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An SDN-Based Self-adaptive Resource Allocation Mechanism for Service Customization

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Wireless Algorithms, Systems, and Applications (WASA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12939))

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Abstract

With the emergence of tremendous novel network applications, the user demand on service quality becomes not only higher, but also more diverse. To address these challenges, the Quality of Experience (QoE) and Quality of Service (QoS) should both be improved. In this regard, we intend to offer a fine-grained and customized service model for users with the objective of minimizing the overall cost. In this model, the idea of Software Defined Networking (SDN) is introduced to implement a self-adaptive resource allocation mechanism. Specifically, the proposed mechanism consists of four parts which are the topology management, the resource monitoring, the service customization and the routing management. In particular, the first two parts are used to support service customization and routing, while the third part is used to compose services and the last part is used to achieve the self-adaptive resource allocation. Experimental results show that the proposed mechanism can achieve better performance in terms of the network resource utilization and the average packet loss rate.

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Acknowledgments

This work is supported by the National Key R&D Program of China under Grant No. 2019YFB1802800; the Major International (Regional) Joint Research Project of NSFC under Grant No. 71620107003; the National Natural Science Foundation of China under Grant No. 61872073 and 62002055; the Fundamental Research Funds for the Central Universities of China under Grant No. N2016012; the Postdoctoral Research Fund of Northeastern University of China under Grant No. 20200103.

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Correspondence to Xingwei Wang .

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Dai, Z., Wang, X., Yi, B., Huang, M., Li, Z. (2021). An SDN-Based Self-adaptive Resource Allocation Mechanism for Service Customization. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12939. Springer, Cham. https://doi.org/10.1007/978-3-030-86137-7_21

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  • DOI: https://doi.org/10.1007/978-3-030-86137-7_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86136-0

  • Online ISBN: 978-3-030-86137-7

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