An Effective Hierarchical Control Plane for Software-Defined Networks Leveraging TOPSIS for End-to-End QoS Class-Mapping

Abstract

If an end-to-end (E2E) path includes multiple domains, we need inter-domain collaboration to ensure the E2E quality-of-service (QoS) for the applications. In heterogeneous networks, an E2E path may go through domains with several QoS classes in each domain. However, the prevalent legacy network architecture and the standard software-defined networking (SDN) model lack effective mechanisms for inter-domain collaboration and QoS class mapping. In this study, we propose a hierarchical SDN control plane approach to guarantee the E2E QoS among multiple domains with various QoS classes on the E2E path. We propose a controller module for selecting the most suitable QoS class for each domain in the E2E path based on multi-criteria decision-making by using the technique for order of preference by similarity to ideal solution (TOPSIS). We map the suitable service classes in the global controller (GC) for provisioning the E2E QoS according to the application service requests. First, we propose an SDN-based inter-domain communication scheme and the message processing algorithm for E2E service delivery when multiple QoS classes exist in each domain. Next, we formulate the problem of service class selection with TOPSIS, provide an E2E mapping scheme, and demonstrate it with an example. Finally, we compare the proposed approach with the existing schemes for E2E QoS class mapping in terms of E2E delay, jitter, packet loss rate (PLR), and cost (per bandwidth unit). According to our simulation results, the proposed approach ensures the E2E QoS and guarantees the E2E delay, jitter, PLR, and cost according to the application service requests.

Management of Software-Defined Networking Powered by Artificial Intelligence

“…Results of the proposed SDNMS-PAI are compared with existing ones i.e., software-defined networking with no artificial intelligence (SDN-NAI) [32]. There are 5 domains on the E2E path and two layers of the controllers i.e., local controllers and a global controller.…”

“…The rewards for state action pairs in the Q-learning are therefore more accurate than the local view states because these rewards with a hierarchical architecture reflect the E2E view of the underlying network. SDN architecture with a global view of the E2E network [32,33].…”

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“…One of the advantages of SDN structure is the programmable network structure. Many researchers have designed many schemes superior to traditional algorithms based on SDN structure [38][39][40]. Specifically, in the animal information collection system, we can pay more attention to the data collection task of the data layer, without too much consideration on how to forward and further process the data collected by sensors.…”

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“…For obtaining the optimal dispatch strategy, a set of "best" solutions could be selected from these non-inferior solutions via specific evaluation methods, such as inferior solution distance method (TOPSIS), analytic hierarchy process, reordering method, superior and hierarchical sequence method, etc. [34,35]. By virtue of TOPSIS, this section manages to draw the optimal solution as the decision-making scheme, and therein the definitions of the weight of each objective function become a key problem.…”