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Performance of multiclass caching with static proportion in mobile named data network
Yovita L.V.a, Syambas N.R.a, Edward I.Y.M.a
a Bandung Institute of Technology, School of Electrical Engineering and Informatics, Bandung, 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]© 2020 IEEE.Regarding the caching needs of the NDN, traffic can be grouped by paying attention to storage requirements in the content store, such as how often requests come for the content in that class and how long the content in that class needs to be stored. Some previous studies explain content differentiated, but not yet analyze the performance of using the multiclass caching technique in various possible scenarios in detail. This paper will be evaluated and analyzed the performance of the multiclass cache technique with the various possible condition. Multiclass caching discussed in this paper is a caching algorithm that notices the content class distinctions and treats them differently in the content store. Each content class has a different logical cache portion in the content store. The goal of this research is to explore the performance of multiclass caching with the various condition of the system. The simulation result shows the effect of various cache portions, Zipf exponential factor, and the number of routers. It can be concluded that the multiclass cache is proven to accommodate the network performance per class and also the overall network cache hit ratio.[/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]Cache hit ratio,Caching algorithm,Caching technique,Exponential factors,Named data networks,Overall networks,Storage requirements[/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]Caching,Content,Multiclass,Named Data Networking,Partition[/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]This research was partly supported by Telkom University and Ministry of Research Technology and Higher Education Republic of Indonesia through Doctoral Dissertation Research (PDD) program.[/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/10.1109/TSSA51342.2020.9310802[/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]