Service and Resource Aware Flow Management Scheme for an SDN-Based Smart Digital Campus Environment

• Main Authors: Yosra Njah, Chuan Pham, Mohamed Cheriet
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Smart digital campus; Internet of Things (IoT); software-defined networking (SDN); flow characterization; Quality of Service (QoS); distributed rate allocation
• Online Access: https://ieeexplore.ieee.org/document/9127419/

Recently, campuses have been embracing smart digital technologies in order to boost the efficiency of education and creativity. Thus, massive heterogeneous flows are generated as a result of multitude simultaneous access from several heterogeneous devices. This is putting pressure on campuses to make better management of their constrained resources and to ensure the required Quality of Service (QoS). In this paper, we propose a multi-flow management scheme over a software-defined smart digital campus network, named Service and Resource Aware Flow Management (SRAFM). Our approach offers a unified fully-programmable architecture, a distributed end-host-based flow characterization plane, and a centralized software-defined optimization model to efficiently manage heterogeneous flows. Network functionalities, including QoS aware routing and resource allocation optimization, are formulated as a mixed-integer linear programming problem. Due to its NP-hard complexity, we propose an approximation algorithm in a decomposed fashion based on Lagrangian Dual Decomposition (LDD) and subgradient methods to find an optimal solution for flow management. We evaluate our scheme from different aspects, including the number of simultaneous heterogeneous flows, QoS provisioning, characterization impacts, and network scalability. Compared to the well-known benchmarks in QoS aware routing and optimization problem, SWAY and LARAC, our simulation results conducted with a large number of flows over a small-scale network show promising performance. The proposed scheme significantly improves the cost reduction by 51% as compared to LARAC, the end-to-end delay by 21% and 34%, the bandwidth availability by 27% and 36%, and the QoS violation by 11% and 29% as compared to SWAY and LARAC, respectively.