US 12,222,849 B2Granted

Infrastructure refactoring via fuzzy upside down reinforcement learning

AssigneeBank of America Corporation

InventorsMadhu Sudhanan Krishnamoorthy, Sreeram Raghavan, Rajarajan Pandiyan

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Abstract

Apparatus and methods for refactoring infrastructure. The methods may include (a) defining parameters of an application landscape. The methods may include (b) stress-testing an application in a simulated environment based on: the parameters; and a simulated input to the application. The methods may include (c) identifying a state of stress of the application based on output of the stress-test. The methods may include (d) repeating (b)-(c) with a different simulated input until the state of stress satisfies a predetermined stochastic threshold. The methods may include (e) providing the state of stress to an upside down reinforcement learning (“UDRL”) engine. The methods may include (f) comparing a throughput corresponding to the state of stress to a benchmark throughput. The methods may include (g) redefining the parameters. The methods may include (h) repeating (a)-(f) until a threshold proximity to the benchmark throughput is reached.

Claims21

Dependent claims

A method for improving application throughput by refactoring infrastructure, the method comprising:

(a) defining a set of parameters of an application landscape;
(b) stress-testing an application in a simulated environment based on:
the parameters;
and
a simulated input to the application;
the stress-testing comprising (1) generating a simulated hardware environment, (2) instantiating the application in the simulated hardware environment, (3) porting in the application landscape parameters, and (4) detecting an output comprising an application throughput;
(c) identifying an initial state of stress of the application based on output of the stress-test;
(d) generating an updated state of stress based on linear combinations of fuzzified basis vectors based on the initial state of stress, the fuzzified basis vectors based on the application landscape parameters;
(e) repeating, sequentially, (b) and (d), with a new application parameter in the set of parameters, and the initial state of stress replaced by the latest updated state of stress, until the updated state of stress satisfies a predetermined stochastic threshold;
(f) providing the updated state of stress to an upside down reinforcement learning (“UDRL”) engine;
(g) comparing a throughput corresponding to the updated state of stress to a benchmark throughput;
(h) re-weighting the parameters;
and
(i) repeating (a)-(i) until a threshold proximity to the benchmark throughput is reached and outputting a set of parameters and weights associated with the benchmark throughput.

11 more elements

A method for improving application throughput by refactoring infrastructure, the method comprising:

defining a reward value corresponding to a first application landscape parameter and a reward value corresponding to a second application landscape parameter;
receiving from an application stress-test agent a first stress-state that:
corresponds to performance of an application under test, the test comprising generating a simulated hardware environment, instantiating the application in the simulated hardware environment, porting in the first application landscape parameter, and detecting an output comprising a first application throughput;
and
includes the first application landscape parameter having a parameter value;
based on a comparison between the reward value and the parameter value, instructing the agent to generate a new stress-state that:
corresponds to performance of the application under test, the test comprising generating a simulated hardware environment, instantiating the application in the simulated hardware environment, porting in the second application landscape parameter, and detecting an output comprising a second application throughput;
and
includes the second application landscape parameter;
the new stress-state based on linear combinations of fuzzified basis vectors, the fuzzified basis vectors based on the first application landscape parameter.

7 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/246,801Filed 3 May 2021
Published: US 2022/0350732 A13 Nov 2022
Granted: 11 Feb 2025
Term ends: Not estimatedThe source records don’t support an estimate.

Classification13 codes

G06F 11/3688
Test management: for test execution, e.g. scheduling of test suites
G06F 11/3457
Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of…:…
G06F 11/3616
Analysis of software for verifying properties of programs: using software metrics
G06F 11/3698
Prevention of errors by analysis, debugging or testing of software: environments for analysis, debugging or testing of software

Family

US 2022/0350732 A1Pre-grant publication · published 3 Nov 2022
US 12,222,849 B2This patent

From the US filing history. Foreign family members aren’t listed.

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