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This paper proposes a real-time chain and a novel embedded Markovian queueing model with variable bulk arrival (VBA) and variable bulk service (VBS) in order to establish and assure a theoretical foundation to design a blockchain-based real-time system with particular interest in Ethereum. Based on the proposed model, various performances are simulated in a numerical manner in order to validate the efficacy of the model by checking good agreements with the results against intuitive and typical expectations as a baseline. A demo of the proposed real-time chain is developed in this work by modifying the open source of Ethereum Geth 1.9.11. The work in this paper will provide both a theoretical foundation to design and optimize the performances of the proposed real-time chain, and ultimately address and resolve the performance bottleneck due to the conventional block-synchrony by employing an asynchrony by the real-time deadline to some extent.
Nohpill Park; Abhilash Kancharla; Hye-Young Kim. A Real-Time Chain and Variable Bulk Arrival and Variable Bulk Service (VBAVBS) Model with λF. Applied Sciences 2020, 10, 3651 .
AMA StyleNohpill Park, Abhilash Kancharla, Hye-Young Kim. A Real-Time Chain and Variable Bulk Arrival and Variable Bulk Service (VBAVBS) Model with λF. Applied Sciences. 2020; 10 (10):3651.
Chicago/Turabian StyleNohpill Park; Abhilash Kancharla; Hye-Young Kim. 2020. "A Real-Time Chain and Variable Bulk Arrival and Variable Bulk Service (VBAVBS) Model with λF." Applied Sciences 10, no. 10: 3651.
With increasing content of data that is being created around the globe, there are at times the need for analyzing the data real time. Few of the constraints that come with real-time analysis of such huge amounts of data are time and infrastructure. In cases where time of analyzing the data is a key factor, analysis cannot be done on all of the data that is being generated real-time as the speed of stream overweighs the speed of the processing the same. When time is not that important of a factor, it calls upon a very high end infrastructure to process heavy incoming traffic of data. In such scenarios where the entire population (real-time streaming data) cannot be analyzed and cases where the prior information about the population size is not available, Sampling of the population can be used as an effective technique and the processing can be done on sampled data by maintaining possible error at the least.
Abhilash Kancharla; Jongyeop Kim; Noh-Jin Park; Nohpill Park. Big Streaming Data Buffering Optimization. 2016 4th Intl Conf on Applied Computing and Information Technology/3rd Intl Conf on Computational Science/Intelligence and Applied Informatics/1st Intl Conf on Big Data, Cloud Computing, Data Science & Engineering (ACIT-CSII-BCD) 2016, 218 -223.
AMA StyleAbhilash Kancharla, Jongyeop Kim, Noh-Jin Park, Nohpill Park. Big Streaming Data Buffering Optimization. 2016 4th Intl Conf on Applied Computing and Information Technology/3rd Intl Conf on Computational Science/Intelligence and Applied Informatics/1st Intl Conf on Big Data, Cloud Computing, Data Science & Engineering (ACIT-CSII-BCD). 2016; ():218-223.
Chicago/Turabian StyleAbhilash Kancharla; Jongyeop Kim; Noh-Jin Park; Nohpill Park. 2016. "Big Streaming Data Buffering Optimization." 2016 4th Intl Conf on Applied Computing and Information Technology/3rd Intl Conf on Computational Science/Intelligence and Applied Informatics/1st Intl Conf on Big Data, Cloud Computing, Data Science & Engineering (ACIT-CSII-BCD) , no. : 218-223.