Enhanced selection of cloud architecture profiles

Patent metadata

Field	Value
Publication number	US-11050677-B2
Application number	US-202016880591-A
Country	US
Kind code	B2
Filing date	May 21, 2020
Priority date	Nov 22, 2019
Publication date	Jun 29, 2021
Grant date	Jun 29, 2021

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 specifying utilization of computing resources of an existing computing system comprising multiple computers connected by a network;
obtaining application workload data specifying tasks performed by one or more applications running on the existing computing system;
generating, based on the infrastructure data and the application workload data, one or more models that define an impact on utilization of each computing resource in response to changes in one or more workloads of the one or more applications;
receiving data specifying an input simulation workload defining a quantity of the tasks to be performed by the one or more applications;
selecting a candidate cloud architecture profile based at least in part on a user-selected cloud provider, wherein the candidate cloud architecture profile specifies a set of cloud computing resources of the cloud provider and, for each computing resource, a quantity of the computing resource;
simulating, using the one or more models, performing the tasks of the input simulation workload on the candidate cloud architecture profile;
determining, based on the simulation, a simulated utilization of each computing resource of the candidate cloud architecture profile for one or more time periods; 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 generating and providing the updated cloud architecture profile comprises determining, based on the utilization of one or more computing resources and a respective scaling baseline for each of the one or more computing resources, to adjust the quantity of the one or more computing resources in the updated cloud architecture profile.

3. The method of claim 2 , wherein determining, based on the utilization of one or more computing resources and the respective scaling baseline for each of the one or more computing resources, to adjust the quantity of the one or more computing resources in the updated cloud architecture profile comprises:
determining, for a particular computing resource of the one or more computing resources, a contention factor and a cohesion factor; and
determining, based on the contention factor, the cohesion factor, and the utilization of the particular computing resource, to scale the particular computing resource horizontally by increasing the quantity of the particular computing resource in the updated cloud architecture profile.

4. The method of claim 3 , wherein determining to scale the particular computing resource horizontally by increasing the quantity of the particular computing resource comprises:
scaling the particular computing resource horizontally by increasing performance of the particular computing resource;
determining that a saturation point for scaling the particular computing resource horizontally has been reached; and
scaling the particular computing resource vertically in response to determining that the saturation point has been reached.

5. The method of claim 2 , wherein the respective scaling baseline for a particular computing resource comprises a maximum utilization of the particular computing resource.

6. The method of claim 2 , wherein determining, based on the utilization of one or more computing resources and the respective scaling baseline for each of the one or more computing resources, to adjust the quantity of the one or more computing resources in the updated cloud architecture profile comprises:
determining to adjust the quantity of a particular computing resource of the one or more computing resources based on a modeler rule for the particular computing resource, wherein the modeler rule defines scaling of the particular computing resource based on a respective quantity of two or more different types of tasks of the workload data.

7. 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 specifying utilization of computing resources of an existing computing system comprising multiple computers connected by a network;
obtaining application workload data specifying tasks performed by one or more applications running on the existing computing system;
generating, based on the infrastructure data and the application workload data, one or more models that define an impact on utilization of each computing resource in response to changes in one or more workloads of the one or more applications;
receiving data specifying an input simulation workload defining a quantity of the tasks to be performed by the one or more applications;
selecting a candidate cloud architecture profile based at least in part on a user-selected cloud provider, wherein the candidate cloud architecture profile specifies a set of cloud computing resources of the cloud provider and, for each computing resource, a quantity of the computing resource;
simulating, using the one or more models, performing the tasks of the input simulation workload on the candidate cloud architecture profile;
determining, based on the simulation, a simulated utilization of each computing resource of the candidate cloud architecture profile for one or more time periods; 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.

8. The system of claim 7 , wherein generating and providing the updated cloud architecture profile comprises determining, based on the utilization of one or more computing resources and a respective scaling baseline for each of the one or more computing resources, to adjust the quantity of the one or more computing resources in the updated cloud architecture profile.

9. The system of claim 8 , wherein determining, based on the utilization of one or more computing resources and the respective scaling baseline for each of the one or more computing resources, to adjust the quantity of the one or more computing resources in the updated cloud architecture profile comprises:
determining, for a particular computing resource of the one or more computing resources, a contention factor and a cohesion factor; and
determining, based on the contention factor, the cohesion factor, and the utilization of the particular computing resource, to scale the particular computing resource horizontally by increasing the quantity of the particular computing resource in the updated cloud architecture profile.

10. The system of claim 9 , wherein determining to scale the particular computing resource horizontally by increasing the quantity of the particular computing resource comprises:
scaling the particular computing resource horizontally by increasing performance of the particular computing resource;
determining that a saturation point for scaling the particular computing resource horizontally has been reached; and
scaling the particular computing resource vertically in response to determining that the saturation point has been reached.

11. The system of claim 8 , wherein the

Assignees

• Accenture Global Solutions Ltd

Inventors

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

Classifications

• H04L41/149

  for prediction of maintenance · CPC title

• H04L41/147

  for predicting network behaviour · CPC title

• H04L41/0897

  by horizontal or vertical scaling of resources, or by migrating entities, e.g. virtual resources or entities · CPC title

• G06F9/5027

  the resource being a machine, e.g. CPUs, Servers, Terminals · CPC title

• H04L67/10

  in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46) · CPC title