Linear Regression in Network Traffic Forecasting

11 Oct 2018
by Febi

Network monitoring tools are used to help human labor to monitor network performance. The network needs to be maintained so that its performance continues to be stable so that the company can continue to reach high availability and can continue to satisfy customers. Like articles that have been discussed. Without the help of network monitoring, network engineers will be overwhelmed to monitor many networks because there are not many network engineers.

There are many network monitoring tools that can provide information about a device’s data traffic. The company must be careful in choosing network monitoring tools because it also affects the managerial domain. There is one advantage that an Indonesian network monitoring tool called NetMonk has. The advantage is that NetMonk can provide predictions when the network will reach its threshold which is supported by network traffic forecasting features.

If the device is predictable, it can be said that a network device will have increased performance so that it can also support work performance management levels. Network traffic forecasting is supported by a method to be able to process analytic data into accurate information about the condition of the device.

Lots of traffic forecasting methods that are used to run network traffic forecasting. Of course, in developing network traffic forecasting, NetMonk looks for suitable methods and can be applied to NetMonk. One method implemented in NetMonk to predict the time of a device to reach a threshold is a linear regression method.

NetMonk utilizes outbound and incoming traffic data to display network traffic forecasting features on the dashboard. Or commonly called upstream and downstream data. Downstream is the speed of data flow from another computer to a local computer (download), while upstream is the speed of data flow from a local computer to another computer (upload).

In network traffic forecasting the regression line becomes a benchmark in understanding data behavior, taken from the average historical data that is limited by the maximum and minimum data limits around the average. So that the presentation of network traffic forecasting data in NetMonk is in the form of a line indicating the time a network reaches the threshold. The line consists of X axis and Y axis. The x-axis is time data, and Y-axis is upstream or downstream data which is limited by maximum threshold number. Based on the X axis and Y axis, you will see when a device reaches the maximum threshold number.

Using NetMonk, means you have saved energy and time. It doesn’t take long to know that your network will reach a threshold and you can anticipate it. Find other unique values of NetMonk in the article.