AI-Assisted Framework for Green-Routing and Load Balancing in Hybrid Software-Defined Networking: Proposal, Challenges and Future Perspective

Abstract

The explosive growth of IP networks, the advent of cloud computing, and the rapid progress in wireless communications witnessed today reflect significant progress towards meeting the explosive data traffic demands. Consequently, communications service providers should deploy efficient and intelligent network solutions to accommodate the huge traffic demands and to ease the capacity pressure on their network infrastructure. Besides, vendors should develop novel energy-efficient networks to reduce network utility costs and carbon footprint. Software-defined networking (SDN) provides a suitable solution, however, complete SDN deployment is currently unachievable in the short-term. An alternative is the hybrid SDN/ open shortest path forwarding (OSPF) network, which allows the deployment of SDN in legacy networks. Nevertheless, hybrid SDN/OSPF also faces several technical, economic and organizational challenges. Although many energyefficiency routing solutions exist in hybrid SDN/OSPF networks, they are generic and reactive by design. Moreover, these solutions are characterized by manual control plane forwarding configurations, leading to sub-optimal performance. The recent promising combination of SDN and artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) in traffic management and control provides tremendous opportunities. In this paper, we first provide a review of the most recent optimization approaches for global energy-efficient routing and load balancing. Next, we investigate a scalable and intelligent integrated architectural framework that leverages deep reinforcement learning (DRL) techniques to realize predictive and rate adaptive energy-efficient routing with guaranteed quality of service (QoS), in transitional hybrid SDN/OSPF networks. Based on the need to minimize global network energy consumption and improve link performance, this paper provides key research insights into the current progress in hybrid SDN/OSPF, ML and AI in the hope of stimulating more research.

Saving Energy Using the Modified Heuristic Algorithm for Energy Saving (MHAES) in Software-Defined Networks

“…By optimizing the on and off state of these devices, energy savings of up to 40% can be achieved. The key idea is to switch on these transmission devices only when necessary and switch them off otherwise, thereby conserving energy [32]. While energy saving is crucial, it is equally important to consider the quality of service (QoS) [33] since a significant reduction in QoS can have a more detrimental impact on our optimized network than the energy saved.…”

A Survey on Machine Learning Techniques for Routing Optimization in SDN

“…As part of that analysis, it encompasses some works related with ML and routing, and the authors specifically have a section devoted to ML for QoS management. Etengu et al [27] extensively analyze AI-assisted networks for green routing and load balancing, focused on a pragmatical approach, that is, hybrid SDN, usually leverage for smooth migration from legacy systems. At the end of the survey, the authors provide a set of challenges and future research directions, and they define a specific framework to tackle them.…”

“…Etengu et al [27] propose a DNN-based approach in a hybrid SDN/OSPF network deployment. The SDN controller performs energy-efficient routing and enhanced performance with QoS guarantees.…”

An Overview on SDN and NFV Security Orchestration in Cloud Network Environment

“…Furthermore, the adaption of a secure APIs in the SDN architecture would lead to the prevention of the attacks in a private networking environment. [13]…”