Forecasting Acceleration of Data Transfer with Fog Computing for Resource Efficiency in Data Centers

Abstract

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