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Enhanced Rough K-Means Based Flow Aggregation for QoS Mapping in Heterogeneous Network Environments | IEEE Journals & Magazine | IEEE Xplore

Enhanced Rough K-Means Based Flow Aggregation for QoS Mapping in Heterogeneous Network Environments


Abstract:

Flow aggregation is capable of reducing the burden on core routing and provide the scalability of network. Among the state-of-the-art methods of QoS (Quality of Service) ...Show More

Abstract:

Flow aggregation is capable of reducing the burden on core routing and provide the scalability of network. Among the state-of-the-art methods of QoS (Quality of Service) mapping, the existing schemes lack effective means to guarantee the QoS of Internet flows in variable network environment. To tackle this problem, in this paper, we design a dynamic flow aggregation method that uses an enhanced rough {k} -means algorithm (ERKM) to properly aggregate network flows and perform flexible QoS mapping across different networks. More importantly, it is able to adjust the allocation of flows according to the degree of membership in ever-changing Internet environment, and control the distance threshold parameter th to make the algorithm more flexible. The experimental results suggest that the proposed method outperforms other methods in terms of QoS support in the high-load and ever-changing networks. Moreover, it is validated that the proposed method can ensure the consistency and flexibility of QoS class mapping.
Published in: IEEE Transactions on Network and Service Management ( Volume: 17, Issue: 2, June 2020)
Page(s): 1197 - 1210
Date of Publication: 27 January 2020

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