DYNAMIC GENERATION ON ENTERPRISE ARCHITECTURES USING CAPACITY-BASED PROVISIONS
The present disclosure is directed to systems and methods for generating an enterprise architecture for an enterprise network. As one example, a method may include: receiving historical information from a plurality of enterprise networks, the historical information comprising information about an enterprise architecture of each of the enterprise networks; analyzing the historical information from the plurality of enterprise networks to generate a network health score for each of the plurality of enterprise networks; training a machine learning model using a plurality of machine learning algorithms based on the historical information and the network health score of each the plurality of enterprise networks; and generating, using the machine learning model, an enterprise architecture for a first enterprise network, the first enterprise network being a new enterprise network or an existing enterprise network from among the plurality of enterprise networks.
1 . A method, comprising:
receiving historical information from a plurality of enterprise networks, the historical information comprising information about an enterprise architecture of each of the plurality of enterprise networks;
analyzing the historical information from the plurality of enterprise networks to generate a network health score for each of the plurality of enterprise networks;
training a machine learning model using a plurality of machine learning algorithms based on the historical information and the network health score of each the plurality of enterprise networks; and
generating, using the machine learning model, an enterprise architecture for a first enterprise network, the first enterprise network being a new enterprise network or an existing enterprise network from among the plurality of enterprise networks.
2 . The method of claim 1 , wherein receiving the historical information comprises continuously receiving the historical information, and wherein the method further comprises:
updating the network health score for each of the plurality of enterprise networks based on the continuously receiving historical information; and
training the machine learning model based on the continuously received historical information and the updated network health scores.
3 . The method of claim 1 , further comprising training the machine learning model to categorize each of the plurality of enterprise networks using a density-based clustering technique.
4 . The method of claim 3 , wherein generating the enterprise architecture for the first enterprise network comprises:
identifying, using the machine learning model, a subset of enterprise networks from among the plurality of enterprise networks with a same category as the first enterprise network;
comparing the first enterprise network to the subset of enterprise networks to identify at least one enterprise network, the comparison being based on one or more parameters for generating the enterprise architecture for the first enterprise network; and
generating the enterprise architecture for the first enterprise network based on the enterprise architecture of the identified at least one enterprise network.
5 . The method of claim 4 , wherein the one or more parameters comprises a budget parameter, a priority parameter, a geographic parameter, and a complexity parameter.
6 . The method of claim 1 , further comprising:
monitoring a performance of the first enterprise network;
calculating a change in the health score for the first enterprise network based on the monitored performance;
determining a cause of the change in the health score; and
generating one or more recommendations for updating the enterprise architecture for the first enterprise network to modify the cause of the change in the health score.
7 . The method of claim 1 , wherein generating the network health score for each of the plurality of enterprise networks comprises generating an overall network health score for each of the plurality of enterprise networks based on a plurality of sub-network health scores.
8 . A device, comprising:
a memory; and
a processor coupled to the memory and configured to:
receive historical information from a plurality of enterprise networks, the historical information comprising information about the enterprise architecture of each of the plurality of enterprise networks;
analyze the historical information from the plurality of enterprise networks to generate a network health score for each of the plurality of enterprise networks;
train a machine learning model using a plurality of machine learning algorithms based on the historical information and the network health score of each the plurality of enterprise networks; and
generate, using the machine learning model, the enterprise architecture for a first enterprise network, the first enterprise network being a new enterprise network or an existing enterprise network from among the plurality of enterprise networks.
9 . The device of claim 8 , wherein receiving the historical information comprises continuously receiving the historical information, and wherein the processor is further configured:
update the network health score for each of the plurality of enterprise networks based on the continuously received historical information; and
train the machine learning model based on the continuously received historical information and the updated network health scores.
10 . The device of claim 8 , wherein the processor is further configured to train the machine learning model to categorize each of the plurality of enterprise networks using a density-based clustering technique.
11 . The device of claim 10 , wherein, to generate the enterprise architecture for the first enterprise network, the processor is further configured to:
identify, using the machine learning model, a subset of enterprise networks from among the plurality of enterprise networks of a same category as the first enterprise network;
compare the first enterprise network to the subset of enterprise networks to identify at least one enterprise network, the comparison being based on one or more parameters for generating the enterprise architecture for the first enterprise network; and
generate the enterprise architecture for the first enterprise network based on the enterprise architecture of the identified at least one enterprise network.
12 . The device of claim 11 , wherein the one or more parameters comprises a budget parameter, a priority parameter, a geographic parameter, and a complexity parameter.
13 . The device of claim 8 , wherein the processor is further configured to:
monitor a performance of the first enterprise network;
calculate a change in the health score for the first enterprise network based on the monitored performance;
determine a cause of the change in the health score; and
generate one or more recommendations for updating the enterprise architecture for the first enterprise network to modify the cause of the change in the health score.
14 . The device of claim 8 , wherein, to generate the network health score for each of the plurality of enterprise networks, the processor is further configured to generate an overall network health score for each of the plurality of enterprise networks based on a plurality of sub-network health scores.
15 . A non-transitory, tangible computer-readable device having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations comprising:
receiving historical information from a plurality of enterprise networks, the historical information comprising information about the enterprise architecture of each of the plurality of enterprise networks;
analyzing the historical information from the plurality of enterprise networks to generate a network health score for each of the plurality of enterprise networks;
training a machine learning model using a plurality of machine learning algorithms based on the historical information and the network health score of each the plurality of enterprise networks; and
generating, using the machine learning model, the enterprise architecture for a first enterprise network, the first enterprise network being a new enterprise network or an existing enterprise network from among the plurality of enterprise networks.
16 . The non-transitory, tangible computer-readable device of claim 15 , wherein receiving the historical information comprises continuously receiving the historical information, and wherein the operations further comprise:
updating the network health score for each of the plurality of enterprise networks based on the continuously received historical information; and
training the machine learning model based on the continuously received historical information and the updated network health scores.
17 . The non-transitory, tangible computer-readable device of claim 15 , the operations further comprising training the machine learning model to categorize each of the plurality of enterprise networks using a density-based clustering technique.
18 . The non-transitory, tangible computer-readable device of claim 17 , wherein generating the enterprise architecture for the first enterprise network comprises:
identifying, using the machine learning model, a subset of enterprise networks from among the plurality of enterprise networks of a same category as the first enterprise network;
comparing the first enterprise network to the subset of enterprise networks to identify at least one enterprise network, the comparison being based on one or more parameters for generating the enterprise architecture for the first enterprise network; and
generating the enterprise architecture for the first enterprise network based on the enterprise architecture of the identified at least one enterprise network.
19 . The non-transitory, tangible computer-readable device of claim 15 , the operations further comprising:
monitoring a performance of the first enterprise network;
calculating a change in the health score for the first enterprise network based on the monitored performance;
determining a cause of the change in the health score; and
generating one or more recommendations for updating the enterprise architecture for the first enterprise network to modify the cause of the change in the health score.
20 . The non-transitory, tangible computer-readable device of claim 15 , wherein generating the network health score for each of the plurality of enterprise networks comprises generating an overall network health score for each of the plurality of enterprise networks based on a plurality of sub-network health scores.