Security-Aware Routing and Core Allocation in Elastic Optical Network with Multi-core

Xuan, Hejun; Wang, Yuping; Hao, Shuai; Xu, Zhanqi; Li, Xiaodong; Gao, Xiao‐Zhi

doi:10.1109/cis.2016.0073

Cited by 5 publications

(10 citation statements)

References 15 publications

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“…Different from all the mentioned references that use heuristic algorithms for solving the RSCA problem, Refs. [9][10][11][12] proposed a corresponding intelligent algorithm that is based on some optimization and guidance mechanisms, and the obtained solutions are better than those resulting from heuristic algorithms. Concisely, these articles first established the mathematical model of the RSCA problem, then designed a reasonable chromosome coding scheme, and finally used a genetic algorithm to evolve the population to obtain the optimal solution.…”

Section: Related Workmentioning

confidence: 99%

“…Equation (9) ensures that the source and destination nodes of the generated request r k cannot be the same. Equation (10) means that each request r k can only select one path from its candidate path set P r k for use.…”

Section: Constraintsmentioning

confidence: 99%

“…[8] studied the RSCA problem by considering both inter-core crosstalk and core switching and proposed a heuristic algorithm, however, they did not use an intelligent algorithm. For the static RSCA problem [9][10][11][12], used an intelligent algorithm, but there is no study that simultaneously considers core switching, constructs a true multiobjective optimization model, and designs corresponding multiobjective algorithms to tackle such a model. Therefore, we focus on considering the static RSCA problem that considers inter-core crosstalk and core switching in MCF-EONs, and use an intelligent algorithm to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we focus on solving the static RSCA problem that considers inter-core crosstalk and core switching in MCF-EONs. Our work makes three key contributions: (a) we consider simultaneously core switching and multiple optimization objectives, i.e., the static RSCA based on core switching (RSCA-CS) problem is formulated as a true multiobjective ILP model, which minimizes both the network requests blocking ratio and crosstalk ratio; (b) in contrast to previous works [9][10][11][12] that separately code the routing and core into two populations resulting in a time-consuming solution. We design a novel joint routing and core coding scheme under core switching for the first time.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Adaptive Multiobjective Genetic Algorithm with Multi-Strategy Fusion for Resource Allocation in Elastic Multi-Core Fiber Networks

et al. 2022

Applied Sciences

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Core switching on different links in optical networks enables network operators to allocate network resources more flexibly, so as to reduce the network request blocking ratio under limited resources. Facing a differentiated network environment and diversified user demands, network operators need to optimize multiple objectives that are independent and diversionary of each other, and to provide multiple resource allocation schemes whose objective values do not dominate each other. For the static routing, spectrum, and core assignment (RSCA) problem in elastic optical networks with multi-core fiber (MCF-EONs), there is no literature that simultaneously considers core switching and multiobjective optimization algorithms. This paper improves the existing models and algorithms to adapt to the RSCA problem. In this paper, the RSCA problem is formulated as an integer linear programming model to minimize both network request blocking and crosstalk ratios simultaneously by considering core switching and inter-core crosstalk. To solve the model efficiently, we, therefore, design a joint routing and core coding scheme supporting core switching and propose a multiobjective evolutionary algorithm based on decomposition with adaptation and multi-strategy fusion (MOEA/D-AMSF), which integrates the new mechanisms of hybrid initial population generation, adaptive crossover, and double-layer and multi-point mutation in different iteration stages. These new mechanisms accelerate algorithm convergence and enhance solution diversity. Simulation results show that the proposed algorithm can obtain more dominated and diverse solutions compared with the existing multiobjective algorithm without considering core switching.

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Section: Related Workmentioning

confidence: 99%

Section: Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Adaptive Multiobjective Genetic Algorithm with Multi-Strategy Fusion for Resource Allocation in Elastic Multi-Core Fiber Networks

et al. 2022

Applied Sciences

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“…Results indicate that sequential heuristic combined with an appropriate ordering can give near-optimal solutions in low running times. In addition, according to [1], although meta-heuristics are widely used to solve resource allocation problems in SDM (such as [18], [36]- [38]), heuris-tics are extensively used too ( [16]- [18], [32], [36], [37], [39]- [42]). Heuristic solutions for the RMLSSA problem are usually solved in sequential stages [5], [31].…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance of SDM-EON Through Demand Prioritization: A Comprehensive Analysis

et al. 2021

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This paper studies the impact of demand-prioritization on Space-Division Multiplexing Elastic Optical Networks (SDM-EON). For this purpose, we solve the static Routing, Modulation Level, Spatial Mode, and Spectrum Assignment (RMLSSA) problem using 34 different explainable demandprioritization strategies. Although previous works have applied heuristics or meta-heuristics to perform demand-prioritization, they have not focused on identifying the best prioritization strategies, their inner operation, and the implications behind their good performance by thorough profiling and impact analysis. We focus on a comprehensive analysis identifying the best explainable strategies to sort network demands in SDM-EON, considering the physical-layer impairments found in optical communications. Also, we show that simply using the common shortest path routing might lead to higher resource requirements. Extensive simulation results show that up to 8.33 % capacity savings can be achieved on average by balanced routing, up to a 16.69 % capacity savings can be achieved using the best performing demandprioritization strategy compared to the worst-performing ones, the most used demand-prioritization strategy in the literature (serving demands with higher bandwidth requirements first) is not the best-performing one but the one sorting based on the path lengths, and using double-criteria strategies to break ties is key for a good performance. These results are relevant showing that a good combination of routing and demand-prioritization heuristics impact significantly on network performance. Additionally, they increase the understanding about the inner workings of good heuristics, a valuable knowledge when network settings forbid using more computationally complex approaches.

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Survey of resource allocation schemes and algorithms in spectrally-spatially flexible optical networking

Klinkowski

Lechowicz

Walkowiak

2018

Optical Switching and Networking

183

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Security-Aware Routing and Core Allocation in Elastic Optical Network with Multi-core

Cited by 5 publications

References 15 publications

An Adaptive Multiobjective Genetic Algorithm with Multi-Strategy Fusion for Resource Allocation in Elastic Multi-Core Fiber Networks

An Adaptive Multiobjective Genetic Algorithm with Multi-Strategy Fusion for Resource Allocation in Elastic Multi-Core Fiber Networks

Improving the Performance of SDM-EON Through Demand Prioritization: A Comprehensive Analysis

Survey of resource allocation schemes and algorithms in spectrally-spatially flexible optical networking

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