Systems, apparatuses, methods, and computer program products for determining a services resource consumption
Systems, apparatuses, methods, and computer program products are provided. For example, a computer-implemented method provided herein may include receiving a system architecture. In some embodiments, the system architecture is representative of a plurality of services and a plurality of services metadata datasets. In some embodiments, the computer-implemented method may include parsing the system architecture to generate a plurality of services resource consumption representation requests. In some embodiments, the computer-implemented method may include processing the plurality of services resource consumption representation requests using a plurality of services resource consumption utilization tools. In some embodiments, the computer-implemented method may include receiving a plurality of services resource consumption responses from the plurality of services resource consumption utilization tools. In some embodiments, the computer-implemented method may include determining a services resource consumption based at least in part on the plurality of services resource consumption responses.
1 . A computer-implemented method comprising:
receiving a system architecture, wherein the system architecture is representative of a plurality of services and a plurality of services metadata datasets, wherein each of the plurality of services is associated with one of the plurality of services metadata datasets;
parsing the system architecture to generate a plurality of services resource consumption representation requests;
processing the plurality of services resource consumption representation requests using a plurality of services resource consumption utilization tools;
receiving a plurality of services resource consumption responses from the plurality of services resource consumption utilization tools;
determining a services resource consumption based at least in part on the plurality of services resource consumption responses; and
wherein the computer-implemented method further comprising:
causing, via a user interface, generation and display of the system architecture by:
displaying an architecture component interface comprising a plurality of architecture components each representative of the plurality of services;
displaying an architecture modeling interface;
generating a plurality of architecture component requests, each comprising an indication of a selection of one of the plurality of architecture components corresponding to one of the plurality of services and one of the plurality of services metadata datasets;
providing the plurality of architecture component requests to a system architecture generation tool; and
displaying an updated architecture modeling interface comprising the system architecture, based at least in part on the plurality of architecture component requests.
2 . The computer-implemented method of claim 1 , wherein parsing the system architecture to generate the plurality of services resource consumption representation requests is performed by a natural language processing machine learning model.
3 . The computer-implemented method of claim 2 , further comprising:
receiving a historical plurality of system architectures each of the historical plurality of system architectures representative of a historical plurality of services and a historical plurality of services metadata datasets, wherein each of the historical plurality of services is associated with one of the historical plurality of services metadata datasets; and
training the natural language processing machine learning model based at least in part on the historical plurality of system architectures.
4 . The computer-implemented method of claim 1 , wherein at least a portion of the plurality of services metadata datasets is generated by a natural language processing machine learning model parsing a plurality of non-functional specifications associated with the plurality of services.
5 . The computer-implemented method of claim 4 , further comprising:
receiving a historical plurality of non-functional specifications associated with a historical plurality of services; and
training the natural language processing machine learning model based at least in part on the historical plurality of non-functional specifications.
6 . The computer-implemented method of claim 1 , wherein a first service of the plurality of services is associated with a first services resource consumption utilization tool of the plurality of services resource consumption utilization tools and a second service of the plurality of services is associated with a second services resource consumption utilization tool of the plurality of services resource consumption utilization tools.
7 . The computer-implemented method of claim 1 , wherein each of the plurality of services resource consumption representation requests are associated with one of a plurality of configurations.
8 . The computer-implemented method of claim 1 , wherein each of the plurality of services metadata datasets comprises one or more of a computing resource consumption metadata, services type metadata, usage metadata, user type metadata, regional deployment infrastructure metadata, or user identification metadata.
9 . The computer-implemented method of claim 1 , wherein the system architecture is representative of a computing system comprising a plurality of services and a plurality of connections between the plurality of services.
10 . An apparatus comprising at least one processor and at least one memory coupled to the at least one processor, wherein the at least one processor is configured to:
receive a system architecture, wherein the system architecture is representative of a plurality of services and a plurality of services metadata datasets, wherein each of the plurality of services is associated with one of the plurality of services metadata datasets;
parse the system architecture to generate a plurality of services resource consumption representation requests;
process the plurality of services resource consumption representation requests using a plurality of services resource consumption utilization tools;
receive a plurality of services resource consumption responses from the plurality of services resource consumption utilization tools;
determine a services resource consumption based at least in part on the plurality of services resource consumption responses; and
wherein the at least one processor is further configured to:
cause, via a user interface, generation and display of the system architecture by:
displaying an architecture component interface comprising a plurality of architecture components each representative of the plurality of services;
displaying an architecture modeling interface;
generating a plurality of architecture component requests, each comprising an indication of a selection of one of the plurality of architecture components corresponding to one of the plurality of services and one of the plurality of services metadata datasets;
providing the plurality of architecture component requests to a system architecture generation tool; and
displaying an updated architecture modeling interface comprising the system architecture, based at least in part on the plurality of architecture component requests.
11 . The apparatus of claim 10 , wherein parsing the system architecture to generate the plurality of services resource consumption representation requests is performed by a natural language processing machine learning model.
12 . The apparatus of claim 11 , wherein the at least one processor is configured to:
receive a historical plurality of system architectures each of the historical plurality of system architectures representative of a historical plurality of services and a historical plurality of services metadata datasets, wherein each of the historical plurality of services is associated with one of the historical plurality of services metadata datasets; and
train the natural language processing machine learning model based at least in part on the historical plurality of system architectures.
13 . The apparatus of claim 10 , wherein at least a portion of the plurality of services metadata datasets is generated by a natural language processing machine learning model parsing a plurality of non-functional specifications associated with the plurality of services.
14 . The apparatus of claim 13 , wherein the at least one processor is configured to:
receive a historical plurality of non-functional specifications associated with a historical plurality of services; and
train the natural language processing machine learning model based at least in part on the historical plurality of non-functional specifications.
15 . The apparatus of claim 10 , wherein a first service of the plurality of services is associated with a first services resource consumption utilization tool of the plurality of services resource consumption utilization tools and a second service of the plurality of services is associated with a second services resource consumption utilization tool of the plurality of services resource consumption utilization tools.
16 . The apparatus of claim 10 , wherein each of the plurality of services resource consumption representation requests are associated with one of a plurality of configurations.
17 . The apparatus of claim 10 , wherein each of the plurality of services metadata datasets comprises one or more of a computing resource consumption metadata, services type metadata, usage metadata, user type metadata, regional deployment infrastructure metadata, or user identification metadata.
18 . A non-transitory computer-readable storage medium comprising computer program code for execution by one or more processors of a device, the computer program code configured to, when executed by the one or more processors, cause the device to:
receive a system architecture, wherein the system architecture is representative of a plurality of services and a plurality of services metadata datasets, wherein each of the plurality of services is associated with one of the plurality of services metadata datasets;
parse the system architecture to generate a plurality of services resource consumption representation requests;
process the plurality of services resource consumption representation requests using a plurality of services resource consumption utilization tools;
receive a plurality of services resource consumption responses from the plurality of services resource consumption utilization tools;
determine a services resource consumption based at least in part on the plurality of services resource consumption responses; and
the computer program code is further configured to, when executed by the one or more processors, further cause the device to:
cause, via a user interface, generation and display of the system architecture by:
display an architecture component interface comprising a plurality of architecture components each representative of the plurality of services;
display an architecture modeling interface;
generate a plurality of architecture component requests, each comprising an indication of a selection of one of the plurality of architecture components corresponding to one of the plurality of services and one of the plurality of services metadata datasets;
provide the plurality of architecture component requests to a system architecture generation tool; and
display an updated architecture modeling interface comprising the system architecture, based at least in part on the plurality of architecture component requests.