Security and Quality of Service (QoS) in communication networks are critical factors supporting end-to-end dataflows in data centers. On the other hand, it is essential to provide mechanisms that enable different treatments for applications requiring sensitive data transfer. Both applications’ requirements can vary according to their particular needs. To achieve their goals, it is necessary to provide services so that each application can request both the quality of service and security services dynamically and on demand. This article presents QoSS, an API web service to provide both Quality of Service and Security for applications through software-defined networks. We developed a prototype to conduct a case study to provide QoS and security. QoSS finds the optimal end-to-end path according to four optimization rules: bandwidth-aware, delay-aware, security-aware, and application requirements (considering the bandwidth, delay, packet loss, jitter, and security level of network nodes). Simulation results showed that our proposal improved end-to-end application data transfer by an average of 45%. Besides, it supports the dynamic end-to-end path configuration according to the application requirements. QoSS also logs each application’s data transfer events to enable further analysis.
Software-defined networking (SDN) is the fastest growing and most widely deployed network infrastructure due to its adaptability to new networking technologies and intelligent applications. SDN simplifies network management and control by separating the control plane from the data plane. The SDN controller performs the routing process using the traditional shortest path approach to obtain end-to-end paths. This process usually does not consider the nodes’ capacity and may cause network congestion and delays, affecting flow performance. Therefore, we evaluate the most conventional routing criteria in the SDN scenario based on Dijkstra’s algorithm and compare the found paths with our proposal based on a cellular genetic algorithm for multi-objective optimization (MOCell). We compare our proposal with another multi-objective evolutionary algorithm based on decomposition (MOEA/D) for benchmark purposes. We evaluate various network parameters such as bandwidth, delay, and packet loss to find the optimal end-to-end path. We consider a large-scale inter-domain SDN scenario. The simulation results show that our proposed method can improve the performance of data streams with TCP traffic by up to 54% over the traditional routing method of the shortest path and by 33% for the highest bandwidth path. When transmitting a constant data stream using the UDP protocol, the throughput of the MOCell method is more than 1.65% and 9.77% for the respective paths.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.