研究目的
Investigating efficient allocation of unicast, anycast, and multicast flows in survivable elastic optical networks with dedicated path protection.
研究成果
The proposed GRA and CG-based methods significantly improve solution quality for flow allocation in survivable elastic optical networks. GRA outperforms reference greedy algorithms, and CG-based methods achieve solutions close to optimal, with performance influenced by traffic pattern and initial column quality. Future work should focus on optimizing anycast demands and scaling to larger networks.
研究不足
The study is limited to single link failures and dedicated path protection. The ILP model does not scale well to large instances, and computational complexity is high for exact methods. Simulations are based on specific network topologies and traffic patterns, which may not generalize to all real-world scenarios.
1:Experimental Design and Method Selection:
The study models the problem as an integer linear programming (ILP) and proposes solution methods including greedy randomized algorithm (GRA) and column generation (CG)-based approaches. Simulations are conducted to evaluate method efficiency.
2:Sample Selection and Data Sources:
Three realistic network topologies (DT14, NSF15, Euro16) are used with varying data center (DC) placements and traffic demand sets generated uniformly at random, including unicast, anycast, and multicast types with specific bit-rates and receiver counts.
3:List of Experimental Equipment and Materials:
Elastic optical networks (EONs) with BV-Ts implementing PDM-OFDM technology, six modulation formats (BPSK, QPSK, 8-QAM, 16-QAM, 32-QAM, 64-QAM), and transponders with capacities of 40, 100, and 400 Gbps. Spectrum resources divided into slices of 6.25 or 12.5 GHz.
4:25 or 5 GHz.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: Algorithms (FF, MSF, LPF, AFA, GRA, CG-based methods) are implemented in C++ and run on an Intel Core i7 machine. Performance is evaluated based on gap to optimal results and processing time, with multiple repetitions for non-deterministic methods.
5:Data Analysis Methods:
Relative gap to optimal result is calculated as a metric. Statistical analysis includes averaging over scenarios and repetitions, with confidence intervals reported.
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