In common energy-aware routing models, path selection is generally formulated as a single objective optimization problem with either a single metric (minimizing the number of active links) or a single function encompassing different metrics (minimizing the number of active nodes and links). However, existing trade-off between network performance and energy saving has motivated the necessity to consider QoS requirements in order to achieve multiple TE goals. A first trivial way to reduce energy consumption and guarantee QoS is to incorporate traffic requirements into mathematical models by means of additional constraints. However, this simple approach may led to over-provisioning (i.e. routing a connection onto a path that has too many resources for it) and, consequently, to a reduction in the number of future requests that can potentially be accommodated. Moreover, several performance studies have shown that, by opti- mizing multiple objectives simultaneously, better solutions can be obtained [87–89]. Therefore, instead of considering only traditional single objective functions with requirement constraints for the paths computation, several works evaluate the potential and the effective applicability of multi-objective procedures, in order to define routing strategies that can guarantee low energy consumption and good performance at the same time.
For such purpose, authors in [90] tackle a multi-objective optimization problem managing the link weights so as to minimize the energy consumption (primary objective) as well as a network congestion measure (secondary objective). To do so, a MILP-based algorithm for Energy-aware Weights Optimization (MILP-EWO) is presented. This approach takes advantage of the Interior Gateway Protocol Weight Optimization (IGP-WO) algorithm [91] to modify the OSPF weights according to the considered objectives. Predicted traffic matrices are assumed and link capacities
2.3. Multi-Objective Traffic Engineering
are considered in this off-line intra-domain proposal to put network elements (links and routers) into sleep mode and to guarantee low levels of network congestion. Thus, the quality of solutions heavily relies on the traffic prediction accuracy.
The approach proposed in [92] simultaneously optimizes the power saving and different QoS- related parameters in software defined data center networks, according to a pre-defined combi- nation of software quality requirements. The authors propose four different linear programming approaches that schedule requested traffic flows on the switches considering different metrics in the objective function such as energy consumption, throughput, transition time between sleep/active mode and their combination. An evaluation decision framework is implemented to assess their proposal. However, the size of the set of path for all flows considered in their solution is only scalable for data center topologies such as Fat-Tree [93], where the number of possible paths is small and does not grow rapidly along with the network size.
The work in [94] aims to improve the energy efficiency together with the quality of transmis- sion in software defined flexible optical networks. To do so, the authors propose a multi-domain routing and spectrum assignment algorithm that takes into account quality of transmission (in terms of bit error rate) and energy saving. These two objectives are balanced considering connec- tion requests separated into two classes of services, for each of which one objective is optimized in the path selection.
In the same way, both metrics can be improved if two different routing approaches are implemented in the SDN controller and applied depending on the context and network operator goals. This idea is conceived in [95], where a heuristic-based algorithm, named GoGreen, is designed to compute routing paths being energy efficient and satisfying the traffic requirement in terms of bit rate. According to the traffic type (video streaming, web browsing, sensor messages), one of the aforementioned objectives is taken as the first metric and used to determine the best k paths. Then, computed paths are sorted following the second metric and the first route is selected as the most suitable solution. Simulations show the trade-off between the number of considered paths (i.e. k) and the solutions quality.
The possibility of choosing different routing algorithms is also proposed in [89] through the design of an SDN-based integrated control plane. After collecting the network energy related information and the QoS requirements, specific traffic groups are defined. Then, based on the specific user application one out of three possible routing algorithms, namely Least Cost (LC),
Shortest Path (SP) and Load Balancing (LB), is selected. Specifically, the LC algorithm is assigned to the web-surfing traffic, the SP algorithm to the VoIP traffic and the LB algorithm to the IPTV traffic. In this way, the QoS level for crucial traffic type can still be maintained, while a more energy efficient routing algorithm is employed for non-crucial traffic.
On the other hand, evolutionary algorithms [96, 97] have been applied to solve single and multi-objective problems in a wide variety of contexts in SDN [98], including routing strategies oriented to achieve power efficiency. For instance, the use of a MOEA for route selection has been proposed in [99] for dynamic optical networks with a centralized software defined integrated control plane. The solution is based on considering different objective functions according to the traffic type. In particular for higher priority traffic, they improve the energy performance without degrading QoS by taking energy saving as the secondary objective. Their approach supports multiple QoS requirements in terms of network performance, such as delay and blocking rate. However, only one network topology is considered, neglecting the effect that different network scenarios may have on the solution quality.
Similarly, in [100] a multi-objective particle swarm optimization algorithm is conceived to achieve network energy saving and load balancing in SDN. In this work, the problem is for- mulated as a multi-objective mixed integer programming model adding QoS constrains to the basic maximum concurrent flow problem. Specifically, it guarantees that the total delay of a routing path allocated to a demand cannot exceed the maximum delay it allows. The proposed heuristic algorithm, called MOPSO, dynamically aggregates and balances the incoming traffic putting the unused switches and links into sleeping mode.