Software defined networking allows network providers to share their physical network (PN) among multiple tenants by means of network slicing, where several virtual networks (VNs) are provisioned on top of the physical one. In this scenario, PN resource utilization can be improved by introducing advanced orchestration functionalities that can intelligently assign and redistribute resources among the slices of different tenants according to the temporal variation of the VN resource requirements. This is a concept known as dynamic slicing. This paper presents a solution for the dynamic slicing problem in terms of both mixed integer linear programming formulations and heuristic algorithms. The benefits of dynamic slicing are compared against static slicing, i.e., an approach without intelligent adaptation of the amount of resources allocated to each VN. Simulation results show that dynamic slicing can reduce the VN rejection probability by more than 1 order of magnitude compared to static slicing. This can help network providers accept more VNs into their infrastructure and potentially increase their revenues. The benefits of dynamic slicing come at a cost in terms of service degradation (i.e., when not all the resources required by a VN can be provided), but the paper shows that the service degradation level introduced by the proposed solutions is very small.

Dynamic slicing approach for multi-tenant 5G transport networks [invited]

The traffic grooming problem is of high practical importance in emerging wide-area wavelength division multiplexing (WDM) optical networks, yet it is intractable for any but trivial network topologies. In this work, we present an effective and efficient hierarchical traffic grooming framework for WDM networks of general topology, with the objective of minimizing the total number of electronic ports. At the first level of hierarchy, we decompose the network into clusters and designate one node in each cluster as the hub for grooming traffic. At the second level, the hubs form another cluster for grooming intercluster traffic. We view each (first- or second-level) cluster as a virtual star, and we present an efficient near-optimal algorithm for determining the logical topology of lightpaths to carry the traffic within each cluster. Routing and wavelength assignment is then performed directly on the underlying physical topology. We demonstrate the effectiveness of our approach by applying it to two networks of realistic size, a 32-node, 53-link topology and a 47-node, 96-link network. Comparisons to lower bounds indicate that hierarchical grooming is efficient in its use of the network resources of interest, namely, electronic ports and wavelengths. In addition to scaling to large network sizes, our hierarchical approach also facilitates the control and management of multigranular networks.

/pdf/on-hierarchical-traffic-grooming-in-wdm-networks-fvqq0jig3v.pdf

On hierarchical traffic grooming in WDM networks

Efficiently solving the Traveling Thief Problem using hill climbing and simulated annealing

The Travelling Thief Problem (TTP) is an optimization problem introduced in order to provide a more realistic model for real-world optimization problems. The problem combines the Travelling Salesman Problem and the Knapsack Problem and introduces the notion of interdependence between sub-problems. In this paper, we study and compare different approaches for solving the TTP from a metaheuristics perspective. Two heuristic algorithms are proposed. The first is a Memetic Algorithm, and the second is a single-solution heuristic empowered by Hill Climbing and Simulated Annealing. Two other state-of-the-art algorithms are briefly revisited, analyzed, and compared to our algorithms. The obtained results prove that our algorithms are very efficient for many TTP instances.

Population-based vs. Single-solution Heuristics for the Travelling Thief Problem

We consider a hierarchical approach for traffic grooming in large multiwavelength networks of a general topology. Inspired by similar concepts in the airline industry, we decompose the network into clusters, and select a hub node in each cluster to groom traffic originating and terminating locally. At the second level of the hierarchy, the hub nodes form a virtual cluster for the purpose of grooming intra-cluster traffic. Clustering and hierarchical grooming enables us to cope with large network sizes and facilitates the control and management of traffic and network resources. Yet, determining the size and composition of clusters so as to yield good grooming solutions is a challenging task. We identify the grooming-specific factors affecting the selection of clusters, and we develop a parameterized clustering algorithm that can achieve a desired trade-off among various goals. We also obtain lower bounds on two important objectives in traffic grooming: the number of lightpaths and wavelengths needed to carry the subwavelength traffic. We demonstrate the effectiveness of clustering and hierarchical grooming by presenting the results of experiments on two network topologies that are substantially larger than those considered in previous traffic grooming studies.

/pdf/clustering-methods-for-hierarchical-traffic-grooming-in-16z756alzz.pdf

Clustering Methods for Hierarchical Traffic Grooming in Large-Scale Mesh WDM Networks

In this paper, we consider the traffic grooming, routing, and wavelength assignment (GRWA) problem for optical mesh networks. In most previous studies on optical mesh networks, traffic demands are usually assumed to be wavelength demands, in which case no traffic grooming is needed. In practice, optical networks are typically required to carry a large number of lower rate (sub-wavelength) traffic demands. Hence, the issue of traffic grooming becomes very important since it can significantly impact the overall network cost. In our study, we consider traffic grooming in combination with traffic routing and wavelength assignment. Our objective is to minimize the total number of transponders required in the network. We first formulate the GRWA problem as an integer linear programming (ILP) problem. Unfortunately, for large networks it is computationally infeasible to solve the ILP problem. Therefore, we propose a decomposition method that divides the GRWA problem into two smaller problems: the traffic grooming and routing problem and the wavelength assignment problem, which can then be solved much more efficiently. In general, the decomposition method only produces an approximate solution for the GRWA problem. However, we also provide some sufficient condition under which the decomposition method gives an optimal solution. Finally, some numerical results are provided to demonstrate the efficiency of our method.

B. Leida

Papers

Traffic grooming, routing, and wavelength assignment in optical WDM mesh networks