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| 2. |
- Furdek, Marija, 1985-, et al.
(författare)
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Attack-aware dedicated path protection in optical networks
- 2016
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Ingår i: Journal of Lightwave Technology. - : IEEE. - 0733-8724 .- 1558-2213. ; 34:4, s. 1050-1061
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Tidskriftsartikel (refereegranskat)abstract
- Due to the high data rates in optical networks,physical-layer attacks targeting service degradation, such as powerjamming, can potentially lead to large data and revenue losses.Conventional network survivability approaches which establishlink-disjoint working and backup paths to protect from componentfaults may not provide adequate protection for such attacks.Namely, the working and the backup paths, although link-disjoint,might both be affected by a single attack scenario due to specificattack propagation characteristics. To enhance the existing survivabilityapproaches, we utilize the concept of an attack group(AG) which incorporates these characteristics to identify connectionswhich can simultaneously be affected by a single attack. Weapply this concept to dedicated path protection (DPP) and developattack-aware DPP (AA-DPP) approaches which aim to establishAG-disjoint primary and backup paths in a cost-effective manner.We provide a two-step ILP formulation for the routing and wavelengthassignment of the working and backup paths, as well as aheuristic for larger problem instances. Numerical results indicatethat the proposed approaches provide dedicated path protectionschemes with enhanced attack protection without using more resources(i.e., wavelengths, average path lengths) than standardDPPmethods.
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| 3. |
- Natalino Da Silva, Carlos, 1987, et al.
(författare)
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Experimental Study of Machine-Learning-Based Detection and Identification of Physical-Layer Attacks in Optical Networks
- 2019
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Ingår i: Journal of Lightwave Technology. - 0733-8724 .- 1558-2213. ; 37:16, s. 4173-4182
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Tidskriftsartikel (refereegranskat)abstract
- Optical networks are critical infrastructure supporting vital services and are vulnerable to different types of malicious attacks targeting service disruption at the optical layer. Due to the various attack techniques causing diverse physical- layer effects, as well as the limitations and sparse placement of optical performance monitoring devices, such attacks are difficult to detect, and their signatures are unknown. This paper presents a Machine Learning (ML) framework for detection and identification of physical-layer attacks, based on experimental attack traces from an operator field-deployed testbed with coherent receivers. We perform in-band and out-of-band jamming signal insertion attacks, as well as polarization modulation attacks, each with varying intensities. We then evaluate 8 different ML classifiers in terms of their accuracy, and scalability in processing experimental data. The optical parameters critical for accurate attack identification are identified and the generalization of the models is validated. Results indicate that Artificial Neural Networks (ANNs) achieve 99.9% accuracy in attack type and intensity classification, and are capable of processing 1 million samples in less than 10 seconds.
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| 4. |
- Ranaweera, Chathurika, et al.
(författare)
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Optical Transport Network Design for 5G Fixed Wireless Access
- 2019
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Ingår i: Journal of Lightwave Technology. - 0733-8724 .- 1558-2213. ; 37:16, s. 3893-3901
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Tidskriftsartikel (refereegranskat)abstract
- The fifth generation (5G) of mobile technology, 5G is anticipated to be a significant leap in the evolution of mobile communication. 5G will be designed to attain 1000 times higher data volumes, 10 times lower latency, and 100 times more connected devices than its predecessor, 4G. Due to 5Gs ability to sustain high bandwidth per unit area, 5G is considered to be a cost-efficient solution to provide fixed wireless access (FWA) to households on a large scale. FWA is seen as an attractive alternative for fixed broadband access in scenarios where last mile access based on wired technologies is not economically viable. While approaches for enhancing user experience in a 5G FWA environment are investigated in the research community, the problem of providing cost-effective high capacity transport for FWA deployments still remains a major challenge. This is particularly challenging due to diverse transport network architectures and requirements imposed by different 5G deployment models. This paper addresses this problem by formulating a generalized joint-optimization framework to simultaneously plan wireless access and optical transport for 5G FWA networks in order to minimize the deployment cost while meeting various network requirements. We demonstrate the applicability of the proposed framework by applying it to a real scenario with a range of deployment options and where different types of optical x-haul solutions are considered. The results provide a cornerstone for deployment strategies that will be imperative for realizing a future-proof and cost-effective broadband access network.
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| 5. |
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| 6. |
- Raza, Muhammad Rehan, et al.
(författare)
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A Slice Admission Policy Based on Big Data Analytics for Multi-Tenant 5G Networks
- 2019
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Ingår i: Journal of Lightwave Technology. - : Institute of Electrical and Electronics Engineers (IEEE). - 0733-8724 .- 1558-2213. ; 37:7, s. 1690-1697
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Tidskriftsartikel (refereegranskat)abstract
- Network slicing is a key concept in 5G networking. It enables an infrastructure provider (InP) to support heterogeneous services over a common platform by creating a customized slice for each one of them. Once in operation, the slices can be dynamically scaled up/down to match the variation of service requirements. Although an InP generates revenue by accepting a slice request, however it might need to pay a penalty (proportional to the level of service degradation) if a slice cannot be scaled up when required. Hence, it becomes crucial to decide which slice requests should be accepted in order to maximize the net profit of an InP. This paper presents a slice admission strategy based on big data analytics (BDA) predictions. The intuition is to accept a slice request only when it is estimated that no service degradation will take place for both the incoming slice request and the slices already in operation. In this way, the penalty paid by an InP is contained, with beneficial effects on the overall net profit. Apart from simulations, the performance of the proposed admission policy has also been evaluated using emulation. Simulation results show that, in the presence of a high penalty due to service degradation, using BDA predictions brings up to 50.7% increase in profit, as compared to a slice admission policy without BDA. Emulation results for a small network scenario show a profit increase of up to 383% with only a small impact on the slice provisioning time (i.e., due to the processing of BDA predictions).
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| 7. |
- Raza, Muhammad Rehan, et al.
(författare)
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Reinforcement Learning for Slicing in a 5G Flexible RAN
- 2019
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Ingår i: Journal of Lightwave Technology. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0733-8724 .- 1558-2213. ; 37:20, s. 5161-5169
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Tidskriftsartikel (refereegranskat)abstract
- Network slicing enables an infrastructure provider (InP) to support heterogeneous 5G services over a common platform (i.e., by creating a customized slice for each service). Once in operation, slices can be dynamically scaled up/down to match the variation of their service requirements. An InP generates revenue by accepting a slice request. If a slice cannot be scaled up when required, an InP has to also pay a penalty (proportional to the level of service degradation). It becomes then crucial for an InP to decide which slice requests should be accepted/rejected in order to increase its net profit. This paper presents a slice admission strategy based on reinforcement learning (RL) in the presence of services with different priorities. The use case considered is a 5G flexible radio access network (RAN), where slices of different mobile service providers are virtualized over the same RAN infrastructure. The proposed policy learns which are the services with the potential to bring high profit (i.e., high revenue with low degradation penalty), and hence should be accepted. The performance of the RL-based admission policy is compared against two deterministic heuristics. Results show that in the considered scenario, the proposed strategy outperforms the benchmark heuristics by at least 55%. Moreover, this paper shows how the policy is able to adapt to different conditions in terms of: (i)slice degradation penalty vs. slice revenue factors, and (ii)proportion of high vs. low priority services.
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| 8. |
- Samadi, Payman, et al.
(författare)
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Flexible Architecture and Autonomous Control Plane for Metro-Scale Geographically Distributed Data Centers
- 2017
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Ingår i: Journal of Lightwave Technology. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0733-8724 .- 1558-2213. ; 35:6, s. 1188-1196
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Tidskriftsartikel (refereegranskat)abstract
- Enterprises and cloud providers are moving away from deployment of large-scale data centers and towards smallto mid-sized data centers because of their lower implementation and maintenance costs. An optical metro network is used to provide connectivity among these data centers. The optical network requires flexibility on bandwidth allocation and various levels of Quality of Service to support the new emerging applications and services including the ones enabled by 5G. As a result, next generation optical metro networks face complex control and management issues that needs to be resolved with automation. We present a converged inter/intra data center network architecture with an autonomous control plane for flexible bandwidth allocation. The architecture supports both single-rate and multi-rate data planes with two types of physical layer connections (Background and Dynamic) that provide connections with strict bandwidth and latency requirements. We demonstrate autonomous bandwidth steering between two data centers on our prototype. Leveraging a simulation platform, we show up to 5x lower transmission times and 25% less spectrum usage compared with the single-rate conventional non-converged networks. This is a significant improvement in the data center network performance and energy efficiency.
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| 9. |
- Udalcovs, Aleksejs, et al.
(författare)
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Analysis of Spectral and Energy Efficiency Tradeoff in Single-Line Rate WDM Links
- 2017
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Ingår i: Journal of Lightwave Technology. - : Institute of Electrical and Electronics Engineers (IEEE). - 0733-8724 .- 1558-2213. ; 35:10, s. 1847-1857
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Tidskriftsartikel (refereegranskat)abstract
- This paper investigates, through simulations, the tradeoff between energy efficiency (EE) and the overall spectral efficiency (SE) of fiber optic links for a given capacity and a link length. The comparison is made for various modulation formats, span lengths, and with/without using forward error correction (FEC). The power consumption of the different system components is estimated from the data sheets of the state-of-the-art equipment. Results show that the use of long single-mode fiber spans (i.e., more than 40 km) improves EE when coherent modulation formats are used. However, with noncoherent formats, the span length must be selected depending on SE, aggregated traffic amount needs to be transmitted, and link length. For almost all modulation formats, FEC reduces the overall energy consumption despite being one of the main power consumers in fiber optic communication systems. The power consumption of 3Rs becomes particularly important when the linear crosstalk limits the system reach. In all other cases, the power consumption of transponders and optical line amplifiers is dominating, but their contribution changes depending on the aggregated traffic amount and system reach.
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| 10. |
- Van, Dung Pham, et al.
(författare)
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Adaptive Open-Shop Scheduling for Optical Interconnection Networks
- 2017
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Ingår i: Journal of Lightwave Technology. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0733-8724 .- 1558-2213. ; 35:13, s. 2503-2513
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Tidskriftsartikel (refereegranskat)abstract
- This paper deals with resource management in optical interconnection networks. It first proposes an optical resource management framework as a platform to develop and evaluate efficient solutions for multipoint-to-multipoint optical communication systems with a centralized controller. The paper then focuses on studying the optical resource scheduling (ORS) problem as a core element in the framework by applying the classical open-shop scheduling theory. The ORS problem can therefore be solved by adopting the existing preemptive and nonpreemptive open-shop scheduling algorithms. In an optical network with nonnegligible reconfiguration delay, a preemptive algorithm may incur high reconfiguration overhead resulting in worse performance compared to the nonpreemptive strategy. Motivated by this fact, this paper proposes an adaptive open-shop scheduling (AOS) algorithm that dynamically decides the optimal scheduling strategy according to traffic condition and system parameters, such as reconfiguration delay, nonpreemptive approximation ratio, and number of involved optical interfaces. The solution is assessed by means of an analytical model that allows to quantify the network performance in terms of packet delay and potential energy savings obtained by the sleep mode operation. As a possible application scenario, the inter- and intrarack optical interconnection networks in data centres are considered. Analytical results demonstrate how the proposed AOS outperforms the nonpreemptive and preemptive scheduling strategies for typical configurations used in data center networks. In addition, the reconfiguration delay and wake-up time of optical devices are identified as performance-determining factors.
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