular, we model the distortion as power-dependent additive Gaussian noise and present a power allocation strategy that provides privacy guarantees within the framework of differential privacy. We conduct numerical experiments to evaluate the performance of the proposed power allocation scheme under different levels of hardware impairments.
|
|
| 40. |
|
|
| 41. |
|
|
| 42. |
- Tonini, Federico, 1990, et al.
(författare)
-
Network Slicing Automation: Challenges and Benefits
- 2020
-
Ingår i: 2020 24th International Conference on Optical Network Design and Modeling, ONDM 2020.
-
Konferensbidrag (refereegranskat)abstract
- Network slicing is a technique widely used in 5G networks where multiple logical networks (i.e., slices) run over a single shared physical infrastructure. Each slice may realize one or multiple services, whose specific requirements are negotiated beforehand and regulated through Service Level Agreements (SLAs). In Beyond 5G (B5G) networks it is envisioned that slices should be created, deployed, and managed in an automated fashion (i.e., without human intervention) irrespective of the technological and administrative domains over which a slice may span. Achieving this vision requires a combination of novel physical layer technologies, artificial intelligence tools, standard interfaces, network function virtualization, and software-defined networking principles. This paper provides an overview of the challenges facing network slicing automation with a focus on transport networks. Results from a selected group of use cases show the benefits of applying conventional optimization tools and machine-learning-based techniques while addressing some slicing design and provisioning problems.
|
|
| 43. |
- Tremblay, Christine, et al.
(författare)
-
Agile Metropolitan Filterless Optical Networking
- 2022
-
Ingår i: Proceedings - 2022 IEEE Future Networks World Forum, FNWF 2022. ; , s. 113-116
-
Konferensbidrag (refereegranskat)abstract
- The tremendous traffic growth generated by video, cloud, future 5G and beyond services is compelling network operators to re-think network architectures to ensure flexible and efficient service support. Filterless optical networking based on broadcast-and-select nodes and coherent transceivers is considered as a disruptive approach for delivering network agility in a cost-effective manner. The filterless network concept has been widely studied for terrestrial and submarine applications. In this paper, we explore the suitability of filterless architectures in metropolitan networks through a comparative performance analysis with a conventional metro network based on active switching. The results show that a filterless solution with lower, but adequate, network connectivity can achieve up to 36% lower power consumption and up to 45.4% cost reduction at the expense of a 19% higher spectrum usage, which makes the filterless architecture an attractive alternative for metro network deployments.
|
|
| 44. |
- Tremblay, Christine, et al.
(författare)
-
Cost- and energy-efficient filterless architectures for metropolitan networks
- 2023
-
Ingår i: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 15:9, s. D47-D55
-
Tidskriftsartikel (refereegranskat)abstract
- Network operators are forced to find cost- and energy-efficient solutions for networks supporting new and emerging services with strict latency and ultra-high-capacity requirements. A disruptive approach for delivering network agility in a cost- and energy-efficient manner is employing filterless optical networking based on broadcast-and-select nodes and coherent transceivers. The filterless network concept has been widely studied for terrestrial and submarine applications. In this paper, we investigate the performance of filterless optical networks in metropolitan core and aggregation networks where agility is required due to service dynamics, customer changes, and service flexibility requirements. We compare our results with a conventional metro network based on active switching. The results show that the filterless metro network based on a hierarchical structure similar to its active switching counterpart has comparable installed first cost and spectrum usage at 11 Tb/s of total traffic, as well as cost and wavelength consumption advantages of 19.5% and 16%, respectively, at 107 Tb/s of total traffic. These results confirm that the filterless architecture is an attractive alternative for metro network deployments.
|
|
| 45. |
- Yaghoubi, Forough, 1988-, et al.
(författare)
-
Design and Reliability Performance of Wireless Backhaul Networks Under Weather-Induced Correlated Failures
- 2021
-
Ingår i: IEEE Transactions on Reliability. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9529 .- 1558-1721. ; , s. 1-14
-
Tidskriftsartikel (refereegranskat)abstract
- Design of reliable wireless backhaul networks is challenging due to the inherent vulnerability of wireless backhauling to random fluctuations of the wireless channel. Considerable studies deal with modifying and designing the network topology to meet the reliability requirements in a cost-efficient manner. However, these studies ignore the correlation among link failures, particularly those caused by weather disturbances. Consequently, the resulting topology designs may fail to meet the network reliability requirements under correlated failure scenarios. To fill this gap, we study the design of cost-efficient and reliable wireless backhaul networks under correlated failures with a focus on rain disturbances. We first propose a new model to consider the pairwise correlation amongf links along a path. The model is verified on real data, indicating an approximation closer to reality than the existing independent failure model. Second, we model the correlation among different paths by defining a penalty cost. Considering the newly formalized link and path correlation, we formulate the correlation-aware network topology design problem as a quadratic integer program to find the optimal solutions. Two lightweight heuristic algorithms are developed to find near-optimal solutions within reasonable time. Performance evaluation shows that correlation-aware design substantially improves the resiliency under rain disturbances at a slightly increased cost compared to independent failure approaches.
|
|
| 46. |
- Yaghoubi, Forough, et al.
(författare)
-
Resilient SDN-Based Routing Against Rain Disruptions for Wireless Networks
- 2020
-
Ingår i: Guide to Disaster-Resilient Communication Networks; Rak, J., Hutchison, D. (eds). - Cham : Springer International Publishing. - 9783030446857 ; , s. 507-522
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Rain is a relatively frequent long-term event that can reduce the wireless networks throughput and availability. Depending on the rain rate, the communication link may fail completely, bringing about further instability problems of end-to-end paths. One of the effective solutions for reducing the impact of a long-term event such as rain is rerouting. However, the first step for mitigating these effects is to detect the presence and identify the type of long-term event to be able to act correctly. Therefore, it is vital to use an accurate and fast rain detection algorithm that can trigger the rerouting process. Throughout years, many algorithms were proposed to implement efficient and fast routing. Nowadays, software-defined networking (SDN) paradigm eases the deployment of centralized routing approaches by shifting the forwarding intelligence and management to a centralized controller and keeping the network elements as simple as possible. SDN is a promising solution that provides network programmability and facilitates dynamic quality-of-service provisioning. The global view captured by SDN eases reconfiguration and management of the whole backhaul network, which is particularly important in the case of large-scale disturbances of varying intensity and coverage due to weather. This chapter presents models for capturing the impact of rain on wireless channel attenuation, scrutinizes algorithms for rain detection, and discusses different rerouting approaches for mitigating the rain impact.
|
|
| 47. |
- Zhu, Jing, et al.
(författare)
-
How to Survive Targeted Fiber Cuts: A Game Theoretic Approach for Resilient SDON Control Plane Design
- 2020
-
Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11616 LNCS, s. 168-180
-
Konferensbidrag (refereegranskat)abstract
- Software-defined optical networking (SDON) paradigm enables programmable, adaptive and application-aware backbone networks via centralized network control and management. Aside from the manifold advantages, the control plane (CP) of an SDON is exposed to diverse security threats. As the CP usually shares the underlying optical infrastructure with the data plane (DP), an attacker can launch physical-layer attacks to cause severe disruption of the CP. This paper studies the problem of resilient CP design under targeted fiber cut attacks, whose effectiveness depends on both the CP designer's and the attacker's strategies. Therefore, we model the problem as a non-cooperative game between the designer and the attacker, where the designer tries to set up the CP to minimize the attack effectiveness, while the attacker aims at maximizing the effectiveness by cutting the most critical links. We define the game strategies and utility functions, conduct theoretical analysis to obtain the Nash Equilibrium (NE) as the solution of the game. Extensive simulations confirm the effectiveness of our proposal in improving the CP resilience to targeted fiber cuts.
|
|
| 48. |
|
|
