Efficient Flow Table Management Scheme in SDN-Based Cloud Computing Networks


Nambong Ha, Namgi Kim, Journal of Information Processing Systems Vol. 14, No. 1, pp. 228-238, Jan. 2018  

10.3745/JIPS.01.0023
Keywords: Cloud Computing Network, Cloud Service, Flow Table, SDN
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Abstract

With the rapid advancement of Internet services, there has been a dramatic increase in services that dynamically provide Internet resources on demand, such as cloud computing. In a cloud computing service, because the number of users in the cloud is changing dynamically, it is more efficient to utilize a flexible network technology such as software-defined networking (SDN). However, to efficiently support the SDNbased cloud computing service with limited resources, it is important to effectively manage the flow table at the SDN switch. Therefore, in this paper, a new flow management scheme is proposed that is able to, through efficient management, speed up the flow-entry search speed and simultaneously maximize the number of flow entries. The proposed scheme maximizes the capacity of the flow table by efficiently storing flow entry information while quickly executing the operation of flow-entry search by employing a hash index. In this paper, the proposed scheme is implemented by modifying the actual software SDN switch and then, its performance is analyzed. The results of the analysis show that the proposed scheme, by managing the flow tables efficiently, can support more flow entries


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Cite this article
[APA Style]
Nambong Ha and Namgi Kim (2018). Efficient Flow Table Management Scheme in SDN-Based Cloud Computing Networks. Journal of Information Processing Systems, 14(1), 228-238. DOI: 10.3745/JIPS.01.0023.

[IEEE Style]
N. Ha and N. Kim, "Efficient Flow Table Management Scheme in SDN-Based Cloud Computing Networks," Journal of Information Processing Systems, vol. 14, no. 1, pp. 228-238, 2018. DOI: 10.3745/JIPS.01.0023.

[ACM Style]
Nambong Ha and Namgi Kim. 2018. Efficient Flow Table Management Scheme in SDN-Based Cloud Computing Networks. Journal of Information Processing Systems, 14, 1, (2018), 228-238. DOI: 10.3745/JIPS.01.0023.