Forecasting Acceleration of Data Transfer with Fog Computing for Resource Efficiency in Data Centers
| Autor: | Niskarto Zendrato, Opim Salim Sitompul, Muhammad Zarlis, Elviawaty Muisa Zamzami |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Multi-core processor
Computer science business.industry Real-time computing Process (computing) 020206 networking & telecommunications Cloud computing 02 engineering and technology Variable (computer science) Acceleration Linear regression 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business Edge computing Data transmission |
| Zdroj: | 2020 International Conference on Data Science, Artificial Intelligence, and Business Analytics (DATABIA). |
| DOI: | 10.1109/databia50434.2020.9190326 |
| Popis: | Accelerate of data transfer always be a problem in fog computing especially workload datacenter This research predicts server performance data on fog computing using linear regression methods. Predictions are made on variables that affect the speed of data transfer namely the number of CPU cores, CPU capacity, memory used based on this variable is used as an attribute and data transfer as a label. With this research the performance of data transfer speeds can be predicted before use. This method provides an improvement in the error value compared of other forecasting methods Thus the process of data transfer in fog computing can be more effective and efficient |
| Databáze: | OpenAIRE |
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