A Multi-Objective Routing Mechanism for Energy Management Optimization in SDN Multi-Control Architecture

Abstract

This paper proposed and implemented an energy-aware routing multi-level and mapping problem (EARMLP) algorithm to minimize the overall power consumption in Software-Defined Networking (SDN)-based core networks. To enforce network utilization toward green policies design for Data Centers (DCs), SDN leverages protocol configurations for routing available in the infrastructure. Therefore, the proposed mechanism aimed to design an optimal routing strategy that considers system configuration and traffic demand between the data and control planes in networks. The problem is then addressed from the perspective of the policy-based EARMLP technique, which is used to carefully determine the optimal assignment between controllers and their switches to optimize network energy savings. Hence, a controller placement problem (CPP) is established to select the optimal locations and number of controllers in core networks and create an optimal mapping and resource allocation between switches and controllers. Since the formulated energy-aware routing algorithm is designed as a multi-objective NP-hardness of the problem, a heuristic approach is developed to find optimal solutions for traffic routing between inter-controllers and controller-switch in terms of energy-aware consumption strategies. Consequently, the proposed optimal routing mechanism can rearrange traffic to meet provisioning criteria by utilizing the capacity-aware design. Remarkably, the energy saved in networks by our suggested method can approach up to 70% of the energy saved in SDN-based networks compared to other methods.

Toward the Design of an Efficient and Secure System Based on the Software-Defined Network Paradigm for Vehicular Networks

“…Most of the literature work has considered routing nodes and latency between SDN controllers as a primary metric. Other metrics were considered, such as load balancing, controller capacity, communication latency between controllers, power consumption, and cost of deployment [24][25] [26][27].…”

Request delay and survivability optimization for software defined‐wide area networking (SD‐WAN) using multi‐agent deep reinforcement learning

“…Nevertheless, the authors totally neglect the failures in the data plane and consider that there are no anomalies. In an effort to estimate flow routing, authors in Reference 22 study the trade‐offs between resilience, latency and cost by investigating several QoS metrics such as network link utilization, controller locations, and energy consumption. Although the proposed solution can reorganize traffic to meet the envisaged criteria and significantly save energy, the studied approach does not consider a solution when switches are failing.…”

Technologies and Infrastructures for a Sustainable Space

“…On one hand, the pervasiveness of connectivity is the key to render "green" most of the vertical domains, from energy to heath to industry, just to mention some very popular application realms. On the other hand, the pervasiveness and its consequences, like for instance the spreading of edge computing, the amount of small and spread around data centres, the increased transport and computational capacity related to the cloud operations need to become truly energy efficient so that pervasive connectivity be indeed a relief for the vertical sectors and, thus, for the Planet from the sustainability viewpoint [28]- [30]. Similarly, when dealing with space sustainability ally technologies, in particular with SDN, focused efforts are needed to render them energy efficient (e.g.…”