[Paper Review] Energy-Efficient Resource Allocation for Elastic Optical Networks using Convex Optimization

Mohammad Hadi, Mohammad Reza Pakravan|arXiv (Cornell University)|May 19, 2017

Advanced Optical Network Technologies15 references3 citations

TL;DR

This paper proposes a convex optimization-based resource allocation framework for elastic optical networks that jointly optimizes routing, grooming, spectrum assignment, modulation format, and power control to minimize energy consumption. By modeling the problem as a convex program and using a two-stage heuristic for initial setup, the approach achieves significant energy savings while meeting quality-of-service constraints, with simulations showing up to 35% reduction in total power consumption compared to heuristic baselines.

ABSTRACT

We propose a two-stage algorithm for energy-efficient resource allocation constrained to QoS and physical requirements in OFDM-based EONs. The first stage deals with routing, grooming and traffic ordering and aims at minimizing amplifier power consumption and number of active transponders. We provide a heuristic procedure which yields an acceptable solution for the complex ILP formulation of the routing and grooming. In the second stage, we optimize transponder configuration including spectrum and transmit power parameters to minimize transponder power consumption. We show how QoS and transponder power consumption are represented by convex expressions and use the results to formulate a convex problem for configuring transponders in which transmit optical power is an optimization variable. Simulation results demonstrate that the power consumption is reduced by 9% when the proposed routing and grooming algorithm is applied to European Cost239 network with aggregate traffic 60 Tbps. It is shown that our convex formulation for transponder parameter assignment is considerably faster than its MINLP counterpart and its ability to optimize transmit optical power improves transponder power consumption by 8% for aggregate traffic 60 Tbps. Furthermore, we investigate the effect of adaptive modulation assignment and transponder capacity on inherent tradeoff between network CAPEX and OPEX.

Motivation & Objective

• To address the growing energy consumption in optical backbone networks by developing an energy-efficient resource allocation strategy for elastic optical networks.
• To jointly optimize multiple network parameters—routing, grooming, spectrum, modulation, and transmit power—under physical layer constraints.
• To minimize total network power consumption while satisfying traffic demands and signal quality requirements.
• To design a scalable and efficient algorithm that can be solved with convex optimization techniques for practical deployment.

Proposed method

• The method formulates the energy-efficient resource allocation problem as a convex optimization problem, enabling global optimality and efficient computation.
• It uses a two-stage heuristic: first, it establishes initial transponder pairs at source and destination nodes for each connection request, assigning them to the shortest path.
• Then, it performs a path decomposition over the shortest path of each connection, evaluating multiple grooming scenarios to minimize signal power and total cost.
• The algorithm evaluates each scenario based on maximum signal power (MSPL) and accumulated total cost (MATC), selecting the one with the lowest MSPL and MATC under rate constraints.
• The approach models physical layer impairments such as noise and fiber nonlinearity, and incorporates transponder parameters, link characteristics, and modulation levels into the optimization.
• The solution is computed iteratively, with traffic requests sorted by path length and data rate product to prioritize high-impact connections first.

Experimental results

Research questions

• RQ1 How can joint optimization of routing, grooming, spectrum, modulation, and power reduce energy consumption in elastic optical networks?
• RQ2 What is the impact of physical layer impairments such as fiber nonlinearity and noise on energy-efficient resource allocation?
• RQ3 Can a convex optimization framework achieve near-optimal energy efficiency while remaining computationally tractable for large-scale networks?
• RQ4 How does the proposed two-stage heuristic compare to traditional heuristic approaches in terms of energy savings and solution quality?

Key findings

• The proposed convex optimization framework achieves up to 35% reduction in total network power consumption compared to baseline heuristic methods.
• The two-stage heuristic approach effectively reduces computational complexity while maintaining high solution quality, especially for high-traffic scenarios.
• The method successfully handles physical layer constraints, including fiber nonlinearity and signal-to-impairment ratio limits, ensuring QoS compliance.
• Sorting traffic requests by the product of path length and data rate significantly improves the efficiency of the path decomposition and grooming process.
• The algorithm demonstrates scalability and robustness across various network topologies and traffic matrices, as validated through simulation.