Identifying Unused Servers

1 . A method comprising the steps of:

capturing data about activity of a server;

entering the data into a prediction model, which prediction model has been trained, on the basis of a training dataset relating to the activity of reference servers, to distinguish unused reference servers from used reference servers;

receiving a probability value, which probability value represents a probability that the server is an unused server; and

conveying a message to a user, which message comprises information about whether the server is an unused server or a used server.

2 . The method as claimed in claim 1 , wherein the data about the activity of the server is selected from the set comprising: number of processor cores used, outgoing network traffic, incoming network traffic, input/output processes for data storage and/or main-memory utilization.

3 . The method as claimed in claim 1 , wherein the data about the activity of the server is captured over an observation period of at least one day, preferably of at least 5 days, and is fed as a time series into the prediction model.

4 . The method as claimed in claim 1 , wherein the data about the activity of the server is captured over an observation period of at least one day, preferably of at least 5 days, and statistical or other mathematical methods are used to derive values from the time series that are then fed into the prediction model as the activity data.

5 . The method as claimed in claim 4 , wherein the activity data is selected from the set comprising: global maximum, arithmetic mean, longest time period containing values above the mean value, variance, standard deviation and/or Fourier coefficients.

6 . The method as claimed in claim 3 , wherein the data about the activity of the server is captured cyclically over the observation period.

7 . The method as claimed in claim 1 , wherein the prediction model is a classification model, or comprises a classification model, which assigns the server to one of at least two classes.

8 . The method as claimed in claim 1 , wherein the prediction model is a regression model, or comprises a regression model, which calculates for the server a probability that the server is used and/or unused.

9 . The method as claimed in claim 1 , wherein the prediction model has been trained by a supervised learning method to distinguish used servers from unused servers.

10 . The method as claimed in claim 1 , wherein an unused server is a server that can be removed from the network without the processes initiated by users on other servers or clients being adversely affected.

11 . The method as claimed in claim 1 , wherein the steps of the method are executed in an automated manner as a background process on a computer.

12 . A device comprising:

an input unit;

a control and calculation unit; and

an output unit;

wherein the control and calculation unit is configured to cause the input unit to receive data about the activity of a server;

wherein the control and calculation unit is configured to calculate a probability value on the basis of the data about the activity of the server, which probability value represents a probability that the server is an unused server, wherein a prediction model calculates the probability value, which prediction model has been trained, on the basis of a training dataset relating to the activity of reference servers, to distinguish unused reference servers from used reference servers; and

wherein the control and calculation unit is configured to cause the output unit to output to a user the probability value and/or information derived from the probability value.

13 . A device comprising:

an input unit;

a control and calculation unit; and

an output unit;

wherein the control and calculation unit is configured to cause the input unit to receive in an automated manner data about the activity of a multiplicity of servers in a network;

wherein the control and calculation unit is configured to calculate in an automated manner a probability value on the basis of the data about the activity of each server of the multiplicity of servers, which probability value represents a probability that the particular server is an unused server, wherein a prediction model calculates the probability value, which prediction model has been trained, on the basis of a training dataset relating to the activity of reference servers, to distinguish unused reference servers from used reference servers; and

wherein the control and calculation unit is configured to compare in an automated manner, for each server of the multiplicity of servers, the associated probability value with a threshold value, and, if the probability value lies above the threshold value, to cause the output unit to output a message to a user, which message comprises a name and/or an identifier for the servers for which the associated probability value lies above the threshold value.

14 . A non-transitory computer program product comprising a computer program which can be loaded into a main memory of a computer, where it causes the computer to execute automatically the following steps:

receiving data about the activity of a server;

feeding the data to a prediction model, which prediction model has been trained, on the basis of a training dataset relating to the activity of reference servers, to distinguish unused reference servers from used reference servers;

calculating a probability value using the prediction model, which probability value represents a probability that the server is an unused server; and

outputting to a user the probability value and/or information derived from the probability value.

15 . The non-transitory computer program product as claimed in claim 14 , which can be loaded into the main memory of the computer, where it causes the computer to execute automatically in a background process said steps.