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

Abstract

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.

Routing, Modulation Format, Spatial Lane, and Spectrum Block Assignment in Static Spatial Channel Networks

“…The modulation format distance limit parameter has been defined considering crosstalk in [38] that was used to select the MF for a connection request, by comparing the considered routing path length. Morales et al [39] solved the static routing, modulation level, spatial mode, and spectrum assignment problem using 34 different explainable demand-prioritization strategies. MFC was not considered in all of the above strategies.…”

“…EONs RSA RMSA ILP: [19,20,40,41,[43][44][45]] Intelligent Optimization Algorithms: [21][22][23][24][25]42] Heuristic Algorithms: [26,27,37,41] Machine Learning: [29,30] SDM-EONs RCSA RMCSA ILP: [32,36], Intelligent Optimization Algorithms: [35,36,46,47] Heuristic Algorithms: [31,33,39,46,47] Machine Learning: [38] SCNs RSWA RSCSA Heuristic Algorithms: [48] ILP and Heuristic Algorithms: [49] These aforementioned studies provide us with ideas to solve the network resource allocation problem in SCNs. We defined the network resource allocation problem in static SCNs as the routing, modulation format, spatial lane, and spectrum block assignment (RMSSA) problem.…”

A Bandwidth-Balanced RMLSA Solution for Static Elastic Optical Network: A Two Stages Approach

“…The different connections were sorted before the spectrumassignment process. According to [37]- [39], sorting connections according to their FSUs demand or route length (measured in terms of the number of hops), both in decreasing order, allows greater efficiency in the use of resources. For this reason, we decided to use both sorting policies and then performed spectrum assignments.…”