METHOD FOR BUILDING MACHINE LEARNING MODELS FOR ARTIFICIAL INTELLIGENCE BASED INFORMATION TECHNOLOGY OPERATIONS
A method and system for forecasting resource utilization of an information technology system having a plurality of system components. The method includes classifying the plurality of system components based on at least one resource utilization metric. The method also includes determining at least one reference component in each class from among the components classified within the respective class. The method also includes building a representative machine learning model for each reference component in each class. The method also includes applying the representative machine learning model to all system components within the respective class. Applying the representative machine learning model to all system components within the respective class forecasts the resource utilization of all system components in the information technology system without building a machine learning model for each system component in the information technology system.
1 . A method for forecasting resource utilization of an information technology system having a plurality of system components, the method comprising:
classifying the plurality of system components based on at least one resource utilization metric;
determining at least one reference component in each class from among the components classified within a respective class;
building a representative machine learning model for each reference component in each class; and
for each class, applying the representative machine learning model to all system components within the respective class,
wherein applying the representative machine learning model to all system components within the respective class, for each class, provides forecasts for the resource utilization of all system components in the information technology system without building a machine learning model for each system component in the information technology system.
2 . The method as recited in claim 1 , wherein the at least one resource utilization metric is at least one of CPU utilization, memory utilization or disk storage utilization.
3 . The method as recited in claim 1 , wherein classifying the plurality of system components comprises classifying the plurality of system components based on data distributions of the plurality of system components.
4 . The method as recited in claim 1 , wherein clustering based decisions are used to classifying the plurality of system components.
5 . The method as recited in claim 1 , wherein representative machine learning models are built using at least one Auto Regressive Integrated Moving Average (ARIMA) model.
6 . The method as recited in claim 1 , wherein classifying the plurality of system components comprises classifying each system component into one of an Underutilized class, a Moderate Low class, a Moderate Mid class, a Moderate High class or an Overutilized class.
7 . The method as recited in claim 1 , wherein classifying the plurality of system components further comprises classifying each system component within each class into a sub-class within the respective class.
8 . The method as recited in claim 7 , wherein classifying each system component within each class into a sub-class within the respective class is based on a stationary property of the respective system component.
9 . The method as recited in claim 1 , wherein building a representative machine learning model for each reference component in each class further comprises building a set of representative machine learning models for each reference component in each class.
10 . The method as recited in claim 1 , wherein the plurality of system components includes at least one of a virtual machine device, a server or a server cluster.
11 . An information technology system, comprising:
a host; and
a plurality of system components coupled to the host,
wherein the host is configured to forecast resource utilization of the plurality of system components by:
classifying the plurality of system components based on at least one resource utilization metric,
determining at least one reference component in each class from among the components classified within a respective class,
building a single representative machine learning model for each reference component in each class, and
for each class, applying the single representative machine learning model to all system components within the respective class,
wherein applying the representative machine learning model to all system components within the respective class, for each class, provides forecasts for the resource utilization of all system components in the information technology system without building a machine learning model for each system component in the information technology system.
12 . The system as recited in claim 11 , wherein the at least one resource utilization metric is at least one of CPU utilization, memory utilization or disk storage utilization.
13 . The system as recited in claim 11 , wherein classifying the plurality of system components comprises classifying the plurality of system components based on data distributions of the plurality of system components.
14 . The system as recited in claim 11 , wherein clustering based decisions are used to classifying the plurality of system components.
15 . The system as recited in claim 11 , wherein representative machine learning models are built using at least one Auto Regressive Integrated Moving Average (ARIMA) model.
16 . The system as recited in claim 11 , wherein classifying the plurality of system components comprises classifying each system component into one of an Underutilized class, a Moderate Low class, a Moderate Mid class, a Moderate High class or an Overutilized class.
17 . The system as recited in claim 11 , wherein classifying the plurality of system components further comprises classifying each system component within each class into a sub-class within the respective class.
18 . The system as recited in claim 17 , wherein classifying each system component within each class into a sub-class within the respective class is based on a stationary property of the respective system component.
19 . The system as recited in claim 11 , wherein the plurality of system components includes at least one of a virtual machine device, a server or a server cluster.
20 . The system as recited in claim 11 , wherein the plurality of system components is coupled to the host via at least one network.