AUTOMATED METHODS AND SYSTEMS FOR PREDICTING BEHAVIOR OF A DISTRIBUTED APPLICATION IN RESPONSE TO A PROPOSED CHANGE TO THE DISTRIBUTED APPLICATION

1 . An automated computer-implemented process for predicting behavior of a distributed application in response to a proposal to add a candidate application component to a distributed computing environment in which the distributed application is executed, the process comprising:

constructing a graph model of the distributed computing environment;

partitioning the graph model into subgraphs based on a rule for partitioning the distributed computing environment into subsystems, wherein each subgraph is a model of a subsystem of objects of the distributed computing environment;

training neural networks for each subgraph based on the proposal to add the candidate application component the corresponding subsystem;

using the trained neural networks associated with each subgraph to predict the behavior of each subsystem to a proposal to add the candidate application to the subsystem; and

based on the predicted behavior of each subsystem, adding the candidate application to a subsystem or rejecting the proposal to add the candidate application.

2 . The process of claim 1 wherein partitioning the graph into subgraphs based on the rule comprises performing one of

partitioning the graph into subgraphs such that each subgraph corresponds to a cluster of the distributed computing environment;

partitioning the graph into subgraphs such that each subgraph corresponds to objects of the distributed computing environment that receive electrical power from the same power source; and

partitioning the graph into subgraphs such that each subgraph comprises correlated objects of the distributed computing environment.

3 . The process of claim 1 wherein training neural networks for each subgraph comprises:

for each subgraph,

for each application component of the subgraph, forming a fragment by aggregating components and metrics of one or more virtual machines used to execute the application component,

forming a candidate fragment by aggregating components and metrics of one or more VMs used to execute the candidate application component,

forming a candidate flattened subgraph from the fragments and the candidate fragment,

training a first neural network to generate a key performance indicators (“KPIs”) for each fragment of the candidate flattened subgraph, and

training a second neural network to generate a KPI of the candidate fragment.

4 . The process of claim 1 wherein using the trained neural networks associated with each subgraph to predict the behavior of each subsystem comprises:

for each subgraph,

using a first of the trained neural networks to generate a predicted KPI for each fragment of the subgraph,

using a second of the trained neural networks to generate a predicted KPI for the candidate application, and

computing an average predicted KPI of the subgraph based on the predicted KPIs of fragments of the subgraph and a predicted KPI.

5 . The process of claim 1 where adding the candidate application component to the subsystem comprises:

computing an average predicted KPI for each subsystem based on the predicted behavior of each subsystem;

for each subsystem comparing the average predicted KPI to an acceptable threshold to identify one or more of the average predicted KPIs as acceptable or unacceptable; and

adding the candidate application component to the subsystem with a corresponding with an average predicted KPI that is more acceptable than the average predicted KPIs of the other subsystems.

6 . The process of claim 1 wherein rejecting the proposal to add the candidate application comprises:

rejecting the candidate application component for addition to the distributed computing environment when the predicted behavior of the subsystems are unacceptable; and

displaying an alert in a graphical user interface, the alert indicating that the candidate application is unacceptable for addition to the distributed computing environment.

7 . A computer system for predicting behavior of a distributed application in response to a proposed change to a distributed computing environment in which the distributed application is executed, the system comprising:

one or more processors;

one or more data-storage devices; and

machine-readable instructions stored in the one or more data-storage devices that when executed using the one or more processors controls the system to perform operations comprising:

constructing a graph model of the distributed computing environment;

partitioning the graph model into subgraphs based on a rule for partitioning the distributed computing environment into subsystems, wherein each subgraph is a model of a subsystem of objects of the distributed computing environment;

training neural networks for each subgraph based on the proposal to add the candidate application component to the distributed computing environment;

using the trained neural networks associated with each subgraph to predict the behavior of each subsystem to a proposal to add the candidate application to the subsystem; and

based on the predicted behavior of each subsystem, adding the candidate application to a subsystem or rejecting the proposal to add the candidate application.

8 . The computer system of claim 7 wherein partitioning the graph into subgraphs based on the rule comprises performing one of

partitioning the graph into subgraphs such that each subgraph corresponds to a cluster of the distributed computing environment;

partitioning the graph into subgraphs such that each subgraph corresponds to objects of the distributed computing environment that receive electrical power from the same power source; and

partitioning the graph into subgraphs such that each subgraph comprises correlated objects of the distributed computing environment.

9 . The computer system of claim 7 wherein training neural networks for each subgraph comprises:

for each subgraph,

for each application component of the subgraph, forming a fragment by aggregating components and metrics of one or more virtual machines used to execute the application component,

forming a candidate fragment by aggregating components and metrics of one or more VMs used to execute the candidate application component,

forming a candidate flattened subgraph from the fragments and the candidate fragment,

training a first neural network to generate a key performance indicators (“KPIs”) for each fragment of the candidate flattened subgraph, and

training a second neural network to generate a KPI of the candidate fragment.

10 . The computer system of claim 7 wherein using the trained neural networks associated with each subgraph to predict the behavior of each subsystem comprises:

for each subgraph,

using a first of the trained neural networks to generate a predicted KPI for each fragment of the subgraph,

using a second of the trained neural networks to generate a predicted KPI for the candidate application, and

computing an average predicted KPI of the subgraph based on the predicted KPIs of fragments of the subgraph and a predicted KPI.

11 . The computer system of claim 7 wherein adding the candidate application component to the subsystem comprises:

computing an average predicted KPI for each subsystem based on the predicted behavior of each subsystem;

for each subsystem comparing the average predicted KPI to an acceptable threshold to identify one or more of the average predicted KPIs as acceptable or unacceptable; and

adding the candidate application component to the subsystem with a corresponding with an average predicted KPI that is more acceptable than the average predicted KPIs of the other subsystems.

12 . The computer system of claim 7 wherein rejecting the proposal to add the candidate application comprises:

rejecting the candidate application component for addition to the distributed computing environment when the predicted behavior of the subsystems are unacceptable; and

displaying an alert in a graphical user interface, the alert indicating that the candidate application is unacceptable for addition to the distributed computing environment.

13 . A non-transitory computer-readable medium encoded with machine-readable instructions that implement a method carried out by one or more processors of a computer system to perform operations comprising:

constructing a graph model of the distributed computing environment;

partitioning the graph model into subgraphs based on a rule for partitioning the distributed computing environment into subsystems, wherein each subgraph is a model of a subsystem of objects of the distributed computing environment;

training neural networks for each subgraph based on the proposal to add the candidate application component to the distributed computing environment;

using the trained neural networks associated with each subgraph to predict the behavior of each subsystem to a proposal to add the candidate application to the subsystem; and

based on the predicted behavior of each subsystem, adding the candidate application to a subsystem or rejecting the proposal to add the candidate application.

14 . The medium of claim 13 wherein partitioning the graph into subgraphs based on the rule comprises performing one of

partitioning the graph into subgraphs such that each subgraph corresponds to a cluster of the distributed computing environment;

partitioning the graph into subgraphs such that each subgraph corresponds to objects of the distributed computing environment that receive electrical power from the same power source; and

partitioning the graph into subgraphs such that each subgraph comprises correlated objects of the distributed computing environment.

15 . The medium of claim 13 wherein training neural networks for each subgraph comprises:

for each subgraph,

for each application component of the subgraph, forming a fragment by aggregating components and metrics of one or more virtual machines used to execute the application component,

forming a candidate fragment by aggregating components and metrics of one or more VMs used to execute the candidate application component,

forming a candidate flattened subgraph from the fragments and the candidate fragment,

training a first neural network to generate a key performance indicators (“KPIs”) for each fragment of the candidate flattened subgraph, and

training a second neural network to generate a KPI of the candidate fragment.

16 . The medium of claim 13 wherein using the trained neural networks associated with each subgraph to predict the behavior of each subsystem comprises:

for each subgraph,

using a first of the trained neural networks to generate a predicted KPI for each fragment of the subgraph,

using a second of the trained neural networks to generate a predicted KPI for the candidate application, and

computing an average predicted KPI of the subgraph based on the predicted KPIs of fragments of the subgraph and a predicted KPI.

17 . The medium of claim 13 wherein adding the candidate application component to the subsystem comprises:

computing an average predicted KPI for each subsystem based on the predicted behavior of each subsystem;

for each subsystem comparing the average predicted KPI to an acceptable threshold to identify one or more of the average predicted KPIs as acceptable or unacceptable; and

adding the candidate application component to the subsystem with a corresponding with an average predicted KPI that is more acceptable than the average predicted KPIs of the other subsystems.

18 . The medium of claim 13 wherein rejecting the proposal to add the candidate application comprises:

rejecting the candidate application component for addition to the distributed computing environment when the predicted behavior of the subsystems are unacceptable; and

displaying an alert in a graphical user interface, the alert indicating that the candidate application is unacceptable for addition to the distributed computing environment.