Joint energy efficiency and load balancing optimization in hybrid IP/SDN networks

Software-defined networking (SDN) is a paradigm that provides flexibility and programmability to computer networks. By introducing SDN nodes in a legacy IP network topology, network operators can benefit on higher control over the infrastructure. However, this migration is not a fast or straightforward process. Furthermore, to provide an adequate quality of service in hybrid IP/SDN networks, the coordination of both IP and SDN paradigm is fundamental. In this paper, this coordination is used to solve two optimization problems that are typically solved separately: (i) traffic load balancing and (ii) power consumption minimization. Each of these problems has opposing objectives, and thus, their joint consideration implies striking a balance between them. Therefore, this paper proposes the Hybrid Spreading Load Algorithm (HSLA) heuristic that jointly faces the problems of balancing traffic by minimizing link utilization and network's power consumption in a hybrid IP/SDN network. HSLA is evaluated over differently sized topologies using different methods to select which nodes are migrated from IP to SDN. These evaluations reveal that alternative approaches that only address one of the objectives are outperformed by HSLA

Reducing the reconfiguration cost of flow tables in energy-efficient Software-Defined Networks

Software-Defined Networking (SDN) is a new networking paradigm that is attracting the attention of the research community due to the flexibility provided by the separation between data and control planes. In particular, the SDN scenario introduces new aspects to be considered when formulating the energy-aware routing problem, such as the reconfiguration cost of flow tables. In this paper we introduce and investigate the problem of minimizing the power consumption of an SDN network while also reducing the number of rules that have to be modified in the flow tables of SDN nodes. An optimization problem formulation and a GA (Genetic Algorithm) based heuristic are presented to tackle this two-fold problem. The performance analysis, carried out over different realistic network topologies, highlights that GA is able to increase the power saving opportunities up to the 20% more than other energy-aware routing solutions proposed in the literature, while reducing the number of rules that have to be modified up to 100 times