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Optimal rate allocation for congestion control support in SDN
Hertiana S.N.a, Kurniawan A.a, Hendrawana, Pasaribu U.S.a
a School of Electrical Engineering and Informatics, Bandung, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018, School of Electrical Engineering and Informatics. All rights reserved.In Software-Defined Networking (SDN), even though centralized information on network condition is available at the controller, this information is not used to improve network condition when congestion happens. SDN requires policy embedded in the controller to manage its network, e.g., strategy for network resource allocation. In this paper, we propose an optimal rate allocation schemes to support congestion control in SDN. Congestion control and rate allocation are like two sides of the same coin. Optimal rate allocation can reduce congestion probability, such that a complicated congestion control is not required. This rate allocation is based on mathematical optimization using three optimization criteria, i.e., minimization on mean transmission time, minimization on standard deviation, and allocation based on proportional rate allocation. The minimization problem for mean and standard deviation are solved using Lagrange method, while proportional rate allocation problem is solved using linear equation. The simulation results show that our proposed formula for rate allocation schemes using rate information provides better performance compared to rate allocation schemes without rate information.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Congestion control,Load distribution,Rate allocation,SDN[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]Directorate of Research and Community Service partially supported this research, the General Directorate of Research and Development Strengthening, the Ministry of Research, Technology, and Higher Education of the Republic of Indonesia under the research contract FY 2017 No. 025/SP2HL/LT/DRPM/IV/2017. The first author also expresses her gratitude to Telkom University for supporting this research. We also thank our colleagues from Telkom University who provided insight and expertise that greatly assisted this research.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.15676/ijeei.2018.10.2.4[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]