Optimizing application availability

• US 10,084,662 B2
• Filed: 09/21/2016
• Issued: 09/25/2018
• Est. Priority Date: 01/06/2014
• Status: Active Grant

• Current Assignee: Kyndryl Incorporated (Kyndryl Holdings, Inc.)
• Original Assignee: International Business Machines Corporation
• Inventors: Hladik, Jr., William J.
• Primary Examiner(s): Patel, Kamini B
• Application Number:
  US15/271,258
• Publication Number:
  US 20170012832A1
• Time in Patent Office: 734 Days
• Field of Search: 714 473
• CPC Class Codes: G06F 11/008 Reliability or availability analysis
• CPC Class Codes: G06F 11/0709 in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
• CPC Class Codes: G06F 11/0751 Error or fault detection not based on redundancy power supply failures G06F1/30; network fault management H04L41/06
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36
• CPC Class Codes: H04L 41/00 Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
• CPC Class Codes: H04L 41/0613 based on the type or category of the network elements
• CPC Class Codes: H04L 41/0618 based on the physical or logical position
• CPC Class Codes: H04L 41/065 involving logical or physical relationship, e.g. grouping and hierarchies
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 43/091 Measuring contribution of individual network components to actual service level

Abstract

An approach to an optimal application configuration. The approach includes a method that includes computing, by at least one computing device, an actual application impact based on an “N” number of failing information technology (IT) infrastructure components within an application architecture. The method includes determining, by the at least one computing device, a factor in likelihood of failure of the “N” number of IT infrastructure components. The method includes determining, by the at least one computing device, a failure profile for the application architecture based on the actual application impact and the factor in likelihood of failure.

Claims

1. A method comprising:

computing, by at least one computing device, an actual application impact based on an “

N”

number of failing information technology (IT) infrastructure components within an application architecture;

determining, by the at least one computing device, a factor in likelihood of failure of the “

N”

number of IT infrastructure components; and

simulating, by the at least one computing device, additional features based on other IT infrastructure components within the application architecture,

wherein the “

N”

number of failing IT infrastructure components include application components that include feed servers, web load balanced servers, and application servers,

the computing of the actual application impact includes analyzing a vertical application layer and calculating throughput volume in the vertical application layer,

the calculating the throughput volume in the vertical application layer comprises subtracting a product of a previous layer throughput volume and a current level fail percentage from the previous layer throughput volume,

the vertical application layer comprises a proxy server layer and a database server layer, and

the other IT infrastructure components comprises a logical partition (LPAR) which is a virtual partition that partitions information into different locations within a device.

2. The method of claim 1, wherein the computing of the actual application impact includes analyzing the “

N”

number of IT infrastructure components across different layers in the application architecture.

3. The method of claim 1, further comprising determining, by the at least one computing device, a failure profile for the application architecture based on the actual application impact and the factor in likelihood of failure,

wherein:

the determining the failure profile includes calculating a chance of failure average for the “

N”

number of failing IT infrastructure components and a product failure average for the “

N”

number of failing IT infrastructure components, and

each product failure value for one or more of the “

N”

number of IT infrastructure components is determined by taking the product of the computed actual application impact and the chance of failure value for the one or more of the “

N”

number of IT infrastructure components.

4. The method of claim 1, further comprising computing a cost profile for the application architecture.

5. The method of claim 4, wherein the computing the cost profile includes determining a cost ratio and a cost influence.

6. The method of claim 1, further comprising computing a protection profile, which determines diversification of application components stored on the “

N”

number of IT infrastructure components.

7. The method of claim 6, wherein the computing the protection profile includes determining a minimum level of protection and a maximum level of protection.

8. The method of claim 1, wherein the “

N”

number of IT infrastructure components further includes a site, a building, a floor, a system, a logical partition (LPAR), and an operating system (OS) instance.

9. The method of claim 1, further comprising outputting a graphical display of a network that indicates a relationship between the “

N”

number of IT infrastructure components.

10. The method of claim 1, wherein the “

N”

number of IT infrastructure components have identifiers that are used to relate the “

N”

number of IT infrastructure components to one another.

11. The method of claim 1, wherein a particular IT infrastructure component has clustered nodes that handle the operation of the particular IT infrastructure component.

12. The method of claim 1, further comprising:

computing, by the at least one computing device, the actual application impact based on the other IT infrastructure components within the application architecture; and

determining, by the at least one computing device, the factor in likelihood of failure of the other IT infrastructure components within the application architecture.

13. The method of claim 12, wherein the simulating the additional features based on the other IT infrastructure components comprise iterating through the other IT infrastructure components.

14. The method of claim 13, further comprising determining a failure profile to indicate a degree of functionality of the application architecture based on the actual application impact and the factor in the likelihood of failure.

15. The method of claim 14, further comprising determining that changes need to be made to the application architecture based on the failure profile.

16. A computer program product for analyzing an application architecture, the computer program product comprising a computer readable hardware storage device having program code stored on the computer readable hardware storage device, the program code readable/executable by a computing device to:

compute an actual application impact based on an “

N”

number of failing IT infrastructure components within an application architecture;

determine a factor in likelihood of failure of the “

N”

number of IT infrastructure components failing; and

simulate additional features based on other IT infrastructure components within the application architecture,

wherein the computing of the actual application impact includes analyzing a vertical application layer and calculating throughput volume in the vertical application layer,

the calculating the throughput volume in the vertical application layer comprises subtracting a product of a previous layer throughput volume and a current level fail percentage from the previous layer throughput volume,

the vertical application layer comprises a proxy server layer and a database server layer, and

the other IT infrastructure components comprises a logical partition (LPAR) which is a virtual partition that partitions information into different locations within a device.

17. The computer program product of claim 16, further comprising determining a protection profile, wherein the protection profile includes determining a minimum level of protection and a maximum level of protection.

18. The computer program product of claim 17, further comprising determining a cost profile, wherein the cost profile includes determining a cost ratio and a cost influence.

19. The computer program product of claim 18, further comprising determining a failure profile based on the actual application impact and the factor in likelihood of failure,

wherein:

the failure profile includes computing an average of product failure values for the “

N”

number of IT infrastructure components,

each product failure value is determined by taking the product of the computed actual application impact and a chance of failure value for one or more of the “

N”

number of IT infrastructure components, and

the cost profile, the protection profile, and the failure profile are used to identify how failing the “

N”

number of IT infrastructure components affect the application architecture.

20. The computer program product of claim 19, wherein the determining the failure profile includes using a combination of analyzing vertical application layers and analyzing application components, and the “

N”

number of failing IT infrastructure components include application components that include feed servers, web load balanced servers, and application servers.