Dynamic Resource Allocation in Metro Elastic Optical Networks Using Lyapunov Drift Optimization

Abstract

Consistent growth in the volume and dynamic behavior of traffic mandates new requirements for fast and adaptive resource allocation in metro networks. We propose a dynamic resource allocation technique for adaptive minimization of spectrum usage in metro elastic optical networks. We consider optical transmission as a service specified by its bandwidth profile parameters, which are minimum, average, and maximum required transmission rates. To consider random traffic events, we use a stochastic optimization technique to develop a novel formulation for dynamic resource allocation in which service level specifications and network stability constraints are addressed. Next, we employ the elegant theory of Lyapunov optimization to solve the stochastic optimization problem and derive a fast integer linear program, which is periodically solved to create an adaptation between available resources and dynamic network state. To quantize the performance of the proposed technique, we report its spectral efficiency as a function of peak to average traffic ratio and Lyapunov penalty coefficient. Simulation results show that the dynamic resource allocation procedure can improve spectral efficiency by a factor of 3.3 for a peak to average traffic ratio of 1.37 and a Lyapunov penalty coefficient of 10 3 in comparison with fixed network planning. There is also a trade-off between transmission delay and spectrum utilization in the proposed technique, which can be adjusted by a Lyapunov penalty coefficient.

The Recent Contributions of Routing and Spectrum Assignment Algorithms in Elastic Optical Network (EON)

“…Mohammad Hadi et al [10] proposed a new dynamic resource assignment method for the metro elastic optical network. This approach addressed the issues in service level provisions and firmness of network constraints.…”

“…In MPS-NSA [13] used next state aware allocation policy is good for fragmentation spectrum because each request light path connection assigned then it will find to the free space spectrum slot for needed so it reduces the number of blocks. The LDO [10] uses the Lyapunov penalty coefficient that helps to allocate and reallocate the spectrum resources periodically based on intention of traffic flows. Also, LDO provides much better solutions defragmentation and produce the higher spectrum resource utilization under large number of the connection request.…”

Reliable Slicing of 5G Transport Networks with Dedicated Protection

“…2) Slice assignment and Spectral Contiguity constraints: We ensure by (4) that if a path p is selected with a specific transmission configuration t, then the required number of slices n t to support the data-rate t (d) is allocated on the path. (5) ensures that each slice on an SLink is allocated to at most one path. Finally, (6) ensures the slices allocated on each link of a path form a contiguous frequency spectrum.…”

“…foreachē ∈Ē in the sorted order do 4 Iē ←FindEmbedding(G,ē, P ī e , Tē) 5 foreach e ∈ p|p ∈ Iē.P do 6 Perform slice assignment using Iē.S…”

“…Transportnetwork capacity must be easily partitionable to facilitate network slicing for 5G services such as enhanced mobile broadband and ultra-reliable low latency communications [4]. Elastic Optical Networks (EONs) are an excellent choice for establishing transport network slices, thanks to their ability to tailor network resources based on service requirements [5].…”