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Performance measurement of data flow processing employing software defined architecture
اندازه گیری عملکرد پردازش داده ها با استفاده از معماری تعریف شده نرم افزار-2018 With the development of information technology, the importance of big data is quickly highlighted. Big
data applications show great value to individuals, companies and governments. Recently, researches on
the storage and utilization of big data have achieved considerable results. The prosperity of big data
applications is a thrust of drawing attention to the system performance such as timeliness, computational
and communication resources. Data retransmission caused by the violation of the stringent delay bound
may result in the reprocessing of these data, which would have a negative effect on user experience. To
fill this gap, a software defined architecture is developed in this work so that the appropriate start point of
processing can be found for the data need to be reprocessed. For further improvement of the processing
performance, two models are presented to this software defined architecture. In the optimized model,
a priority queue is employed to facilitate the processing efficiency. In addition, data flows transmitting
through networks exhibit obvious self-similar characteristics. Performance analysis without taking traffic
self-similarity into account may lead to unexpected results. In the optimized model, the tightly coupled
system makes performance analysis difficult. Therefore, a decomposition approach is employed to divide
the coupled system into a group of single server single queue systems. Finally, the developed model is
validated through extensive experimental results.
Keywords: Software defined architecture ، Performance measurement ، Data flow processing ، Queueing system ، Self-similar |
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