Enhanced selection of cloud architecture profiles

Patent metadata

Field	Value
Publication number	US-11700210-B2
Application number	US-202117238514-A
Country	US
Kind code	B2
Filing date	Apr 23, 2021
Priority date	Nov 22, 2019
Publication date	Jul 11, 2023
Grant date	Jul 11, 2023

Abstract

This document describes modeling and simulation techniques to select a cloud architecture profile based on correlations between application workloads and resource utilization. In some aspects, a method includes obtaining infrastructure data specifying utilization of computing resources of an existing computing system. Application workload data specifying tasks performed by one or more applications running on the existing computing system is obtained. One or more models are generated based on the infrastructure data and the application workload data. The model(s) define an impact on utilization of each computing resource in response to changes in workloads of the application(s). A workload is simulated, using the model(s), on a candidate cloud architecture profile that specifies a set of computing resources. A simulated utilization of each computing resource of the candidate cloud architecture profile is determined based on the simulation. An updated cloud architecture profile is generated based on the simulated utilization.

First claim

What is claimed is:

1. A method performed by one or more data processing apparatus, the method comprising:
obtaining infrastructure data indicating utilization of computing resources of an existing computing system comprising multiple computers connected by a network;
obtaining application workload data indicating tasks performed by one or more applications running on the existing computing system;
generating, based on patterns between the utilization of the computing resources and the tasks performed by the one or more applications, one or more models that define an impact on utilization of each computing resource in response to changes in an actual level of one or more workloads of the one or more applications;
receiving data specifying an input simulation workload level and a candidate cloud architecture profile comprising a set of cloud computing resources of a cloud provider;
simulating, using the one or more models, performing a quantity of tasks corresponding to the input simulation workload level on the candidate cloud architecture profile;
determining, based on the simulating, a simulated utilization of each cloud computing resource of the candidate cloud architecture profile; and
generating and providing an updated cloud architecture profile based on the simulated utilization of each computing resource of the candidate cloud architecture profile, wherein the updated cloud architecture profile specifies an updated set of cloud computing resources of the cloud provider.

2. The method of claim 1 , wherein:
the infrastructure data comprises, for each of the computing resources of the existing computing system, a series of utilization values that each indicate a utilization of the computing resource and, for each utilization value, a first timestamp indicating a time at which the utilization value was generated; and
the application workload data comprises one or more series of tasks performed by the one or more applications running on the existing computing system and, for each task, a second timestamp indicating a time at which the task was performed and an actual level of one or more workloads for the one or more applications at the time at which the task was performed.

3. The method of claim 2 , further comprising detecting, based on the infrastructure data and the application workload data, the patterns between the utilization of the computing resources and the tasks in the one or more series of tasks, the detecting comprising detecting a pattern between the utilization of a particular computing resource, an occurrence of one or more particular tasks, and a quantity of instances of the particular task being performed.

4. The method of claim 2 , further comprising detecting, based on the infrastructure data and the application workload data, the patterns between the utilization of the computing resources and the tasks in the one or more series of tasks, the detecting comprising detecting a pattern between the utilization of a particular computing resource, one or particular types of tasks, and a quantity of instances of each particular type task being performed.

5. The method of claim 2 , wherein generating and providing the updated cloud architecture profile based on the simulated utilization of each computing resource of the candidate cloud architecture profile comprises predicting infrastructure requirements for peak workload requirements using the patterns between the utilization of the computing resources and the tasks performed by the one or more applications and the simulated utilization of each computing resource of the candidate cloud architecture profile.

6. The method of claim 2 , further comprising detecting, as patterns between the utilization of the computing resources and the tasks in the one or more series of tasks, a first range of a number of user tasks performed during each particular time period of a plurality of time periods, a second or range of a number of business tasks performed during the particular time period, and respective utilization ranges for each computing resources during the particular time period.

7. The method of claim 1 , wherein generating, based on the patterns between the utilization of the computing resources and the tasks performed by the one or more applications, the one or more models that define the impact on utilization of each computing resource in response to changes in the actual level of the one or more workloads of the one or more applications comprises using the patterns to model an effect of a chance in a level of each workload on the utilization of each computing resource.

8. The method of claim 1 , further comprising detecting, as the patterns,
correlations between the utilization of the computing resources with user tasks and business
transaction tasks at workloads for which a total number of tasks performed by the one or more applications exceeds an average number of tasks performed by the one or more applications.

9. The method of claim 1 , further comprising selecting the candidate cloud architecture profile based at least in part on a user-selected cloud provider, wherein the candidate cloud architecture specifies, for each cloud computing resource in the set of cloud computing resources, a quantity of the cloud computing resource.

10. The method of claim 1 , further comprising causing a client device of a user to display a user interface comprising a user interface control that enables the user to adjust the input simulation workload level, wherein receiving the data specifying the input simulation workload level comprises detecting the input simulation workload level based on the user interface control.

11. The method of claim 1 , wherein the one or more models comprises:
a first user workload model that defines a first impact on the utilization of each computing resource in response to changes in a number of users of the one or more applications; and
a second user workload model that defines a second impact on the utilization of each computing resource in response to changes in a number of business tasks performed by the one or more applications.

12. The method of claim 1 , wherein generating the one or more models comprises:
classifying each task into a respective workload category of a set of specified workload categories; and
generating, based on the classifications, scalability rules for scaling the computing resources based on simulation of tasks of a given workload category.

13. The method of claim 1 , wherein generating an updated cloud architecture profile comprises using an enhancement technique that predicts that one or more particular conditions will occur and scales one or more of the cloud computing resources to reduce a likelihood of an occurrence of the one or more particular conditions.

14. A system, comprising:
one or more computers; and
one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform operations comprising:
obtaining infrastructure data indicating utilization of computing resources of an existing computing system comprising multiple computers connected by a network;
obtaining application workload data indicating tasks performed by one or more applications running on the existing computing system;
generating, based on patterns between the utilization of the computing resources and the tasks performed by the one or more applications, one or more models that define an impact on utilization of each computing resource in response to changes in

Assignees

• Accenture Global Solutions Ltd

Inventors

• Ghosh Bhaskar
• Durg Kishore P
• Gouthaman Jothi
• Golden Radhika
• SEKHAR Mohan
• Venkataraman Mahesh

Classifications

• H04L47/828

  Allocation of resources per group of connections, e.g. per group of users · CPC title

• H04L47/788

  Autonomous allocation of resources · CPC title

• H04L47/745

  Reaction in network · CPC title

• H04L47/762 Primary

  triggered by the network · CPC title

• H04L47/748 Primary

  Negotiation of resources, e.g. modification of a request · CPC title