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• W3033448214 abstract "In this paper, we study the problem of data flow management in the presence of heterogeneous flows — elephant and mice flows — in software-defined networks (SDNs). Most of the researchers considered the homogeneous flows in SDN in the existing literature. The optimal data flow management in the presence of heterogeneous flows is NP-hard. Hence, we propose a game theory-based heterogeneous data flow management scheme, named FlowMan. In FlowMan, initially, we use a generalized Nash bargaining game to obtain a sub-optimal problem, which is NP-complete in nature. By solving it, we get the Pareto optimal solution for data-rate associated with each switch. Thereafter, we use a heuristic method to decide the flow-association with the switches, distributedly, which, in turn, helps to get a Pareto optimal solution. Extensive simulation results depict that FlowMan is capable of ensuring quality-of-service (QoS) for data flow management in the presence of heterogeneous flows. In particular, FlowMan is capable of reducing network delay by 77.8–98.7%, while ensuring 24.6–47.8% increase in network throughput, compared to the existing schemes such as FlowStat and CURE. Additionally, FlowMan ensures that per-flow delay is reduced by 27.7% with balanced load distribution among the SDN switches." @default.
• W3033448214 created "2020-06-12" @default.
• W3033448214 creator A5032787307 @default.
• W3033448214 creator A5039343912 @default.
• W3033448214 date "2020-07-01" @default.
• W3033448214 modified "2023-10-16" @default.
• W3033448214 title "FlowMan: QoS-Aware Dynamic Data Flow Management in Software-Defined Networks" @default.
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• W3033448214 doi "https://doi.org/10.1109/jsac.2020.2999682" @default.
• W3033448214 hasPublicationYear "2020" @default.
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